PROJECT REPORT1 (1) new

Adaptive Moving Target Indicator
Dept of TCE, BNMIT Page 1
CHAPTER 1
INTRODUCTION
Since conception of radar just prior to Second World War many different types of radar have
been developed. Although the original radars were developed to extract information about the
position of aircraft, the realization soon came that by transmitting different waveform and
applying different forms of processing to the received waveform, much more detailed
information could be obtained. It was realized that not only could more information about targets
be obtained, but that information could also be gained about operating environment of radar.
Example is weather radar, and sideways looking airborne radars.
Radar (radio detection and ranging) is designed to detect and locate targets within the specified
range. Targets are in general defied as planes and other aircraft. The radar emits an
electromagnetic pulse, and each object that becomes illuminated by the pulse reflects a small
replica of the pulse back to the radar. From this echo signal the radar then attempts to detect and
locate the reflecting object. The radar pulse is reflected by targets, but may also be reflected by
other objects, for instance the ground, high mountains and rain. Such reflections are called
clutter, and they are regarded as noise since they reduce the radars ability to detect and locate
targets. Targets are concentrated spatially, whereas clutter tends to occupy a larger area. The
random clutter process is therefore characterized by its spatial and temporal statistic.
Moving target indication (MTI) is a mode of operation of radar to discriminate a target against
clutter. The most common approach takes advantage of the Doppler Effect. For a given sequence
of radar pulses, the moving target will change its distance from the radar system. Therefore the
phase of the radar reflection that returns from the target will be different for successive pulses.
This differs from a stationary target (or clutter) which will cause the reflected pulses to arrive at
the same phase shift.
Clutter variation requires the use of adaptive cancellers that sense the clutter characteristics and
adjust their weights accordingly. Weights are the complex, than real value and there by allow the
nulls to be steered in Doppler frequency to cancel clutter as appropriate. Adaptive filters have
been proposed as an answer to the problem of clutter suppression in spatially and time varying

clutter environments. The performance of general adaptive filters is reduced when the statistical
properties of the clutter process vary with radial distance, as the filter weights are estimated from
neighboring range bins. An alternative strategy is to make the MTI filter adaptive.
Radar can be broken down in 2 main categories those which can be transmit and receive
continuously called CW (continuous wave radar) and those that transmit for a short period of
time, and then received whilst the transmitter is turned off, called pulse radar. CW radar is quite
severely restricted in amount of information that they can provide hence much more attention is
given to pulse radar. The difficulty of isolating a power full transmitter from a sensitive receiver
tends to limit amount power transmittable and hence limits useful range of radar.
Whatever the type of radar that is employed certain principle always applies. One of these
overriding principles is that in absence of interference the delectability of targets increases as the
energy transmitted increases. This implies that the only limitation on the detection process is the
presence of thermal noise in front end of receiver. Thus from a delectability point of view the
optimum situation is to transmit as much power as possible for as long as possible.
Unfortunately, this conflict with requirement of resolution: there would be little use in defining
the presence of target, if its whereabouts couldn’t be accurately established. Its well known in
Fourier transform theory that in order to resolve something accurately in time, the bandwidth of
transmitted signal has to be large, that to resolve something accurately in frequency, the time
duration of signal has to be large. This is simply a consequence of time frequency duality.
It might be thought that at first that the approximate time-frequency relationship given for simple
pulses would prevent the accurate simultaneous resolution of both time and Doppler frequency (
i.e. velocity) of radar target , but this need not to be so. It is possible to transmit pulses of a fairly
complicated structure that can resolve well in both time and frequency. This technique is
commonly referred to as pulse compression technique. the basic idea of any pulse radar is to
repetitively transmit a pulse, which need not necessarily be the same pulse each time, the time
interval between pulses needed not be same either. Usually it’s convenient to transmit same
pulse each time, and it is also convenient to make the inter pulse period either same each time or
to make periods related to each other by some simple ratio. A little thoughts shows that
transmitting a pulse train, instead of longer more complicated pulse, as a signal duration
increases, the bandwidth doesn’t decrease hence pulse repetition can be viewed as convenient

way of increasing signal duration, without a proportionate decrease in bandwidth. This enables
pulse train to have good resolution properties in time in frequency. Using pulse trains, it’s
possible to design signals of extreme complexity and yet still use simple equipment. In particular
if the transmitted pulse stream is coherent that is burst of carrier maintains the correct phase
relationship with the last burst, then it is possible to get even higher resolution. This means that
coherent pulse radar can give a good performance even in dense target environment. This way
Problems of ambiguity are encounter both in frequency and time processing.
1.1 General Block diagram
Input from the receiver is series of pulses which is nothing but the reflected echoes from
different objects which is in the range of the radar. These echoes consists various information
about the object like distance, density, temperature. Each radar is designed to perform specific
function that it could detect either weather conditions or distance etc and any other information
obtained is considered as clutter to that radar.
Pulse compression is a signal processing technique mainly used in radar, sonar and echography
to increase the range resolution as well as the signal to noise ratio. This is achieved by
modulating the transmitted pulse and then correlating the received signal with the transmitted
pulse. The simplest signal pulse radar can transmit is a sinusoidal pulse of amplitude, A and
carrier frequency, f0, this pulse is transmitted periodically.
An adaptive system performs the processing by using an architecture having time-varying
parameters on the received signals which accompanies with clutters. An adaptive moving target
INPUT FROM
RECEIVER
DISPLAY DETECTION
PULSE
COMPRESSION
ADAPTIVEMTI CFAR

detector has been designed to meet the challenges of target detection amidst various levels of
clutter environments. The approach has been used that is able to overcome the inherent
limitations of conventional systems (e.g. Moving Target Indicator, Fast Fourier Transform etc.).
Constant false alarm rate (CFAR) detection refers to a common form of adaptive algorithm used
in radar systems to detect target returns against a background of noise, clutter and interference.
In the radar receiver the returning echoes are typically received by the antenna, amplified, down-
converted and then passed through detector circuitry that extracts the envelope of the signal. ,
unwanted clutter and interference sources mean that the noise level changes both spatially and
temporally. In this case, a changing threshold can be used, where the threshold level is raised and
lowered to maintain a constant probability of false alarm. This is known as constant false alarm
rate (CFAR) detection.
The combination of this group of trolleys and big swells present an excellent opportunity to
practice with our radar. In the image above, sea and rain clutter are turned off. Gain is turned up
to 50 - where we leave it offshore (this is too high for inshore work where there are close targets
surrounding us). Notice the almost solid return from the sea clutter within quarter of a mile of the
boat. ARPA vectors are set to true here, to show us the actual courses of these targets.
Fig 1.1: real time display of radar

1.2 Objective and Scope of the Project
The main object of our project is to design an Adaptive mti filter with improved performance in
non stationary clutter environment Non stationary clutter processes are characterized by a
space/time dependent statistics. An estimator for pulse to pulse amplitude variations of the clutter
process is therefore introduced. This estimator is then implemented in adaptive mti filter, for the
purpose of adapting filter to the non stationary behavior of clutter process. Results of applying
this adaptive mti filter on real radar data demonstrates that the filter gives an extensive reduction
of clutter detections in radar image. Therefore this design can be used in defence, by aircrafts,
security surveillance etc

CHAPTER 2
LITERATURE SURVEY
Radar is an object-detection system that uses radio waves to determine the range, altitude,
direction, or speed of objects. It can be used to detect aircraft, ships, spacecraft, guided missiles,
motor vehicles, weather formations, and terrain. The radar dish (or antenna) transmits pulses of
radio waves or microwaves that bounce off any object in their path. The object returns a tiny part
of the wave's energy to a dish or antenna that is usually located at the same site as the
transmitter. Radar was secretly developed by several nations before and during World War II.
The term RADAR was coined in 1940 by the United States Navy as an acronym for Radio
Detection And Ranging. The term radar has since entered English and other languages as a
common noun, losing all capitalization. The modern uses of radar are highly diverse, including
air and terrestrial traffic control, radar astronomy, air-defense systems, antimissile systems;
marine radars to locate landmarks and other ships; aircraft anti-collision systems; ocean
surveillance systems, outer space surveillance and rendezvous systems; meteorological
precipitation monitoring; altimetry and flight control systems; guided missile target locating
systems; and ground-penetrating radar for geological observations. High tech radar systems are
associated with digital signal processing and are capable of extracting useful information from
very high noise level.
Fig 2.1: radar

The performances of the studied designs are investigated by comparing the frequency response
characteristics and the average signal-to-clutter suppression capabilities of the filters with respect
to a number of defined performance measures. Two further approaches are considered to
increase the signal-to-clutter suppression performance. First approach is based on a modified
min-max filter design whereas the second one focuses on the multiple filter implementations.
There has been IEEE paper published on adaptive filter in past year.
The algorithm developed to meet our objective on adaptive mti is different from those mentioned
in IEEE paper for adaptive mti. As adaptive mti will involve non-stationary clutters with change
in radar’s velocity which will make performance of adaptive complex.

CHAPTER 3
OVERVIEW OF THE SYSYTEM
3.1 Moving targetindicator
Moving target indication (MTI) is a mode of operation of radar to discriminate a target against clutter. In
contrast to another mode, stationary target indication, it takes advantage of the fact that the target moves
with respect to stationary clutter. The most common approach takes advantage of the Doppler Effect. For
a given sequence of radar pulses, the moving target will change its distance from the radar system.
Therefore the phase of the radar reflection that returns from the target will be different for successive
pulses. This differs from a stationary target (or clutter) which will cause the reflected pulses to arrive at
the same phase shift. Radar MTI may be specialized in terms of the type of clutter and environment:
airborne MTI (AMTI), ground MTI (GMTI), etc., or may be combined mode: stationary and moving
target indication (SMTI).In radar (radio detection and ranging) oriented system which is used for varies
application such as In our project we are trying to locate moving target using mti in this process clutter is
eliminated received at consecutive prf(pulse repetitive frequency).When the wavelength is large
compared to the objects dimension, scattering follows a Rayleigh distribution. When the wavelength is
small compared to the objects dimension, it is in the optical region, where the target can be detected. The
received signal for radar system with an input noise that is Gaussian can be modeled as a Rayleigh
distribution. Moving target indicator (MTI) begins with sampling two successive pulses. Sampling begins
immediately after the radar transmits pulse ends. The sampling continues until the next transmit pulse
begins. Sampling is repeated in the same location for the next transmit pulse, and the sample taken (at the
same distance) with the first pulse is rotated 180 degrees and added to the second sample. This is called
destructive interference. If an object is moving in the location corresponding to both samples, then the
signal reflected from the object will survive this process because of constructive interference. If all
objects are stationary, the two samples will cancel out and very little signal will remain. High power
microwave devices, like crossed-field amplifier, are not phase-stable. The phase of each transmit pulse is
different from the previous and future transmit pulses. This phenomenon is called phase jitter.
3.2 Clutters
Radar clutter is unwanted echoes from the natural environment, which “clutter” the radar and
challenge target detection. Clutter includes echoes from land, sea, weather, and animals. Clutter
is spatially distributed and larger in physical size than the radar resolution cell. Manmade objects

(i.e. buildings), are “point,” or discrete, clutter echoes, that produce large backscatter. Large
clutter echoes can mask echoes from desired targets. When clutter is greater than receiver noise,
clutter signal processing dominates. Echoes from land or sea are examples of surface clutter.
Echoes from rain and chaff are examples of volume clutter. The echo magnitude from distributed
surface clutter is proportional to the area illuminated. To independently model the clutter echo of
the illuminated area, the clutter cross section per unit area, σ0 , is utilized: σ0 = σc Ac (9) where
σc is the radar cross section of the clutter occupying an area Ac. σ0 is a dimensionless quantity,
expressed in decibels with a reference value of one, and is also known as the scattering
coefficient, differential scattering cross section, normalized radar reflectivity, backscattering
coefficient, and normalized radar cross section (NRCS).The spectrum of the ground clutter is
assumed to have narrow width and is centered on zero Doppler velocity
3.2.1 Characteristics of clutter
There are 2 types of clutter. Surface and volume clutter. In our project it is important to know
different characteristics of clutter in order to design our notch filter according to it and to
eliminate clutter at that particular region.
a) Surface clutter
In designing surface clutter models, we develop different models based on the grazing angle
which can be effective to model land and sea clutter. Figure Elevation geometry shows the
extent of the surface illuminated by the radar pulse, (top) plan view showing the illuminated
clutter resolution cell consisting of individual, independent scatters Low Grazing Angle. Figure
depicts radar illuminating the surface at a small grazing angle, ψ. For low grazing angles the
range is determined by the radar pulse width τ. The cell width in the cross-range dimension is
determined by the azimuth beam widthθB and the range R. From the simple radar equation, the
received echo power Pr is Pr = Pt G Ae σ (4π) 2 R4
where Pt = transmitter power,
W G = antenna gain
Ae = antenna effective aperture

m2 R = range m
σ = radar cross section of the scatter
we let Pr = S (received target signal power)
σ = σ t, (target cross section).
The signal power returned from a target is then
S = Pt G Aeσt (4π) 2 R4
When the echo is from clutter, the cross section σ becomes σc = σ0 Ac, where the area Ac of the
radar resolution cell is Ac = R θB (c τ / 2) sec ψ (12) with
θB = two-way azimuth beamwidth,
c = velocity of propagation,
τ = pulsewidth,
ψ = grazing angle (defined with respect to the surface tangent).
The area Ac in range resolution, is (c τ / 2), where the factor of 2 in the denominator accounts
for the two-way propagation of radar. With these definitions, the radar equation for the surface-
clutter echo-signal power C is C = Pt G Ae [θB(c τ / 2) sec ψ] (4π) 2 R3.When the echo from
surface clutter is large compared to receiver noise, the signal-to-clutter ratio is S C =σt σ0 R θB
(c τ / 2) sec ψ. If the maximum range, Rmax, corresponds to the minimum discernible signal-to-
clutter ratio (S/C)min, then the radar equation for the detection of a target in surface clutter at
low grazing angle is Rmax = σt (S/C)min σ0 θB (c τ / 2) sec ψ (15)
Variation of Surface Clutter with Grazing Angle. Figure shows the general form of surface
clutter as a function of grazing angle. There are three different scattering regions. At high
grazing angles, the radar echo is due mainly to reflections from clutter that can be represented as
individual planar facets oriented so that the incident energy is directed back to the radar. The
backscatter can be quite large at high grazing angles. At the intermediate grazing angles,
scattering is somewhat similar to that from a rough surface. At low grazing angles, back

scattering is influenced by shadowing (masking) and by multipath propagation. Shadowing of
the trough regions by the crests of waves prevents low-lying scatters from being illuminated.
Figure General nature of the variation of surface clutter as a function of grazing angle, showing
the three major scattering regions. High Grazing Angle The surface clutter area viewed by the
radar is determined by the antenna beam widthsθB and φB, in the two principal planes. The
clutter illuminated area Ac in is (π/4) R θB R φB / 2 sin ψ, where ψ = grazing angle and R =
range. The factor π / 4 accounts for the elliptical shape of the illuminated area, and the factor of 2
in the denominator is necessary since in this case θB and φB are the one-way beam widths.
Substituting σ = σ0 Ac, letting Pr = C (the clutter echo power), and taking G = π2 θBφB, the
clutter radar equation in this case is C = π PtAe σ0 128 R2 sin ψ.The clutter power is seen to
vary inversely as the square of the range. This equation applies to the echo power received from
the ground by a radar altimeter or the remote sensing radar known as a scatter meter One
complicating factor in the study of clutter is that it means different things in different situations.
For example, to an engineer developing a missile to detect and track a tank, the return from
vegetation and other natural objects would be considered to be “clutter”. However, a remote
sensing scientist would consider the return from natural vegetation as the primary target. Clutter
is thus defined as the return from a physical object or a group of objects that is undesired for a
specific application. Clutter may be divided into sources distributed over a surface (land or sea),
within a volume (weather or chaff) or concentrated at discrete points (structures, birds or
vehicles). The magnitude of the signal reflected from the surface back to the receiver is a
function of the material, roughness and angle. There are three primary scattering types into
which Clutter is generally classified. These are secular, retro and diffuse as shown in the figures
Below.

Fig 3.1: The reflections from the aircraft totally reflected and partially reflected

Fig 3.2 clutter signals being diffracted in all directions
b) Ground Clutter
Because of the statistical nature of clutter, the mean reflectivity is most often quoted. A
Convenient mathematical way to describe this mean value for surface clutter is the
Constant γ model in which the surface reflectivity is modeled as fluctuating proportion is
reflected back to the radar
σ = γ sinψo , (9.18)
Where σo – Reflectivity (cross section per unit area m2
ψ - Grazing angle at the surface (rad),
γ - Parameter describing the scattering effectiveness.

Figure 3.3: Effect of grazing angle on clutter reflectivity for different clutter types
It can be seen from the figure that, at low grazing angles, the measurements fall below the Model
because of propagation-factor effects. At high grazing angles the measured Reflectivity rises
above the value predicted by the model because of quasi specular
Reflections from surface facets.
For different surface types, the following are typical:
• Values for γ between –10 and –15dB are widely applicable to land covered by crops, bushes
and trees.
• Desert, grassland and marsh are more likely to have γ near –20dB
• Urban or mountainous regions will have γ near –5dB
These values are almost independent of wavelength and polarization, but they only apply to
modeling of mean clutter reflectivity.

Thus we could conclude that clutter amplitude is less for sea clutter compared to surface clutters
due to the facts that surface would have many sharp edges like in buildings and vehicles. These
sharp edges will have major scattering whereas the sea clutter would not have sharp edges as
compared to the surface. In surface clutter homogenous clutter occurs in the forest region this is
due to the reflections from the tress in the forest which are evenly spread, thus getting same kind
of echoes. Whereas the echoes from the cities consisting of different kind of vehicles and
buildings would have non homogenous type of scattering.Some of the examples for clutter are
rain and dust.
a) Rain
The graphs below show the theoretical values for the reflectivity as a function of rainfall rate at
different frequencies.
Figure 3.4: Theoretical raindrop reflectivity vs rainfall rate using Marshall Palmer drop size.
This data is determined using the relationship between the reflected and incident power on small
spherical targets as discussed earlier in the section on the RCS of a sphere. Though a given
rainfall rate does not imply a specific drop-size distribution, the trend that the drops get bigger as
the rainfall rate increases, generally holds true. In the Rayleigh region (πD/λ < 1), the RCS is
given by the following formula

Distributions refl = = , (9.28) 2 2 5 4Rσ π πε K , (9.29) D KSinc1− = ε2+64λ
Where ε - Relative dielectric constant of the material,
D – Diameter of the scattering object,
λ - Wavelength 279
When πD/λ > 10 the equation for RCS reduces to the geometric optics form 2 πσ = . (9.30) 4
These equations can be combined with the density of particles in the medium to determine the
total reflectivity, η. Nησ . (9.31) ∑==ii1
b) Dust
The volume of dust that can be supported in the atmosphere is extremely small, and so the
reflectivity can often be neglected for EM radiation with wavelengths of 3mm or more.
However, under certain circumstances, if the dust density is very high (such as in rock crushers)
or if the propagation path through dust is very long (in dust storms), then it can be useful to
determine the reflectivity and the total attenuation.

Figure 3.5: Backscatter from dust after explosion (a) at 10GHz and (b) 35GHz
3.3 Doppler Effect
The Doppler Effect was first recognized by Christian Johann Doppler, who observed that the
color of a luminous body and the pitch of a sounding body are changed by the relative motions of
the body and observer. The Doppler Effect (or Doppler shift) is the change in frequency of a
wave (or other periodic event) for an observer moving relative to its source. It is named after the
Austrian physicist Christian Doppler, who proposed it in 1842 in Prague. It is commonly heard
when a vehicle sounding a siren or horn approaches, passes, and recedes from an observer.
Compared to the emitted frequency, the received frequency is higher during the approach,
identical at the instant of passing by, and lower during the recession.

When the source of the waves is moving toward the observer, each successive wave crest is
emitted from a position closer to the observer than the previous wave. Therefore, each wave
takes slightly less time to reach the observer than the previous wave. Hence, the time between
the arrival of successive wave crests at the observer is reduced, causing an increase in the
frequency. While they are travelling, the distance between successive wave fronts is reduced, so
the waves "bunch together". Conversely, if the source of waves is moving away from the
observer, each wave is emitted from a position farther from the observer than the previous wave,
so the arrival time between successive waves is increased, reducing the frequency. The distance
between successive wave fronts is then increased, so the waves "spread out". For waves that
propagate in a medium, such as sound waves, the velocity of the observer and of the source are
relative to the medium in which the waves are transmitted. The total Doppler effect may
therefore result from motion of the source, motion of the observer, or motion of the medium.
Each of these effects is analyzed separately. For waves which do not require a medium, such as
light or gravity in general relativity, only the relative difference in velocity between the observer
and the source needs to be considered. A very common example is the change in pitch (not the
frequency) of an approaching or receding vehicle with respect to you. The pitch rises as the
oncoming vehicle gets nearer, goes to zero as the vehicle passes (i.e. there is a zero relative
velocity) and then starts to fall as it recedes. Light from moving objects will appear to have
different wavelengths depending on the relative motion of the source and the observer. Observers
looking at an object that is moving away from them see light that has a longer wavelength than it
had when it was emitted (a red shift), while observers looking at an approaching source see light
that is shifted to shorter wavelength (a blue shift). In many radar applications there is a relative
movement between the radar and the target to be detected. Examples include, Air Traffic
Control, Battlefield Surveillance, and Weapon Locating, all Airborne Radars, SAR and ISAR as
well as many others. Consider the example of Air Traffic Control radar. This has to detect
incoming and outgoing aircraft in the presence of a clutter background. We have already seen
that clutter can be distinguished from receiver noise by virtue of its narrower, low frequency
spectrum.

Fig 3.6: Doppler variation
Targets can be distinguished from background clutter by virtue of their motion. This enables a
track history to be built up. More useful, however, is exploitation of the Doppler effect which
enables moving targets to be filtered such that clutter is rejected based upon the differing
velocities of the two received signal components. A processor that distinguishes moving targets
from clutter by virtue of differences in their spectra is called a Moving Target Indicator or MTI.
MTI processors can take a number of forms.
Consider stationary ground based radar observing an approaching aircraft. As the aircraft
approaches each radar pulse travels a shorter and shorter distance, consequently the phase of the
signal is constantly changing with each pulse or target position. The faster the aircraft
approaches the radar the faster the rate of change of the phase of the reflected signal. Thus the
rate of change of the measured phase to the approaching aircraft is relative to the velocity of the
aircraft.

Fig 3.7: The phase represented by the two-way path from radar to target is Φ=2π (2r/λ)
The Doppler shift is -2vfo/c (- sign because an increase in path length represents a phase lag)
Fig 3.8: doppler w.r.t aircraft

3.3.1 Derivation of Doppler frequency formula
The phase shifting φ of an electromagnetic wave from the radar antenna to the aim and back
results from the ratio of the covered distance and the wavelength of the transmitted energy
multiplied with the scale of the full circle (2·π):
This means: In practice the Doppler- frequency occurs twice at radar. Once on the way from the
radar to the aim, and then for the reflected (and already afflicted by a Doppler-shift) energy on
the way back. In the radar signal processing the Doppler frequency will be divided by the actual
transmitted frequency to eliminate the influence of different transmitter’s frequencies. Now the
Doppler frequency is a measure of the radial speed only and is called “normalized”.

Fig 3.9: doppler spread delay
Doppler spread in the spectrum is the delay occurred of the signal echoes returned, which should
have been returned at the actual time. Doppler spread is mainly due to 2 methods
1) Temporal variation: The time taken by the first echo is different from the time taken by the
other echoes. This is due to the wind, dust, moisture between the target and the radar.
2) Spatial variation: This variation occurs due to the change in the position of object with respect
to radars. It may be because the target is moving or may be because the radar platform moving or
may be the relative motion between the radar and the target.
3.4 Blind speeds
It can be seen that the frequency response has zeroes at Doppler shifts corresponding to

These can also be thought of in terms of target movement such that the pulse-to-pulse phase
changes by 2pi, in other words that the target range changes by l/2 in one PRI.
This also says that to keep the first blind speed above the highest likely target velocity, it is
necessary to use a high PRF. This conflicts with the requirement to use a low PRF in order to
avoid range ambiguities. This is an example of the tradeoffs involved in the choice of PRF in the
design of the radar modulation scheme, and is covered in more detail when considering the
ambiguity function.
3.5 Adaptive moving target indicator
The Adaptive Moving Target Indicator (AMTI) is an adaptive filter automatically suppressing
the unwanted reflection echo (clutter) from the ocean surface, clouds, and rain contained in the
radar receive signal. In this paper, with a view to assuring the suppression of the clutter in the
case where the AMTI is applied to search radar that can acquire only several samples of received
signals with assuring coherence, a burst averaging type AMTI is proposed. In this AMTI, the
received signals without coherence from the same direction intermittently obtained by beam
scanning are used for weighting control. From the performance evaluation by computer
simulation, it is proven that the clutter suppression capability can be improved by about 3 dB in
comparison with the case without burst averaging for a steady single peak clutter. It is also
clarified that the clutter suppression capability can be improved by 0.3 to 1.8 dB by burst
averaging with two to five scans even in the case where the center frequency of the clutter
changes for each beam scan as long as the frequency variation width is less than 12% with

respect to the pulse repetition frequency. In many radar applications there is a relative movement
between the radar and the target to be detected. Examples include, Air Traffic Control,
Battlefield Surveillance, and Weapon Locating, all Airborne Radars, SAR and ISAR as well as
many others. Consider the example of Air Traffic Control radar. This has to detect incoming and
outgoing aircraft in the presence of a clutter background. We have already seen that clutter can
be distinguished from receiver noise by virtue of its narrower, low frequency spectrum.

CHAPTER 4
FILTERS
Filters are a basic component of all signal processing and telecommunication systems. The
primary functions of a filter are one or more of the followings: (a) to confine a signal into a
prescribed frequency band or channel for example as in anti-aliasing filter or a radio/TV channel
selector, (b) to decompose a signal into two or more sub-band signals for sub band signal
processing, for example in music coding, (c) to modify the frequency spectrum of a signal, for
example in audio graphic equalizers, and (d) to model the input-output relation of a system such
as a mobile communication channel, voice production, musical instruments, telephone line echo,
and room acoustics.
Filters are widely employed in signal processing and communication systems in applications
such as channel equalization, noise reduction, radar, audio processing, video processing,
biomedical signal processing, and analysis of economic and financial data. For example in a
radio receiver band-pass filters, or tuners, are used to extract the signals from a radio channel. In
an audio graphic equalizer the input signal is filtered into a number of sub-band signals and the
gain for each sub-band can be varied manually with a set of controls to change the perceived
audio sensation. In a Dolby system pre-filtering and post filtering are used to minimize the effect
of noise. In hi-fi audio a compensating filter may be included in the preamplifier to compensate
for the non-ideal frequency-response characteristics of the speakers. Filters are also used to
create perceptual audio-visual effects for music, films and in broadcast studios.
The primary functions of filters are one of the followings:
(a) To confine a signal into a prescribed frequency band as in low-pass, high-pass, and band-pass
filters.
(b) To decompose a signal into two or more sub-bands as in filter-banks, graphic equalizers, sub-
band coders, frequency multiplexers.
(c) To modify the frequency spectrum of a signal as in telephone channel equalization and audio
graphic equalizers.
(d)To model the input-output relationship of a system such as telecommunication channels,
human vocal tract, and music synthesizers.

4.1 Non-Recursive or Finite Impulse Response (FIR) Filters
A non-recursive filter has no feedback and its input-output relation is given by
y(m)=∑ 𝑏 𝑘 𝑥(𝑚 − 𝑘)𝑀
𝑘=0
Fig 4.1: direct-form finite impulse response (FIR)
The output y (m) of a non-recursive filter is a function only of the input signal x(m). The
response of such a filter to an impulse consists of a finite sequence of M+ 1 sample, where M is
the filter order. Hence, the filter is known as a Finite-Duration Impulse Response (FIR) filters.
Other names for a non-recursive filter include all-zero filter, feed-forward filter or moving
average (MA) filter a term usually used in statistical signal processing literature.
4.1.1 FIR filter types which can be used to remove clutters are:
a) Low pass filter
Low pass filters can be used for many applications. One area in which these filters can be
used is on the output of digital to analogue converters where they are able to remove the
high frequency alias components. However they can be used in many other areas where it
is necessary to pass the low frequency components of the signal, but remove the
unwanted high frequency elements. Active low pass filters are capable of providing a
relatively high level of performance for a small number of components.

Fig 4.2: Low pass filter basic response curve
The shape of the curve is of importance with features like the cut-off frequency and roll off being
key to the operation. The cut-off frequency is normally taken as the point where the response has
fallen by 3dB as shown. Another important feature is the final slope of the roll off. This is
generally governed by the number of 'poles' in the filter. Normally there is one pole for each
capacitor inductor in a filter. When plotted on a logarithmic scale the ultimate roll-off becomes a
straight line, with the response falling at the ultimate roll off rate. This is 6dB per pole within the
filter.
Consider the design of a low-pass linear-phase digital FIR filter operating at a sampling rate of
Fs Hz and with a cutoff frequency of Fc Hz. The frequency response of the filter is given by
The impulse response of this filter is obtained via the inverse Fourier integral as

b) High pass filter
As the name implies, a high pass filter is a filter that passes the higher frequencies and rejects
those at lower frequencies. In other words, high-frequency signals go through much easier
and low-frequency signals have a much harder getting through, which is why it's a high pass
filter. This can be used in many instances, for example when needing to reject low frequency
noise, hum, etc. from signals. This may be useful in some audio applications to remove low
frequency hum, or within RF to remove low frequency signals that are not required.
Fig 4.3: High pass filter basic response curve
The shape of the curve is of importance. One of the most important features is the cut-off
frequency. This is normally taken as the point where the response has fallen by 3dB.
Consider the design of a high-pass linear-phase digital FIR filter operating at a sampling rate of
Fs Hz and with a cutoff frequency of Fc Hz. The frequency response of the filter is given by

c) Band pass filter
As the name implies a band pass filter is one where only a given band of frequencies is allowed
through. All frequencies outside the required band are attenuated. There are two main areas of
interest in the response of the filter. These are the pass-band where filter passes signals and the
stop-band where signals are attenuated. As it is not possible to have an infinitely steep roll off,
there is an area of transition outside the pass-band where the response is falling but has not
reached the required out of band attenuation. Band pass filters are used in many areas of
electronics. They are particularly widely used for RF applications where tuned circuits are used.
However for lower frequencies, active band pass filters provide an effective means of making a
filter to pass only a given band if frequencies. For these filters the most widely used active
element is an operational amplifier, or op amp. These op amp band pass filters are easy to design
and construct, requiring only a minimum of components. In addition to this, these active band
pass filters provide a very effective level of performance.
Fig 4.4: frequency response of band pass filter

Consider the design of a band-pass linear-phase digital FIR filter operating at a sampling rate of
Fs Hz and with a lower and higher cutoff frequencies of FL and FH Hz. The frequency response
of the filter is given by
d) Band stop filter
A band stop filter is a circuit that ideally filters out signals with frequencies in a certain range.
This range can be quite large, depending on inherent characteristics of the circuit. The smaller
the range of frequencies the circuit filters, the higher the Q factor it is said to have. Band stop
filters with high Q Factors are also called notch filters. The band pass filter passes one set of
frequencies while rejecting all others. The band-stop filter does just the opposite. It rejects a band
of frequencies, while passing all others. This is also called a band-reject or band-elimination
filter. Like band pass filters, band-stop filters may also be classified as (i) wide-band and (ii)
narrow band reject filters. The narrow band reject filter is also called a notch filter. Because of its
higher Q, which exceeds 10, the bandwidth of the narrow band reject filter is much smaller than
that of a wide band reject filter.

Fig 4.5: frequency response of band stop filter
Consider the design of a band-stop linear-phase digital FIR filter operating at a sampling rate of
Fs Hz and with a lower and higher cutoff frequencies of FL and FH Hz. The frequency response
of the filter is given by

4.1.2 Filter using window techniques
The window method is most commonly used method for designing FIR filters. The simplicity of
design process makes this method very popular. A window is a finite array consisting of
coefficients selected to satisfy the desirable requirements. This provides a few methods for
estimating coefficients and basic characteristics of the window itself as well as the result filters
designed using these coefficients. The point is to find these coefficients denoted by w[n].
When designing digital FIR filters using window functions it is necessary to specify:
 A window function to be used; and
 The filter order according to the required specifications (selectivity and stop band
attenuation).
These two requirements are interrelated. Each function is a kind of compromise between the two
following requirements:
 The higher the selectivity, i.e. the narrower the transition region; and
 The higher suppression of undesirable spectrum, i.e. the higher the stop band attenuation.
The table 4.1 below gives the equations for different window types.
Window Type Weight Equation
Rectangular
Bartlett
Hanning

Hamming
Blackman
Frequency response and weight values of different window types
Fig 4.6 frequency response and weight values

4.2 Recursive or Infinite Impulse Response (IIR) Filters
A recursive filter has feedback from output to input, and in general its output is a function of the
previous output samples and the present and past input samples as described by the following
equation
y(m)=∑ 𝑎 𝑘 𝑦( 𝑚 − 𝑘) + ∑ 𝑏 𝑘 𝑥(𝑚 − 𝑘)𝑀
𝑘=0
𝑁
𝑘=1
Fig 4.7: direct-form pole-zero IIR filter
Fig shows a direct form implementation of IIR. In theory, when a recursive filter is excited by an
impulse, the output persists forever. Thus a recursive filter is also known as an Infinite Duration
Impulse Response (IIR) filters. Other names for an IIR filter include feedback filters, pole-zero
filters and auto-regressive-moving-average (ARMA) filter a term usually used in statistical
signal processing literature.

4.3 Comparison of FIR and IIR filters
FIR filter uses only current and past input digital samples to obtain a current output sample
value. It does not utilize past output samples. Simple FIR equation is mention below.
y(n)= h(0)x(n) + h(1)x(n-1) + h(2)x(n-2) + h(3)x(n-3) + h(4)x(n-4)
IIR filter uses current input sample value, past input and output samples to obtain current output
sample value. Simple IIR equation is mention below.
y(n)= b(0)x(n) + b(1)x(n-1) + b(2)x(n-2) + b(3)x(n-3) + a(1)y(n-1) + a(2)y(n-2) +a(3)y(n-3)
 Transfer function of FIR filter will have only zeros, need more memory, while transfer
function of IIR filter will have both zeros and poles and will require less memory than
FIR counterpart.
 FIR filters are preferred due to its linear phase response and also they are non-recursive.
Feedback is not involved in FIR, hence they are stable. IIR filters are not stable as they
are recursive in nature and feedback is also involved in the process of calculating output
sample values.
 FIR filter consume low power and IIR filter need more power due to more coefficients in
the design.
 IIR filters have analog equivalent and FIR have no analog equivalent.
 FIR filters are less efficient while IIR filters are more efficient.
 FIR filters are used as anti-aliasing, low pass and baseband filters. IIR filters are used as
notch (band stop), band pass functions.

 FIR filter need higher order than IIR filter to achieve same performance. Delay is more
than IIR filter. It has lower sensitivity than IIR filter. These are disadvantages of FIR
filters.
4.4 FIR Notch Filter
Digital signal processing (DSP) techniques have rapidly developed in the recent years due to
advances in digital computer technology and integrated circuit fabrication [3], [26], [27]. The use
of digital circuits yields high speed as well as high reliability, and also permits us to have
programmable operations. DSP techniques and applications in a variety of areas such as speech
processing, data transmission on telephone channels, image processing, instrumentation, bio-
medical engineering, seismology, oil exploration, detection of nuclear explosion, and in the
processing of signals received from the outer space, besides others. Various types of digital
filters, such as Low pass (LP), High-pass (HP), Band-pass (BP), Band-stop (BS), and Notch
filters (NF), and various types of digital operations such as Differentiation, Integration and
Hilbert transformation, to mention a few, are invariably used in many of the applications just
mentioned. In this review paper, we focus our attention on the design and performance analysis
of notch filters.
The FIR notch filter equation is given by which we are using in our code to eliminate the clutter
is
H (n) = 1- 2cos (w0)𝒆−𝒋𝒘
+ 𝒆−𝟐𝒋𝒘
4.4.1 Notchfilter characteristics
A notch filter highly attenuates/ eliminates a particular frequency component from the input
signal spectrum while leaving the amplitude of the other frequencies relatively unchanged. A
notch filter is, thus, essentially a band stop filter with a very narrow stop band and two pass
bands. The amplitude response, H1 (w), of a typical notch filter (designated as NF1) is shown in
Fig. 4.8 and is characterized by the notch frequency, wd (in radians) and 3-dB rejection
bandwidth, BW. For an ideal notch filter, BW should be zero, the pass band magnitude should be
unity (zero dB) and the attenuation at the notch frequency should be infinite.

Fig 4.8: The amplitude response H1 (w) of notch filter
4.5 Power PC
Power PC 8641 or 8641D is the hardware we have used in our project. It is a 32 bit RISC
processor and 128 bit active engine. The power PC consists of 12 8641D processors in number.
The radar data is too huge that must be processed, in order to process this huge data they are fed
to all these 12 processors. There are 2 types of feeding the huge data to the processors
1) Data slicing
Out of all the huge data received, a single output which is taken in the form single
matrix is sliced into different parts. Each of these parts of matrix is fed to all the
processors to perform the operations. After this is processed now the processor is
ready to take the next set of signal in the form of matrix.
2) Pipelining
Out of all huge data received each single output is fed to single processor rather
than slicing it. The received signal in the form of single matrix is given to one of
the processor. The next signal in the form of single matrix is given to the next
processor and so on. Thus each processor receives different set of data to be
processed. This method helps fast computation of data.

The MPC8641D uses two high-performance superscalar e600 cores running at up to 1.5 GHz.
This three-issue machine has a compact 7-stage pipeline which is particularly efficient with code
that branches unpredictably. It avoids the extensive delays associated with flushing a long
pipeline on mispredicted branches. Unpredictable branching is typical of code paths driven by
largely random arrival of different types of packets. These processors support up to 8 out-of-
order instructions on the system bus that allows for making forward progress even while waiting
for previous instructions to finish (ie, access to main memory required). The e600 has an on-
board 128-bit vector processor for efficient data movement (useful for copying TCP payloads
from kernel space to user space) and for math functions that rival a DSP. With a large backside
L2 cache for each core, the e600 benefits from high bandwidth and low latency between the
processor and the L2 cache. There are two flexible high-performance I/O ports. Dual 8-lane PCI
Express ports leverage PCI legacy with a high-performance serial point-to-point link that is
commonly used to connect to a variety of other on-board high-performance devices. There are
two flexible high-performance I/O ports. Dual 8-lane PCI Express ports leverage PCI legacy
with a high-performance serial point-to-point link that is commonly used to connect to a variety
of other on-board high-performance devices. There are four Ethernet controllers, supporting 10
Mbps, 100 Mbps, and 1000 Mbps. The Ethernet controllers have advanced capabilities for TCP
and UPD checksum acceleration, QoS support, and packet header manipulation. Each Ethernet
controller can be converted into a FIFO mode for high-efficiency ASIC connectivity. The
MPC8641D supports flexible software implementations: symmetric multiprocessing (SMP) and
Asymmetric multiprocessing. With SMP, one operating system runs on both cores, but from a
programming perspective, it appears that the developer is writing a program for a single core.
With Asymmetric multiprocessing, two instances of the same operating system or two entirely
separate operating systems can be run on the two cores, largely unaware of each other.
The following gives overview of the MPC8641 key feature set:
 High-performance, 32-bit superscalar microprocessor that implements the PowerPC ISA
 Eleven independent execution units and three register files
 Branch processing unit (BPU)
 Four integer units (IUs) that share 32 GPRs for integer operands
 64-bit floating-point unit (FPU)

 Four vector units and a 32-entry vector register file (VRs)
 Three-stage load/store unit (LSU)
Fig 4.9: PowerPC processing nodes
Fig 4.10: 8641 D

Chapter 5
IMPLENTATION
The beginning flow of the project was done in 4 main stages:
1. To generate a data signal matrix with complex values and noise in Matlab.
2. Adding the target and clutter to the same signal
3. Design a filter with suitable specifications to remove the clutters
4. Pass the signal though the filter.
 We have generated the data in Matlab through the noise matrix using the function
normrnd which created random noise with the specifications like mean, variance,order of
the matrix which had to be specified. Each column represents 1 sweep in the prf. Noise in
the matrix has no exact amplitude value, thus we use standard deviation which
determines the amplitude of noise. The noise amplitude is 6-8 times of standard
deviation. Mean defines the range of the random values that has to be generated for the
matrix. The order of the matrix was 128 X 16.
 For the matrix which consisted of random noise matrix has to be added with the target
and the clutter. The target could be desired object of interest. The target of sinusoidal
signal with different phase was added to all the 16 columns. The clutter of sinusoidal
signal with same phase was added to all the columns.
 The filter which had to design was basically meant to remove the stationary and non
stationary clutters associated. To remove clutters we choose windows, as the windows
could be designed for any order. In order to do this we first designed windows like
Kaiser, Blackman, hamming windows using Matlab to check if it could eliminate the
clutters. But these windows had a very low order and very less bandwidth. Due to the less
bandwidth it could eliminate very few clutters. Because of this reason we choose band
stop and band pass filters, and we could eliminate most of the clutters but in these filters
we needed two frequencies which couldn’t be related with the velocity of target and
clutter. We later came up with a new filter design which overcame all the problems like
the bandwidth, design the filter for any of the order also it could relate the relative
velocity of the clutter and the target. This filter is known as notch filter.

 This notch filter which we had designed gave very good results compared to all the other
filters. In this stage we had to pass the signal data generated to the designed filter and
remove the clutters. There are mainly 2 steps to do this.
a) To shift the filter to the position wherever the clutter is present.
b) To shift the data signal to wherever the filter is present
Fig 5.1: represents the 2 cases for removing the clutter.
In implementation of the project where we have to remove 3 clutters from the received data from
the radar. The position of the clutter is generated by hardware which makes use of parameters
like velocity of radar and PRF that is pulse repetitive frequency using these parameters Doppler
of clutter is calculated. As radar is moving stationary clutter in case of MTI behave as non-
stationary clutter shifted at certain Doppler ,Doppler of clutter is found using velocity and prf as
in case of MTI where stationary clutters used to lie near zero Doppler in this case due to adaptive
condition clutter will shift at Doppler frequency . knowing this we can remove clutter at exact
position ,to remove a certain frequency we make use of fir notch filter as its stable as compared
to iir notch filter if used for such applications. To make use of notch filter we design Doppler
dependent filter where the notch frequency is same as Doppler frequency used to remove a
particular clutter at particular frequency or Doppler. In our case the position generated for 3
clutters are at Doppler zero, prf/4, and prf/2. Using these generated Doppler we design notch
frequency at these positions and remove all 3 perfectly. The filter response is generated for
particular number of pulses which depends on size of data received from radar.

Having used power pc as our hardware we converted the software simulation done on mat lab to
c code which is supported by visual box software used for power pc in lrde. Processing radar
signals require many power pc‘s working parallel, as radar signal obtained consist of large
amount of data which once received is compressed and signal to noise ratio is checked then
further depending of particular application its processes, the application used in our project
consist of finding particular position of clutter depending on Doppler principal and removing it
therefore processing time is less as this is 2-3% of the total processing that happens. For
hardware implementation in c code different software supported function are used for filter
performance and its effectiveness on received signal taking care of parameter used.

5.1 FLOWCHART
READ INPUT DATA
FROMRADAR
READ SIZE OF INPUT
MATRIX
READ AND STORE
SIZE OF COMPLEX
MATRIX
DESIGN FILTER FOR REQUIRED
NUMBER OF PULSES
GENERATE COMPLEX
MATRIX FROMINPUT
PLOT THE OUTPUT
MULTIPLY FILTER RESPONSE
WITH INPUT DATA
PLOT INPUT DATA
MAKE DOPPLER DEPENDANT
FILTER TO REMOVE REQUIRED
DOPPLER
CALCULATE DOPPLER OF
CLUTTER BASED ON VELOCITY
OF RADAR

In our project input consist of clutter and target signals obtained from radar. As input signal is
obtained we first read the signal from hardware and store its size for further processing. The
input signal is in form of matrix therefore its dimension is store for further processing. We get to
know total number of pulses knowing the size of matrix. Now the read data is converted into
complex form as radar signals are complex in nature. Input data is then plotted. Then specific
filter is chosen to remove particular clutter to do so first position of clutter is read from hardware
using Doppler principle and velocity of radar. Now filter is linked or made dependent on these
factors where giving Doppler frequency as read as input of the signals obtained they can be
eliminated. Using doppler frequency we calculate notch frequency which would eliminate that
particular position where clutter lies.
According to Doppler frequency formula that is fd =2*vr*ft/c where vr is velocity of radar, ft as
transmitted frequency. Now omega for Doppler frequency is calculated and given as notch
frequency. The response of notch filter is then plotted for total number of pulses obtained from
input data. After calculating response these are multiplied by input data obtained number of prt
used for different clutter signals. In the end output for each case with different doppler is plotted
to show that at different doppler of clutter it can be removed.

CHAPTER 6
TEST RESULTS
Fig6.1: received signal from radar
This is the matlab plot obtained from the reflections from three different targets which are
located in different positions. These positions are determined by its prf, velocity and the distance
between the radar and the target. The x axis represents the position of the object measured from
the Doppler and the y axis represents the magnitude of the targets. These received signals are
passed through the designed filter to eliminate the undesired targets which is nothing but, called
as clutters.

6.2 Notch filter response at 0 doppler is obtained by using fir notch filter equation
6.3 Output of notch filter performed on received data

The first fig 6.2 represents plot for the notch filter obtained for particular frequency response, in
this case it is at 0 Doppler. The received signal consisting of three targets is passed through this
respective filter. At this specification of the filter the clutter signal at 0th position is eliminated.
The x axis represents the frequency and y axis represents the magnitude. The position of the
filter can be changed according to the prf. PRF is the frequency range beyond which a target
could not be located. This is known as pulse repetitive frequency. PRF is calculated by the time
required by the radar to hit the target and receive back the reflections from the objects.

Fig 6.4: Notch filter response at prf/4 doppler is obtained by using fir notch filter equation
6.5 output of notch filter performed on received data for clutter at prf/4 doppler

The first fig 6.4 represents plot for the notch filter obtained for particular frequency
response, in this case it is at prf/4 Doppler. The received signal consisting of three targets is
passed through this respective filter. At this specification of the filter the clutter signal at prf/4th
position is eliminated. The x axis represents the frequency and y axis represents the magnitude.
The position of the filter can be changed according to the prf. PRF is the frequency range beyond
which a target could not be located. This is known as pulse repetitive frequency. PRF is
calculated by the time required by the radar to hit the target and receive back the reflections from
the objects.

6.6 notch filter response at prf/2 doppler is obtained by using fir notch filter equation
6.7 output of notch filter performed on received data for clutter at prf/2 doppler

The first fig 6.6 represents plot for the notch filter obtained for particular frequency response, in
this case it is at prf/2 Doppler. The received signal consisting of three targets is passed through
this respective filter. At this specification of the filter the clutter signal at prf/2th position is
eliminated. The x axis represents the frequency and y axis represents the magnitude. The position
of the filter can be changed according to the prf. PRF is the frequency range beyond which a
target could not be located. This is known as pulse repetitive frequency. PRF is calculated by the
time required by the radar to hit the target and receive back the reflections from the objects.

CHAPTER 7
CONCLUSION
The MTI system which was used is incapable of detecting targets at low frequency also they
eliminated only stationary clutters which made the moving clutters wrongly interpreted as
targets. It also removed targets associated with stationary clutter which appeared at 0 doppler. As
a result, a new method is developed where the filter is made automatically to recognize the
clutter characteristics and adjust itself to a condition to remove only the moving clutters. This is
known as adaptive filter.
In this project, a simple and efficient technique for adaptive moving target detector using a
notch filter is presented. The implementation is to enhance adaptive filtering capability during
radar target detection in an ever changing environment. The proposed design is highly effective
even in very strong clutter conditions. Since it is very difficult to extract target embedded with
non-homogeneous clutters, using the relative velocity between the target and clutter is
successfully used to remove clutters. The MATLAB simulation results for adaptive MTI shows
that the proposed design works satisfactorily in various clutter environments.
An adaptive MTI filter that is unique, since the filter weights are estimated in the same resolution
cell as is filtered. Additionally the algorithm is very simple and fast. The average Doppler shift
must be estimated separately.

REFERENCES
[1] Bowyer D B, Rajaw: kai.an P K and Oebhart W W. Aahptive Clwer
Autoregtvssive Spe~:&~l Estimation, IBEE Trans. Aerospaceand Eleattonic
Sptems, tol.A@b15 No. 4 July 1979
[2]JArmstrong B C, GriFfith White R G. Perfomurn processotsin heten Rada&
Sonar Navig, Vol. 142, No 4 August 1995
[3]Scolnik M I (1990): R& Hundbook, Second Edition, Mdjraw-Hill, ISBN0-07
057913-X
[4]Schleher D C (1991 :): A427 and Pulsed Doppler R h, Artech HOUS~, ISBN0-
89006-320-6.
[5] Fundamentals of radar system: Byron eddy

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