1. NAME - ANNWESH BARIK
ROLLNO - 111CS0119

2.  Target tracking using Kalman Filter can be
described as the process of determining the
location of a target at current state
depending upon its location at previous state.
 It is one of the most important applications
of sequential state estimation, which naturally
admits Kalman filter as its main candidate.

3.  Design of Kalman filter algorithm to track the
target and show the resulting improvement in
tracking. This is of utmost importance for
high-performance real-time applications.
 Using MATLAB

4.  The Kalman filter, is an algorithm that uses a
series of measurements observed over time,
containing noise (random variations) and
other inaccuracies, and produces estimates of
unknown variables that tend to be more
precise than those based on a single
measurement alone.

5. ENV Coordinate System
Range = r
Azimuth = θ
Elevation = φ
Z= r*sin(φ)
X= r*cos(φ)*cos(θ)
Y= r*cos(φ)*sin(θ)

6. The two equations of Kalman Filter is as follows :
State Prediction :
Measurement Prediction :
To start the process, we need to know the
estimate of x0, and P0.

10.  M.S.Grewal, A.P. Andrews, "Kalman Filtering -
Theory ", Wiley, 2001
 An Approach for Switching of the Process
Noise Co-Variance of Kalman Filter for
ManeuveringTargets - S.M.Sahoo1, Sandipan
Sarkar2, H.K.Ratha3
 Thank you