Abstract:
In Multitarget Tracking (MTT), several targets of interest are being tracked
simultaneously with the help of any optimal estimator. MTT tracking finds its
applications in diverse fields like Pattern Recognition, Computer Vision, Radar
Tracking, Robotics and many other research fields. In the literature, several
algorithms have been implemented for MTT including Probabilistic Data
Association Filter (PDAF), Joint Probabilistic Data Association Filter
(JPDAF), Nearest Neighbor Standard Filter (NNSF), etc. JPDAF is the
multitarget version of PDAF in which joint association probabilities are
computed and tracks are then updated based upon these probabilities.
Measurement noise covariance matrix R in JPDAF needs to be transformed from
polar to Cartesian coordinate system. The optimal value of R should be
calculated for the good performance of filter. In this thesis, measurement noise
covariance matrix for JPDAF algorithm has been derived using standard radar
parameters. 2D tracking is performed using scan radar and JPDAF algorithm.
3D tracking is also performed in a closed loop fashion using monopulse radar and
JPDAF algorithm. For both 2D and 3D tracking, simulations are performed in
MATLAB. Desired results are achieved and the error is reduced to such an
extent that it lies inside the range bin for both cases.
