  In this work, we develop tracking and estimation techniques relevant to
underwater targets. Particularly, we explore particle filtering techniques for
target tracking. It is a numerical approximation method for implementing a
recursive Bayesian estimation procedure. It does not require the assumptions of
linearity and Guassianity like the traditional Kalman filter based techniques
and is capable of handling non-Gaussian noise distributions and non-linearities
in the measurements as well as target dynamics. The performance of particle
filters is verified using simulations and compared with Extended Kalman Filter.
Particle filters can track multi-targets and highly maneuvering targets.
However, it has higher computational load. The efficient use of particle
filters for multi-target tracking using Independent Partition Particle Filter
(IPPF) and tracking highly maneuvering targets using Multiple Model Particle
Filter(MMPF) are also explored in this work. These techniques require only
smaller number of particles and help in reducing the computational cost. Data
association problem exists in multi-target tracking due to lack of information
at the observer about the proper association between the targets and the
received measurements. The problem becomes more involved when the targets move
much closer and there are clutter and missed target detections at the observer.
Monte Carlo Joint Probabilistic Data Association Filter (MCJPDAF) efficiently
solves data association for the mentioned situation. Due to the inability of
the standard MCJPDAF to track highly maneuvering targets, Monte Carlo Multiple
Model Joint Probabilistic Data Association Filter (MC-MMJPDAF) which combines
the technique of Multiple Model Particle Filter(MMPF) in the framework of
MC-JPDAF has been proposed. The simulation results shows the superiority of the
proposed method.
