Multiple Radar Target Tracking in Environments with High Noise and Clutter

Tracking a time-varying number of targets is a challenging dynamic state estimation problem whose complexity is intensified under low signal-to-noise ratio (SNR) or high clutter conditions. This is important, for example, when tracking multiple, closely spaced targets moving in the same direction such as a convoy of low observable vehicles moving through a forest or multiple targets moving in a crisscross pattern. The SNR in these applications is usually low as the reflected signals from the targets are weak or the noise level is very high. An effective approach for detecting and tracking a single target under low SNR conditions is the track-before-detect filter (TBDF) that uses unthresholded measurements. However, the TBDF has only been used to track a small fixed number of targets at low SNR.;This work proposes a new multiple target TBDF approach to track a dynamically varying number of targets under the recursive Bayesian framework. For a given maximum number of targets, the state estimates are obtained by estimating the joint multiple target posterior probability density function under all possible target existence combinations. The estimation of the corresponding target existence combination probabilities and the target existence probabilities are also derived. A feasible sequential Monte Carlo (SMC) based implementation algorithm is proposed. The approximation accuracy of the SMC method with a reduced number of particles is improved by an efficient proposal density function that partitions the multiple target space into a single target space.;The proposed multiple target TBDF method is extended to track targets in sea clutter using highly time-varying radar measurements. A generalized likelihood function for closely spaced multiple targets in compound Gaussian sea clutter is derived together with the maximum likelihood estimate of the model parameters using an iterative fixed point algorithm. The TBDF performance is improved by proposing a computationally feasible method to estimate the space-time covariance matrix of rapidly-varying sea clutter. The method applies the Kronecker product approximation to the covariance matrix and uses particle filtering to solve the resulting dynamic state space model formulation.