The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.

This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.

This paper presents a new multi-target data association method for automotive radar which we call the order statistics joint probabilistic data association (OSJPDA). The method is formulated using the association probabilities of the joint probabilistic data association (JPDA) filter and an optimal target-to-measurement data association is accomplished using the decision logic algorithm. Simulation results for heavily cluttered conditions show that the tracking performance of the OSJPDA filter is better than that of the JPDA filter in terms of tracking accuracy by about 18%.