Improved Multitarget Track Before Detect Algorithm Using the Sequential Monte Carlo Probability Hypothesis Density Filter

The Detection and tracking of multi-target is a challenging issue under the condition with unknown and varied target number, especially when the Signal-to-Noise Ratio (SNR) is low. An improved Track-Before-Detect (TBD) method for multiple spread targets is proposed by using point spread observation model. The method is prepared from the framework of the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter, and it is implemented by firstly adopting an adaptive particle generation strategy, which can obtain the rough position estimates of the potential targets. The particle set is then partitioned into multiple subsets according to their position coordinates in 2D image plane and an efficient evaluation of the updated particle weights is accomplished by utilizing the convergence property of the particles. Target tracks are finally constructed from the extracted multitarget states via dynamic clustering technique. Simulation results show that the presented method can not only greatly improve the performance of multitarget TBD, but also significantly reduce the executing time of SMC-PHD based implementation.