Gaussian mixture probability hypothesis density smoothing with multistatic sonar

Abstract

Passive sonar is widely used in practice to covertly detect maritime vessels. However, the detection of stealthy vessels often requires active sonar. The risk of the overt nature of active sonar operation can be reduced by using multistatic sonar techniques. Cheap sonar sensors that do not require any beamforming technique can be exploited in a multistatic system for spacial diversity. In this paper, Gaussian mixture probability hypothesis density (GMPHD) filter, which is a computationally cheap multitarget tracking algorithm, is used to track multiple targets using the multistatic sonar system that provides only bistatic range and Doppler measurements. The filtering results are further improved by extending the recently developed PHD smoothing algorithm for GMPHD. This new backward smoothing algorithm provides delayed, but better, estimates for the target state. Simulations are performed with the proposed method on a 2-D scenario. Simulation results present the benefits of the proposed algorithm.

Gaussian mixture PHD smoother for jump Markov models in multiple maneuvering targets tracking

“…As discussed in [15], the second term on the right hand side of (45) can be implemented using the Rauch-Tung-Striebel type smoothing algorithm [19]. More precisely,…”

“…Recently, the backward smoothing PHD recursion has been derived to improve the tracking performance by employing the physicalspace approach [13]. The particle implementation and the Gaussian mixture implementation have been carried out in [14] and [15], respectively. Furthermore, the authors in [16] extended the particle implementation to the multiple model PHD recursion.…”

Poisson Point Processes

“…This contributes to what has been appropriately called [37] "spooky action at a distance." An even more recent topic of interest is that of smoothing PHD filters [47,86,91]. The intention is to reduce variance by introducing time lag into the intensity function estimates.…”

Promotion of GM-PHD Filtering Approach for Single-Target Tracking in Raw Data of Synthetic Aperture Radar in Spotlight Imaging Mode

“…For tracking multiple maneuvering targets, similar results have been extended to handle jump Markov models [7][8][9][10]. To derive PHD smoothers, the particle and Gaussian mixture techniques have also been used [11][12][13][14][15][16][17][18]. In [19], the GM-PHD filter is extended to multi-sensor tracking system and the target state estimates are obtained sequentially at each sensor.…”