Gaussian mixture probability hypothesis density smoothing with multistatic sonar

N. Nandakumaran; R. Tharmarasa; T. Lang; T. Kirubarajan

doi:10.1117/12.779236

17 April 2008 Gaussian mixture probability hypothesis density smoothing with multistatic sonar

N. Nandakumaran, R. Tharmarasa, T. Lang, T. Kirubarajan

Proceedings Volume 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII; 696807 (2008) https://doi.org/10.1117/12.779236
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

N. Nandakumaran, R. Tharmarasa, T. Lang, and T. Kirubarajan "Gaussian mixture probability hypothesis density smoothing with multistatic sonar", Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 696807 (17 April 2008); https://doi.org/10.1117/12.779236

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