A Bayesian tracker for multi-sensor passive narrowband fusion

Ryan J. Pirkl; Jason M. Aughenbaugh

doi:10.1117/12.2223794

17 May 2016 A Bayesian tracker for multi-sensor passive narrowband fusion

Ryan J. Pirkl, Jason M. Aughenbaugh

Proceedings Volume 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV; 984204 (2016) https://doi.org/10.1117/12.2223794
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

We demonstrate the detection and localization performance of a multi-sensor, passive sonar Bayesian tracker for underwater targets emitting narrowband signals in the presence of realistic underwater ambient noise. Our evaluation focuses on recent advances in the formulation of the likelihood function used by the tracker that provide greater robustness in the presence of both realistic environmental noise and imprecise/inaccurate a priori knowledge of the target’s narrowband signal. These improvements enable the tracker to reliably detect and localize narrowband emitters for a broader range of propagation environments, target velocities, and inherent uncertainty in a priori knowledge.

Citation Download Citation

Ryan J. Pirkl and Jason M. Aughenbaugh "A Bayesian tracker for multi-sensor passive narrowband fusion", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 984204 (17 May 2016); https://doi.org/10.1117/12.2223794

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