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Proceedings Paper

Experimental results in bearings-only tracking using the sequential Monte-Carlo probability hypothesis density filter
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Paper Abstract

We evaluate the use of a probability hypothesis density (PHD) filter in a bearings-only tracking application. The main feature of a PHD filter is that it propagates the first-order statistical moment of a multisource posterior distribution. Multisource estimation using a PHD filter has been shown to reliably track multiple simulated targets in the bearings-only case. In this paper we evaluate the utility of the sequential Monte-Carlo PHD filter for tracking surface ships using bearings-only data acquired from a Bluefin-21 unmanned underwater vehicle in Boston Harbor. The unmanned underwater vehicle was equipped with a rigidly mounted planar hydrophone array that measures the bearing angle to sources of acoustic noise, of which shipping traffic is the dominant source. We further evaluate several target maneuvering models, including clockwise and counter-clockwise coordinated turns. The combination of the coordinated turn models with a constant velocity model is used in a multiple model PHD filter. The results of the multiple model PHD filter are compared to the results of a PHD filter using only a constant velocity model.

Paper Details

Date Published: 7 May 2019
PDF: 14 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101808 (7 May 2019); doi: 10.1117/12.2519047
Show Author Affiliations
Jorge Jimenez, Virginia Polytechnic Institute and State Univ. (United States)
Artur Wolek, Univ. of Maryland, College Park (United States)
Daniel J. Stilwell, Virginia Polytechnic Institute and State Univ. (United States)
James McMahon, U.S. Naval Research Lab. (United States)
Benjamin Dzikowicz, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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