Paper
19 May 2006 Track management in a multisensor MHT for targets with aspect-dependent SNR
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Abstract
In many active sonar tracking applications, targets frequently undergo fading detection performance in which the target's detection probability can shift suddenly from a high to a low value. This characteristic is a function of the undersea environment. Using a multistatic active sonar problem, we examine the performance of track management (initiation and termination) routines where the target detection probability is based on an underlying Hidden Markov Model (HMM) with high and low detection states. Using a likelihood ratio test, we develop the optimum track initiation performance as measured by a System Operating Characteristic, similar to a Receiver Operating Characteristic, which plots the probability of initiating a true track versus the probability of initiating a false track. We show that near-optimal performance can be attained using track initiation logic that differentiates the measurements as to receiver source in an "M detections of N scans from C sensors" type of rule. Performance can further be improved by using a composite track initiation test that combines two or three such rules in a logical OR operation. We next show that the use of a Shiryaev-Roberts test for track termination yields the quickest detection of false tracks for a given duration of true target tracks when compared to a Page test and rule-based tests of the form "M or fewer detections from K scans".
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wayne R. Blanding, Peter K. Willett, Yaakov Bar-Shalom, and Stefano Coraluppi "Track management in a multisensor MHT for targets with aspect-dependent SNR", Proc. SPIE 6236, Signal and Data Processing of Small Targets 2006, 623613 (19 May 2006); https://doi.org/10.1117/12.665198
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Cited by 2 scholarly publications.
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KEYWORDS
Sensors

System on a chip

Target detection

Composites

Fourier transforms

Logic

Performance modeling

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