DocumentCode :
781580
Title :
Offline and Real-Time Methods for ML-PDA Track Validation
Author :
Blanding, Wayne R. ; Willett, Peter K. ; Bar-Shalom, Yaakov
Author_Institution :
Dept. of Electr. & Comput. Eng, Connecticut Univ., Storrs, CT
Volume :
55
Issue :
5
fYear :
2007
fDate :
5/1/2007 12:00:00 AM
Firstpage :
1994
Lastpage :
2006
Abstract :
We present two procedures for validating track estimates obtained using the maximum-likelihood probabilistic data association (ML-PDA) algorithm. The ML-PDA, developed for very low observable (VLO) target tracking, always provides a track estimate that must then be tested for target existence by comparing the value of the log likelihood ratio (LLR) at the track estimate to a threshold. Using extreme value theory, we show that in the absence of a target the LLR at the track estimate obeys approximately a Gumbel distribution rather than the Gaussian distribution previously ascribed to it in the literature. The offline track validation procedure relies on extensive offline simulations to obtain a set of track validation thresholds that are then used by the tracking system. The real-time procedure uses the data set that produced the track estimate to also determine the track validation threshold. The performance of these two procedures is investigated through simulation of two active sonar tracking scenarios by comparing the false and true track acceptance probabilities. These techniques have potential for use in a broader class of maximum likelihood estimation problems with similar structure
Keywords :
maximum likelihood estimation; probability; sonar tracking; target tracking; Gumbel distribution; ML-PDA track validation; active sonar tracking; extreme value theory; log likelihood ratio; maximum likelihood estimation; maximum-likelihood probabilistic data association; offline track validation; real-time methods; target tracking; Dynamic programming; Gaussian distribution; Maximum likelihood detection; Maximum likelihood estimation; Noise measurement; Personal digital assistants; Sonar; State estimation; Target tracking; Testing; Extreme value theory; maximum likelihood (ML); probabilistic data association (PDA); target tracking; track validation;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2007.893212
Filename :
4156361
Link To Document :
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