Computer Science > Computer Vision and Pattern Recognition
arXiv:2302.11356 (cs)
[Submitted on 22 Feb 2023]
Title:Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems
View a PDF of the paper titled Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems, by Haiyi Mao and 4 other authors
View PDFAbstract:A variety of filters with track-before-detect (TBD) strategies have been developed and applied to low signal-to-noise ratio (SNR) scenarios, including the probability hypothesis density (PHD) filter. Assumptions of the standard point measurement model based on detect-before-track (DBT) strategies are not suitable for the amplitude echo model based on TBD strategies. However, based on different models and unmatched assumptions, the measurement update formulas for DBT-PHD filter are just mechanically applied to existing TBD-PHD filters. In this paper, based on the Kullback-Leibler divergence minimization criterion, finite set statistics theory and rigorous Bayes rule, a principled closed-form solution of TBD-PHD filter is derived. Furthermore, we emphasize that PHD filter is conjugated to the Poisson prior based on TBD strategies. Next, a capping operation is devised to handle the divergence of target number estimation as SNR increases. Moreover, the sequential Monte Carlo implementations of dynamic and amplitude echo models are proposed for the radar system. Finally, Monte Carlo experiments exhibit good performance in Rayleigh noise and low SNR scenarios.
| Comments: | in 2023 IEEE Radar Conference (RadarConf2023), 2023 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2302.11356 [cs.CV] |
| (orarXiv:2302.11356v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2302.11356 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems, by Haiyi Mao and 4 other authors
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