Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems
Haiyi Mao, Cong Peng, Yue Liu, Jinping Tang, Hua Peng, Wei Yi

TL;DR
This paper derives a new Poisson conjugate prior for PHD filters in track-before-detect radar systems, providing a rigorous, closed-form solution and demonstrating improved performance in low SNR conditions.
Contribution
It introduces a principled derivation of the TBD-PHD filter based on Kullback-Leibler divergence and establishes the conjugacy of PHD filters to Poisson priors in TBD strategies.
Findings
Effective in Rayleigh noise and low SNR scenarios
Provides a closed-form solution for TBD-PHD filter
Demonstrates improved target tracking performance
Abstract
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…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Structural Health Monitoring Techniques · Probabilistic and Robust Engineering Design
