Bayesian track-before-detect algorithm for nonstationary sea clutter

Abstract

Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

“…Assume that represents N independent particles distributed with weights which are generated by the posterior PDF p(x 0:k |z 1:k ), then the true PDF can be expressed [24] based on Dirac function δ(•):…”

“…4.2.6 Resampling [24] Resampling is an effective method to mitigate the degeneracy problem in the SMC implementation of PF. The main idea of resampling is that if the effective sample number is larger than a threshold, resampling is then performed.…”

“…In general, the QPE caused by Doppler rate can defocus the target and lead to reduction in image SNR; but if the CIT is short enough (several seconds), these effects will not be serious. Based on this consideration, the nonthreshold method can be used for MTD, such as track before detection (TBD) [23,24]. Using a sequence of system measurements, the TBD can potentially detect mov-Manuscript received February 28, 2022.…”

“…A few TBD algorithms have been developed based on the recursive Bayesian estimator, such as the particle filter (PF) [25−27], the probability hypothesis density (PHD) filter [28,29], and the multi-Bernoulli filter [30,31]. Due to its simple implementation and good performance at low SNR conditions, the PF and its variations have been widely used [24]. The PF and its implementation were firstly introduced for low SNR point target detection [25,27].…”

“…The PF and its implementation were firstly introduced for low SNR point target detection [25,27]. Moreover, detailed explanation, performance analysis, and the Cramer-Rao lower bound of PF were also presented [24], and modified PFs were proposed for detection of maneuvering targets, multiple targets and extended targets [32−34]. Further improvements were made on the PF in radar signal processing [23,35].…”