Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

Abstract

Multi-range-false-target (MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking (MTT) methods suffer from high computational complexity and limited usage in the presence of MRFT jamming. In order to solve the above problems, an efficient and adaptable probability hypothesis density (PHD) filter is proposed. Based on the gating strategy, the obtained measurements are firstly classified into the generalized newborn target and the existing target measurements. The two categories of measurements are independently used in the decomposed form of the PHD filter. Meanwhile, an amplitude feature is used to suppress the dense clutter. In addition, an MRFT jamming suppression algorithm is introduced to the filter. Target amplitude information and phase quantization information are jointly used to deal with MRFT jamming and the clutter by modifying the particle weights of the generalized newborn targets. Simulations demonstrate the proposed algorithm can obtain superior correct discrimination rate of MRFT, and high-accuracy tracking performance with high computational efficiency in the presence of MRFT jamming in the dense clutter.

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

“…Especially, since cognitive radar technology gradually became a research hotspot in the field, the demand for real-time perception and fine characterization of radar clutter environment has become more and more urgent. The refined characterization of radar clutter is represented by its corresponding optimal selection of radar clutter model, which has a profound impact on cognitive waveform design [19], constant false alarm rate (CFAR) detection [20], multi-target tracking [21], and target recognition under clutter background [22]. Different from the previous environmental attribute classification based on clutter features, clutter model for real radar data does not have an objective label in hand.…”

Multi‐target pair‐matching method based on angle information in transmit/receive‐receive synergetic High Frequency Surface Wave Radar

“…However, the literature with respect to T/R-R radar multi-target matching are few. The current multi-target pairing methods mainly focus on the track association of target [16][17][18][19].…”

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

“…Under the scenario of dense clutter, an improved SMC‐PHD algorithm for MTT problem is proposed by Chen et al. to deal with multi‐range‐false‐target jamming [31]. By propagating the posterior intensity of each sensor at prediction step, the iterated‐corrector probability hypothesis density (IC‐PHD) algorithm is proposed by Liu et al.…”

“…Considering the unknown process noise and measurement noise, a robust adaptive PHD filter is proposed by Gu et al to address the degradation of PHD performance, which introduces the inverse Wishart distribution to model the prior distribution of process noise and measurement noise [30]. Under the scenario of dense clutter, an improved SMC-PHD algorithm for MTT problem is proposed by Chen et al to deal with multi-range-false-target jamming [31]. By propagating the posterior intensity of each sensor at prediction step, the iterated-corrector probability hypothesis density (IC-PHD) algorithm is proposed by Liu et al to improve the tracking accuracy [32].…”