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.

中文翻译：

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基于PHD滤波器的多目标假目标多目标跟踪算法

由于密集的杂波和重复的多个错误目标，多范围错误目标（MRFT）干扰对于跟踪雷达尤其具有挑战性。常规的基于关联的多目标跟踪（MTT）方法存在较高的计算复杂度，并且在存在MRFT干扰的情况下使用受限。为了解决上述问题，提出了一种高效且适应性强的概率假设密度（PHD）滤波器。基于门控策略，首先将获得的测量结果分为广义新生儿目标测量值和现有目标测量值。这两类测量以PHD滤波器的分解形式独立使用。同时，使用幅度特征来抑制密集杂波。另外，将MRFT干扰抑制算法引入滤波器。目标幅度信息和相位量化信息通过修改广义新生儿目标的粒子权重，共同用于处理MRFT干扰和混乱。仿真表明，该算法在密集杂波中存在MRFT干扰的情况下，可以得到较高的MRFT正确判别率，并具有较高的计算效率和高精度的跟踪性能。