基于GM-PHD的目标空间分布感知算法

对边扫描边跟踪的认知雷达，目标的空间分布特性是实现对雷达信号控制的重要依据之一。本文介绍了一种基于高斯混合概率假设密度（GM-PHD）滤波算法的目标空间分布感知方法，利用该算法，可同时实现多目标高虚警环境下的目标数目和目标空间位置以及运动状态的估计。该算法实际是一种对标准GM-PHD滤波器的改进算法，能在新生目标强度未知的情况下完成对新生目标的检测跟踪。实验表明该算法不仅能在未知新生目标强度的情况下检测并跟踪新生目标，且在新生目标速度较大的情况下，该算法对新生目标的检测性能优于标准GM-PHD滤波器。

Target Spatial Distribution Perception Algorithm Based on GM-PHD

For track-while-scan (TWS) cognitive radar, the spatial distribution characteristic of target is one of the important references to realize the control of radar signal. In this paper, a target spatial distribution perception method based on the Gaussian mixture probability hypothesis density (GM-PHD) filtering algorithm is introduced, which can be used to simultaneously estimate the number, spatial positions, and motion states of targets in the environment of multiple target and high false alarm rate. This algorithm is actually an improved algorithm for the standard GM-PHD filter. It can complete the detection and tracking of newborn targets when the strength of newborn targets is unknown. Experiments show that the algorithm can not only detect and track newborn targets with unknown strength, but also outperform the standard GM-PHD filter in detecting newborn targets with high speed.