基于雷达辐射源信号特征的类别信息辅助GM-PHD滤波器

雷达辐射源目标跟踪在军事应用领域具有重要的意义。结合目标类别信息有助于提高高斯混合概率假设密度(Gaussian mixture-probability hypothesis density,GM-PHD)滤波器多目标跟踪的性能,但电子侦察系统获得的雷达辐射源信号信息无法直接应用于上述滤波器。为此,先利用辐射源信号特征进行雷达类型识别,然后基于可传递信度模型根据雷达平台的配属关系将该识别结果转换到与已知类别信息相同的辨识框架内。在此基础上,采用相容系数度量其相似度用以近似GM-PHD滤波器中的量测似然值,从而实现类别信息的辅助目标跟踪。仿真实验表明,在不同的杂波密度下所提方法能够有效提高GM PHD滤波器的跟踪性能。

Classification-aided GM-PHD filter based on signal feature of radar emitter

Tracking for radar emitter targets plays an important role in the field of military application. Although combining with the target classification information is helpful to improve the multi-target tracking performance of the Gaussian mixture-probability hypothesis density (GM-PHD) filter, the signal information of the radar emitter received by the electronic reconnaissance system cannot be applied to the above filter directly. Therefore, this paper first makes use of the signal features to identify the radar types, then based on the transferable belief model the recognition results are transformed into the same frame of the known classification information according to the radar-platform affiliation. Based on that, their similarity measured by the compatibility ratio is used to approximate the likelihoods in the GM-PHD filter. As a result, a modified GM-PHD filter with the classification information can be implemented. The simulation results show that the proposed method can effectively improve the tracking performance of the GM-PHD filter in the scenarios with different clutter densities.