Abstract:

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.