A Method of False-target Elimination Based on Asynchronous Fusion
Cai Wang, Tao Tang, Meiguo Gao · 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) · 2021
For the problem of multi-false-target deception jamming, the netted radar system is one effective anti-jamming method. This paper describes a method of false-target elimination based on the asynchronous centralized fusion of the netted radar system and applies it to the range false-target deception jamming and the velocity false-target deception jamming. The method first fuses the measurements from different node radars to generate tracks using the asynchronous sequential fusion technique. Then, based on the spatial position distribution correlation characteristics of the measurements obtained by different node radars, three attribute features of an obtained track, i.e., the number of data source node radars, the average data rate and the ratio for each node radar’s measurements used for updating the track, are extracted to determine whether a track is real or false. For a velocity false target, its measurements are automatically absorbed at the data association stage of the fusion filtering processing by the nearest neighbor (NN) association criterion and no false track are generated. Simulations are performed to validate the effectiveness of the method.