| The presence of towing jamming seriously jeopardizes radar tracking performance.Towing jamming tricks the radar tracking wave gate to break away from the real target,resulting in the interruption of the radar target tracking trajectory,and it is difficult to achieve continuous and robust monitoring of the target.Therefore,the research on anti-towing jamming tracking method is an urgent frontier in the field of electronic anti-jamming at home and abroad.The existing anti-towing jamming methods mainly utilize the differential characteristics of echo signals to identify and suppress towing jamming,making it difficult to cope with the current threat of refined and intelligent towing jamming,resulting in a significant decline in radar tracking performance.Studying anti-towing jamming methods at the data processing level is beneficial for compensating for the shortcomings of anti-towing jamming methods at the signal level and improving the target tracking performance of radar in complex electromagnetic backgrounds.Based on the analysis of traditional target tracking algorithms,this article conducts research on the generation method of anti-towing jamming tracking strategies.The main achievements are as follows:1.The main processes of target tracking were summarized,including data association,trajectory processing and tracking filtering,the process of towing jamming was analyzed,and conventional towing jamming strategies were introduced,laying the foundation for studying radar anti-towing jamming tracking methods.2.Focusing on the impact of towing jamming measurement on data association,theχ~2-M/N logic method was used to study the anti-towing jamming tracking method based on trajectory backtracking,which improved the performance degradation of the Joint Probability Data Association(JPDA)algorithm when dealing with towing jamming.The simulation results indicate that the method has better tracking performance than the JPDA-Kalman filtering algorithm in terms of performance indicators such as cumulative interruption times,trajectory accuracy,and optimal sub-pattern assignment(OSPA)distance.3.In response to the problem of deteriorating performance of traditional Kalman filtering when subjected to towing jamming,a memory factor was introduced to construct an anti-towing jamming memory filter tracking model.A method for generating anti-towing jamming memory filter tracking strategies based on improved particle swarm optimization algorithm was studied.Finally,the tracking performance of the proposed method was verified to be superior to the conventional JPDA-Kalman filtering algorithm through performance indicators such as cumulative interruption times,track accuracy,and OSPA distance.4.A non-uniform revisit anti-towing jamming tracking model was constructed to address the issue of difficulty in dealing with towing jamming in the tracking beam uniform revisit method.A method for generating non-uniform revisit anti-towing jamming tracking strategies based on improved particle swarm optimization algorithm was studied.The proposed method was verified to be superior to the tracking beam uniform revisit mode through simulation using indicators such as cumulative number of interruptions in the trajectory,trajectory accuracy,and OSPA distance.The methods proposed in this thesis have been validated for effectiveness through simulation. |
