Research On Key Technologies Of Clutter Suppression And Target Tracking For Non-cooperative Bistatic Radar

In recent years,non-cooperative bistatic radar detection system has become a research hotspot in radar field because of its "four anti" characteristics and low cost.The passive detection system using non-cooperative radar signal as external radiation source not only improves the detection power performance of external radiation source radar,but also expands the types of available external radiation sources.However,it is facing many challenges,especially the clutter interference and target stable tracking in the process of system detection.In this dissertation,the key technologies of clutter suppression and target tracking for non-cooperative bistatic radar are studied.The main contents are as follows:The second chapter analyzes the detection performance of non-cooperative bistatic radar system.Firstly,the principle and structure of the non-cooperative bistatic radar system are introduced.Then,based on the bistatic radar equation,the range equation of the non-cooperative bistatic radar with LFM pulse signal as the external radiation source is constructed,and the influence of the non-cooperation of the radiation source on the detection performance of the system is analyzed.Finally,the main problems in system detection are analyzed.In the third chapter,the algorithm of short-range clutter suppression in the signal processing phase of non-cooperative bistatic radar detection system is studied.On the basis of the time domain extended cancellation algorithm,combined with the characteristics of the pulse signal emitter,and taking full advantage of the advantages of the digital array receiving antenna,a step-by-step adaptive extended cancellation algorithm based on the blocking matrix is proposed.The experimental results show that the algorithm proposed in this dissertation can retain the echo signal components of the target and suppress the direct wave interference and multi-path ground clutter interference as much as possible in the process of clutter suppression,which is helpful for the detection of the following short-range targets.In Chapter 4,the algorithm of clutter suppression in non-cooperative bistatic radar data is studied.Based on the bistatic measurement model,an iterative grid element construction algorithm for non-uniform bistatic measurement is proposed.Then,based on the measurement grid cell model,an algorithm of false alarm suppression based on morphological expansion of non-uniform grid is proposed for the false alarm interference in non-cooperative bistatic radar data.Simulation and experimental results show that the proposed algorithm can suppress a large number of false alarm data,and combine with data association algorithm can effectively improve the performance of non-cooperative bistatic radar data association algorithm in dense clutter interference environment.Aiming at the static clutter and other residual clutter problems in non-cooperative bistatic radar data,a clutter suppression algorithm based on grid morphological processing and multidimensional RANSAC(Random Sample Consensus)is proposed.The experimental results show that the proposed algorithm can effectively suppress the ground clutter and other residual clutter interference,and retain the target data,especially the target data with low signal-to-noise ratio,which is not affected by the pulse interval time agility of the emitter signal.In Chapter 5,the multi-target tracking algorithm based on random finite set is studied.In order to solve the problems of complex scenes such as low target detection probability,unknown location and occurrence time of new targets in multi-target tracking of noncooperative bistatic radar detection system,an improved PHD filter with adaptive new strength estimation is proposed in the framework of RFS(Random Finite Set)multi-target tracking algorithm.Aiming at the problem of low target detection probability,the formal state set and temporary state set are established in the classical PHD(Probability Hypothesis Density)filter,and the forgetting factor parameter of state is introduced in the temporary state set,so as to realize the construction of dynamic detection probability in the temporary state set.Combined with the proposed improved PHD filter structure,an adaptive estimation method of newborn intensity is proposed to solve the problems of position random and time random.Simulation and experimental results show that the performance of the proposed algorithm is better than that of other multi-target tracking algorithms with adaptive strength estimation.