Research On Ship Target Tracking Under Non-common-view Condition Based On Passive Location

The continuous development of contemporary electronic information technology has led to an increasing demand for passive localization and filtering tracking systems in electronic warfare.The focus of research is on how to locate enemy radar targets covertly,quickly,and accurately,as well as controlling the dynamics of our radar.However,in practical radar localization environments,various complex situations can cause multiple observation stations in a multi-station passive localization system to be unable to receive radiation source signals simultaneously,which is referred to as the "non-common-view situation".This article addresses this situation by conducting research on the existing problems in practical applications based on Time Difference of Arrival(TDOA)and Direction of Arrival(DOA)localization models.The article begins by introducing common traditional passive localization methods and conducting simulation experiments on TDOA and DOA models.The formula for the geometric precision factor is derived,and the theoretical positioning performance is simulated and analyzed.Additionally,the article introduces the basic principles of the traditional Kalman filter algorithm and applies it to optimize the filtering of two localization algorithms.The accuracy of the localization algorithm before and after filtering is compared and analyzed.Secondly,aiming at the problem that the joint action of multiple errors in the traditional positioning algorithm leads to the sharp deterioration of positioning accuracy,this article applies the sparrow search algorithm to the field of passive localization and proposes a localization method based on the sparrow search algorithm to improve the robustness of the positioning algorithm.Inspired by the behavior of sparrow populations searching for food,this algorithm analogizes coordinate points to sparrow individuals and continuously searches and optimizes the observation space to find the optimal position of food.In addition,by analyzing some shortcomings of the sparrow search algorithm,the traditional sparrow search algorithm is improved mainly for the problem that it is easy to fall into the local optimal solution.The experimental results show that the improved algorithm can further improve the positioning accuracy while maintaining the convergence ability of the population.In addition,the non-common-view conditions for multi-station passive localization tracking are investigated,and this aspect of non-common-view conditions due to obstacles is specifically analyzed.In traditional applications,when the target is converted from co-visual to non-co-visual(or from non-co-visual to co-visual),the localization algorithm is often switched between time-difference localization and directional localization,but at the same time,the target motion trajectory may jump because of the different accuracy of the two algorithms.Therefore,this paper proposes a joint algorithm combining traditional Kalman filter tracking algorithm and long short-term memory network,and applies it to the passive positioning tracking system.The stability and effectiveness of the algorithm are guaranteed.Finally,based on the actual simulation data,this paper first proposes the conversion method between WGS-84 coordinate system and the coordinate system required for target positioning,and then carries out the engineering implementation of the filter tracking algorithm proposed in this paper to address the situation that data may be missing in the actual simulation data based on the actual simulation data,at this time,the initial PDW data are fused and sorted by the process of fusion according to PRI for pulse In this case,the initial PDW data are fused and binned according to the PRI to fill in the gaps and mark them,and then the useful information in the pulse is extracted for target localization.Through the analysis of the simulation results,it is proved that the application of this method in engineering projects can effectively improve the positioning accuracy and reduce the occurrence of state change boundary jump.