Research On Distributed Passive Location Method Under Uncertainty

Passive location technology is a kind of technology which can accquire the target's position only by receiving and analyzing the target's radiation signal instead of radiate signals to the outside,and has the characteristics of strong concealment and anti-interference ability.Multi-station passive location system is to acquire the position information of the target by setting up multiple observation stations,and to realize the fusion of observation information by centralized data processing or distributed data processing,so as to realize the target location.It has the significant advantages of the invisibility and the stability,and is the key technology of electronic reconnaissance,homeland defense,navigation and guidance,location and detection,and it is also used in the fields of sea,land and air rescue and aircraft interception.The key problem to realize moving target location and tracking is to research for filtering and tracking algorithm.However,because of the influence of target maneuver,environmental factors and equipment factors in the actual location process,it will cause uncertain problems such as model uncertainty and random loss of measurement data.If these problems are ignored,the positioning accuracy will decrease.Therefore,based on the multi-station passive location system,this article studies the distributed passive location method under uncertain conditions for uncertain problems such as model uncertainty and random loss of measurement data.The main contents are as follows:(1)In order to establish a multi-station passive location and tracking system,the models and algorithms involved are studied,and a multi-station passive location and tracking model based on Angle Observation and a multi-target motion model are established,several filter tracking algorithms are introduced in detail,and the performance index of the following simulation experiment is determined.(2)Research the filtering algorithm of the multi-station passive location and tracking problem,aiming at the problem that the filtering precision and convergence speed of the distributed Kalman filter algorithm are lower than that of the centralized kalman filter algorithm,combining the advantages of information filtering and distributed processing,an improved distributed extended information filtering algorithm is proposed has better positioning accuracy and need less time to convergence by adding more prior information into the common step of prediction information.The simulation results show that the performance of the distributed extended information filtering algorithm is basically the same as that of the centralized extended information filtering algorithm when the system model is matched and without measurement data loss,it takes only a short time for the algorithm to reach the state of convergence and the error is smaller after the convergence.(3)Research the problem of distributed passive location and tracking under the condition of random loss of measurement data,establish a random model of measurement data loss,and derive the Kalman filter algorithm in the case of random loss of measurement data for incomplete measurement systems,Combining the non-linear characteristics of the positioning and tracking process and extending to multiple observing stations,the measurement data is lost immediately,and a distributed extended information filtering algorithm suitable for the problem of measurement data loss is proposed.The simulation experiment result shows that the tracking performance of the algorithm is good in the presence of measurement data loss,and achieves fast and accurate locating and tracking of the target in the case of missing measurement data of some observation stations,as the arrival rate of the measured data decreases,the rate of convergence decreases slightly,but it can still reach the state of convergence eventually.