Research On Adaptive Target Tracking Based Oncovariance Matrix

Target tracking technology is one of the research hotpots in the data processing of radar. Kalman filter is a frequently-used tracking filter at present. This paper focuses on the research of energy control method for radar tracking. The main work of the paper is the use of more optimal Quadrature Kalman Filter and Cubature Kalman filter in basic research on several filtering algorithms, combined with the interacting multiple model to design the adaptive target tracking algorithm for controlling the radar radiation energy. In other words, increasing the sampling interval, thereby reducing the radar radiation energy on the premise of not changing the radar tracking accuracy, which is good for tracking more targets and improving the efficiency. The main research contents are as follows:1. The development of target tracking technology is analyzed. And the research significance of adaptive sampling energy control in the process of target tracking is also analyzed. At the same time, the current research status of nonlinear filter tracking algorithm and adaptive tracking control of energy is studied.2. Adaptive design of sampling interval of the tracking process can reduce the radar’s radiation times. Desired tracking accuracy is set for every target, and then the algorithm selects the largest sampling interval from the interval sequence. In order to expand the choices of the sampling interval, a novel adaptive sampling interval(ASI) algorithm for multi-target tracking is presented by incorporating the grey relational grade(GRG) into the particle swarm optimization(PSO). First of all,we elaborated some basic theory of target tracking algorithm, including target motion model,several filtering algorithm,interacting multiple modelalgorithm(IMM),grey relational grade,and particle swarm optimization.3. This paper presents an adaptive sampling interval interacting multiple model Quadrature Kalman filtering algorithm. Then we introduced the target covariance matrix estimation,and then tracking algorithm of the adaptive sampling interval design, optimization algorithm based on grey relational grade intothe particle swarm optimization, combined Quadrature Kalman filter algorithm,simulations show that our method improves both the tracking accuracy and tracking efficiency of the phased array radar.4. In this paper, we propose an adaptive sampling interval interacting multiple model Cuadrature Kalman filtering algorithm.It’s based on the adaptive sampling interval interacting multiple model Quadrature Kalman filtering algorithm.First introduced Cubature Kalman filter algorithm, and then the third-degree spherical- Radial guidelines, based on the Kalman filter algorithm to achieve higher degreecubature rules. Finally, the simulation results show that the method of filtering algorithms is able to optimize the sampling interval to reduce the number of radar illumination than before.