ADAPTIVE UPDATE RATE FOR PHASED ARRAY RADAR BASED ON IMMK-PF  

作　　者：Zhang Jindong Wang Haiqing Zhu Xiaohua 

机构地区：[1]Department of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

出　　处：《Journal of Electronics(China)》2010年第3期371-376,共6页电子科学学刊（英文版）

摘　　要：Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.Interacting Multiple Model Kalman-Particle Filter （IMMK-PF） has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter （IMMPF）. Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar （PAR） is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix （PCRBM） of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.

关 键 词：Phased Array Radar (PAR) Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) Posterior Cramer-Rao Bound Matrix (PCRBM) Adaptive sampling 

分 类 号：TN958.92[电子电信—信号与信息处理]