深圳大学学报（理工版）2011，Vol.28Issue(2)：147-153,7.
基于迭代CDKF的单站无源定位算法
Iterated CDKF for single observer passive location
摘要
Abstract
A new novel iterated central difference Kalman filter algorithm was presented to solve the issues of the filter in the single observer passive location system, such as bad stability, slow convergence speed and poor locating accuracy.In the iterative sentencing guideline constraints, the new algorithm can generate more accurate and reasonable estimation of the state vector and error covariance matrix by reusing the observation information.The Levenberg-Marquardt optimization method is used to adjust the forecast error covariance matrix.This ensures the global convergence of the algorithm.Simulation results show that the new algorithm has stabler performance, larger convergence speed and higher locating accuracy in different scenarios.关键词
单站无源定位/Gauss-Newton方法/Levenberg-Marquardt优化方法/中心差分卡尔曼滤波算法/现代信息战分类
信息技术与安全科学引用本文复制引用
刘学,焦淑红..基于迭代CDKF的单站无源定位算法[J].深圳大学学报（理工版）,2011,28(2):147-153,7.基金项目
国家重点基础研究发展计划资助项目(61393010101-1) （61393010101-1）
