摘要
针对多目标情况下雷达组网的误差配准问题,提出了一种基于不敏卡尔曼滤波(Unscented Kalman Filter,UKF)和最优压缩的系统偏差稳健估计方法。该算法将目标的运动状态和传感器系统偏差组合在同一状态方程中,构建扩维的系统偏差动态方程,接着采用UKF的方法对目标状态和系统偏差进行联合估计。然后通过对多个估计结果的进一步融合,最终得到较高精度的系统偏差估计。仿真结果表明,该算法可以有效地实现多目标情况下的误差配准。
To improve the system errors estimation for radar network in multi-target, a novel robust estimation method of sensor bias based on Unscented Kalman Fiher（UKF） and optimum compression is proposed.Firstly,a dynamic equation is built up for target by incorporating target states and sensor bias into one state equation,and UKF is used to estimate target states and sensor bias simultaneously. Then,the results of UKF are processed by optimum compression to get the systematic bias of the higher precision. Simulation results show that errors registration in multi-target can be finished more effectively.
出处
《电子设计工程》
2012年第19期106-109,共4页
Electronic Design Engineering
基金
国家自然科学基金项目(61032001)
航空科学基金(20085184003)
关键词
误差配准
不敏卡尔曼滤波
最优压缩
稳健估计
errors registration
Unscented Kalman filter
optimum compression
robust estimation
