摘要
针对不敏卡尔曼滤波算法在单站无源定位的应用中受初始状态误差和可观测条件等影响易产生滤波发散、收敛精度低、收敛速度慢的问题.提出一种双向平方根不敏卡尔曼滤波的无源定位算法.充分利用了平方根不敏卡尔曼滤波算法数值稳定性高的优点,采用后向平滑算法逐次修正状态估计值,从而提高了定位算法对初始状态的鲁棒性.试验结果验证了该算法的有效性.
As the Unscented Kalman Filter（UKF） is sensitive to initial value and system observation, to enhance the robustness, increase the convergence speed and improve the locating accuracy of the unscented Kalman filter in the single observer passive Location, this study presented an improved forward-backward smoothing algorithm based on Square-Root Unscented Kalman Filter （SRUKF）. To guarantee the stability of the filter algorithm, the square root of covariance instead of itself was used in the step estimation, and a more accurate state estimate as an initial condition can be obtained by backward smoothing to improve the robustness to the initial value. Simulation results show that the algorithm has better performance to UKF and SRUKF in the filter＇s stability, convergence velocity, positioning precision and the robustness to the initial value.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2014年第2期267-271,共5页
Journal of Liaoning Technical University (Natural Science)
关键词
无源定位
不敏卡尔曼滤波
后向平滑
非线性滤波
状态估计
平方根
扩展卡尔曼滤波
稳定性
passive location
Unscented Kalman Filter（UKF）
backward-smoothing
nonlinear filtering
stateestimation square-root
extend Falman filter
robustness
