摘要
对于纯方位目标跟踪问题 ,在利用卡尔曼滤波算法进行处理时 ,首先要进行观测方程及状态方程的线性化处理 ,自然导致线性化误差 ,为减少它对目标跟踪的影响 ,该文利用衰减记忆的卡尔曼滤波算法 ,通过蒙特卡罗模拟仿真实验表明其跟踪效果在收敛速度和收敛率以及稳定性等方面有了较大的提高。
The utilization of extended Kalman filters in cartesian for bearings-only tracking introduces errors when the observed equation and state equation are linearized. It thus causes the estimates to deviate from the real parameters while the filter works for a long time.In this paper,a modified Kalman filter with an attenuation memory is proposed.The Monte Carlo simulation experiments demonstrate that this filter has improved the convergence of estimated states and its stability.
基金
国家自然科学基金课题!(6 95 72 0 15 )资助
