基于协方差加权的卡尔曼滤波融合跟踪算法

针对卡尔曼滤波融合跟踪对系统模型准确度和先验信息精度要求较高的问题，提出一种基于协方差加权的卡尔曼滤波融合方法，利用最小二乘准则作为误差加权的标准，使误差小的传感器加权因子大。基于此，再利用卡尔曼滤波融合，充分保留有用信息，抑制噪声干扰。在目标跟踪应用中，即使噪声统计信息未知且噪声互相关，利用该方法仍能够获得最小均方误差准则下的最优目标状态跟踪估计。

Tracking Algorithm Based on Kalman Filter Fusion in Weighted Covariance

The tracking based on Kalman filter fusion requires accurate system model and exact apriori information. Therefore, a novel method based on Kalman filter fusion in weighted covariance is proposed, which can increase the weighting factor of the sensor with less error according to the criterion of least squares. The Kalman filter fusion method can retain effectively valuable information and suppress noise. In such application as target tracking, even if the statistic information of noise is unknown, but correlative, optimal state estimation for target is still carried out by the proposed method in the criterion of least squares.