Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index
Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index
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DOI：
10.1109/access.2015.2486766
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发表时间：
2015-01-01
期刊：
影响因子：
3.9
通讯作者：
Masugi, Masao
中科院分区：
文献类型：
--
作者：
Saho, Kenshi;Masugi, Masao
提出了一种基于稳态性能指标的二阶卡尔曼滤波跟踪器参数自动整定方法。首先，我们提出了一个有效的稳态性能指标，该指标对应于跟踪中的均方根（rms）预测误差。然后，我们推导出建议的性能指标和过程噪声的广义误差协方差矩阵之间的解析关系，其中使用导出的关系的自动确定。与传统的过程噪声经验模型相比，该方法计算的模型具有更高的精度。数值分析和仿真验证了该方法对加速运动目标的有效性。对10 m/s（2）的目标加速度，用该方法设计的跟踪器的均方根预测误差为传统经验选择模型的63.8%。
We present an automatic parameter setting method to achieve an accurate second-order Kalman filter tracker based on a steady-state performance index. First, we propose an efficient steady-state performance index that corresponds to the root-mean-square (rms) prediction error in tracking. We then derive an analytical relationship between the proposed performance index and the generalized error covariance matrix of the process noise, for which the automatic determination using the derived relationship is presented. The model calculated by the proposed method achieves better accuracy than the conventional empirical model of process noise. Numerical analysis and simulations demonstrate the effectiveness of the proposed method for targets with accelerating motion. The rms prediction error of the tracker designed by the proposed method is 63.8% of that with the conventional empirically selected model for a target accelerating at 10 m/s(2).