基于差分演化算法的自适应无迹卡尔曼滤波
doi: 10.3724/SP.J.1146.2012.00912
Adaptive Unscented Kalman Filter Based on Differential Evolution Algorithm
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摘要: 该文在分析无迹变换缩放参数选择方法的基础上，通过对几种缩放参数选择方法的对比分析后，确定以缩放参数选择作为优化目标，将差分演化算法(Differential Evolution, DE)应用到无迹卡尔曼滤波(Unscented Kalman Filter, UKF)计算中，选择每时刻滤波误差最小的缩放参数。提出了基于差分演化算法的自适应无迹卡尔曼滤波算法。通过实验表明，这种自适应策略不仅能够有效提高UKF的精度，避免使用固定缩放参数时可能造成的滤波随机发散；而且不受缩放参数个数限制，可以应用到任意形式的UKF中。Abstract: This paper discusses choice for scaling parameter of the unscented transformation. By analyzing and comparing some scaling parameter selection methods, the scaling parameter is selected as an optimization objective. Differential Evolution (DE) algorithm is applied to the Unscented Kalman Filter (UKF), the optimized scaling parameter leads to the minimum error at each time interval. An adaptive UKF based on DE is proposed. The experiments show that the accuracy of UKF is significantly improved by the adaptive strategy which not only to avoid random divergence with the constant parameter but also suitable for any form of UKF without the constraints of the number of parameters.
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