Kalman Filtering for Interval System Using Evolutionary Programming
基于进化规划的区间系统卡尔曼滤波

This paper develops a robust Kalman filtering algorithm by incorporating with the evolutionary programming (EP) technique for interval systems containing uncertainties. Based on the global optima_searching capability of EP, the new filtering algorithm is able to find the optimal Kalman filtering results at every iteration. The upper and lower boundaries and the nominal trajectory of the optimal estimates of the system state vectors are computed by the new algorithm, under the same statistical conditions while yielding the same optimal estimates as the conventional Kalman filtering scheme. A typical computer simulation example is included for comparison with the interval Kalman filtering method, which shows that the new algorithm is more accurate and less conservative.