数据丢包的参数不确定下无人机滚动时域估计

针对网络中通信链路中断及系统参数不确定现象,研究了数据包丢失的参数不确定无人机系统状态估计问题,基于滚动时域估计理论和随机最小二乘理论,提出了一种分布式滚动时域估计算法。对于数据包丢失和参数不确定问题,采用已知概率的马尔可夫序列和系统矩阵扰动噪声进行建模。仿真结果表明,该算法的估计效果优于一种新的卡尔曼滤波算法。最后,研究分析了系统压缩量、数据包接收概率和时窗长度对所提算法估计性能的影响。在系统不确定性和丢包概率未知的情况下,适当增加时窗长度可以提高算法估计性能。

Moving horizon estimation of UAV with random parameter ncertainty and data missing

The state estimation problem of unmanned aerial vehicle (UAV) system with random parameter uncertainty and data packet missing under the phenomenon of network communication link failure and system parameters uncertainty is studied. Based on moving horizon estimation theory and random least squares theory, a recursive distributed moving horizon estimation algorithm is proposed. For parameter uncertainty and data packet missing problems, the model adopts system matrix disturbance noise and Markov random series based on given probability. The simulation results show that the proposed algorithm is better than a new Kalman filtering algorithm. Finally, the effects of system compression, data packet missing probability and time window length on the estimated performance of the proposed algorithm are analyzed. In the case where the system uncertainty and the data packet missing probability are unknown, an appropriate increase in the window length can improve the estimation performance of the algorithm.