GARUKF算法在无人船对准中的应用

针对无人船在恶劣海况下不仅做大幅摇摆运动,还会遭受海浪的周期性瞬时冲击以及阵发性短时强干扰等影响,提出一种GARUKF算法来提高对准精度,利用投影统计(PS)算法对滑动窗口内的新息向量重新赋权,实现对量测噪声方差阵的实时估计,并利用遗传(GA)算法求解自适应因子阵,最优化地调整量测噪声方差阵。与常规自适应鲁棒UKF算法(ARUKF)算法相比,该算法实现了自适应调节能力的最优化。仿真试验表明,本文提出的GARUKF算法在系统遭受短时强干扰以及瞬间冲击等影响时,水平方向对准精度达4′,方位对准精度可达5′。结合半实物实验结果,验证了该算法可有效提高滤波精度和稳定性,满足恶劣海况下无人船对准的要求。

Application of GARUKF Algorithm in Unmanned Ship Alignment

This paper presentes the GARUKF algorithm to improve the alignment accuracy of unmanned ships under rough sea conditions,which the unmanned ships not only perform large swinging motion,but also suffer from periodic transient impact of the ocean wave and bursting short-term strong interference. In the GARUKF algorithm,the projection statistics(PS)algorithm is applied to re-weight the innovation vector in the sliding window to estimate the measurement noise variance matrix in real time,and the Genetic algorithm(GA)is applied to calculate the adaptive factor matrix to adjust the measurement noise variance matrix optimally. Compared with the conventional adaptive robust UKF algorithm(ARUKF)algorithm,the GARUKF algorithm greatly improves the adaptive adjustment ability. The simulation experiment demonstrates that the horizontal alignment accuracy can reach 4′ and the azimuth alignment accuracy can reach 5′,when the system suffers from short-term strong interference and instantaneous impact. Combined with the results of the semi-physical experiment,it is verified that the proposed algorithm can effectively improve the filtering accuracy and stability satisfy the requirements of unmanned ship alignment under harsh sea conditions.