McGuire John L, Law Yee Wei, Doğançay Kutluyıl, Ho Sook-Ying, Chahl Javaan
UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia.
Entropy (Basel). 2022 Aug 22;24(8):1169. doi: 10.3390/e24081169.
We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an -step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.
我们考虑最优机动问题，例如自动驾驶车辆（如无人驾驶飞行器（UAV））必须进行机动以最大化或最小化目标函数。我们考虑一辆在全球导航卫星系统（GNSS）信号被阻断的环境中行驶的车辆，该车辆利用来自已知位置的固定信标的到达角（AOA）测量值在二维空间中进行自我定位。车辆的目标是沿着使其位置和航向估计误差最小的路径行驶。本文提出了一种信息路径规划（IPP）算法，该算法：（i）使用无迹卡尔曼滤波器的自我定位估计误差协方差矩阵的行列式作为目标函数；（ii）应用多步前瞻（LSLA）算法来确定匀速车辆的最优航向。该新颖算法考虑了车辆的运动学约束和AOA测量方式。我们在涉及固定和移动信标的五种场景中评估了该算法的性能，并且发现估计误差接近估计器的下限。仿真结果表明，即使信标放置不利于精确估计，车辆也会机动到能够实现最小估计不确定性的位置。
