Dogancay Kutluyil, Hmam Hatem
UniSA STEM, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia.
Defence Science & Technology Group, Sensors and Effectors Division, Edinburgh, SA 5111, Australia.
Sensors (Basel). 2024 May 14;24(10):3120. doi: 10.3390/s24103120.
In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples.
在本文中，针对无人机（UAV）的自定位和目标跟踪，开发了新的路径优化算法，该算法利用了来自机动目标的信标（地标）方位和到达角（AOA）测量值。为了考虑无人机局部坐标相对于全局笛卡尔坐标系的时变旋转，无人机的未知方位角也与其从信标方位确定的位置一起进行估计。这至关重要，因为方位误差会显著降低自定位性能。联合自定位和目标跟踪问题被表述为一个卡尔曼滤波问题，其增广状态向量包含所有未知参数，测量向量包括信标方位和目标AOA测量值。这种表述涵盖了基于全球导航卫星系统（GNSS）的自定位不可用或不可靠，且只能利用信标或地标进行无人机自定位的应用场景。通过A-最优性准则和D-最优性准则对贝叶斯费希尔信息矩阵进行优化，从而确定最优的无人机路径。研究了该方法在不同测量噪声水平下的性能。还提出了一种基于先前工作的改进型闭式投影算法，以实现最优的无人机路径。通过大量的仿真示例展示了所开发的无人机路径优化算法的性能。
