This paper proposes a comprehensive strategy for complex multi-target-multi-drone encirclement in an obstacle-rich and GPS-denied environment, motivated by practical scenarios such as pursuing vehicles or humans in urban canyons. The drones have omnidirectional range sensors that can robustly detect ground targets and obtain noisy relative distances. After each drone task is assigned, a novel distance-based target state estimator (DTSE) is proposed by estimating the measurement output noise variance and utilizing the Kalman filter. By integrating anti-synchronization techniques and pseudo-force functions, an acceleration controller enables two tasking drones to cooperatively encircle a target from opposing positions while navigating obstacles. The algorithms effectiveness for the discrete-time double-integrator system is established theoretically, particularly regarding observability. Moreover, the versatility of the algorithm is showcased in aerial-to-ground scenarios, supported by compelling simulation results. Experimental validation demonstrates the effectiveness of the proposed approach.

翻译：本文针对障碍物密集且GPS拒止环境中的复杂多目标-多无人机合围问题，提出了一套综合性策略，其应用背景包括在城市峡谷中追踪车辆或人员等实际场景。无人机配备全向距离传感器，能够鲁棒地探测地面目标并获取含噪声的相对距离。在完成各无人机任务分配后，本文通过估计测量输出噪声方差并利用卡尔曼滤波器，提出了一种新型的基于距离的目标状态估计器（DTSE）。通过融合反同步技术与伪力函数，设计的加速度控制器使得两架执行任务的无人机能够在规避障碍的同时，从相对位置协同合围目标。本文从理论上证明了该算法在离散时间双积分器系统中的有效性，特别是在可观测性方面。此外，通过令人信服的仿真结果支撑，该算法在空对地场景中的普适性得到了充分展示。实验验证进一步证明了所提方法的有效性。