虽然无人机应用科学近年来发展迅速，但是无人机在搜索救援方面相关应用的智能化水平依然难以满足需求。因此，迫切需要一种能够快速实现多目标位置精确、快速确定的智能无人机搜救方法。本项目以如何由无人机机载传感器目标方向角量测中精确提取多目标位置信息为研究问题，在量测噪声抑制、多目标位置实时估计、估计精度快速提升及智能性方面进行深入理论研究。具体内容包括：（1）基于机器学习算法的复杂量测噪声抑制研究，探究相应目标估计理论；（2）分析多目标估计中的数据关联问题，开展基于学习策略的无人机多目标数据关联抑制研究；（3）探究无人机-目标相对位置关系与目标定位精度、数据关联之间的理论联系，构建无人机路径优化代价函数，提出路径优化方法，进一步改进定位速度及精度；（4）最后进行相应计算机仿真实验验证。本项目的完成，将提供一种无需地面遥控的无人机多目标快速精确定位方法，为无人机自主搜索救援的应用提供理论基础。

Unmanned aerial vehicles (UAVs) have been widely used in different areas and the related UAV technologies have been developed rapidly in recent years. However, in the search and rescue (SAR) missions, UAVs are not intelligent enough to satisfy the practical requirements. Therefore, it is urgently necessary to develop a fast, accurate and intelligent UAV multi-target localization strategy for SAR missions. In this strategy, the UAV has the intelligent self-decision making abilities of target accurate estimation without any help from a remote human controller. This project focuses on multi-target localization problem using UAV-equipped sensors' target angle-of-arrival (AOA) measurements. The measurements include the processed target angle-of-arrival (AOA) information. Four stages are contained in this project, i.e., measurement noise mitigation, multi-target estimation algorithm, estimation accuracy rapidly promotion and intelligence improvement of the UAV target localization system. First, a measurement noise mitigation algorithm using machine learning methods will be developed to acquire the accurate targets’ positions. Second, the data association problem in multi-target localization is analyzed and a machine learning method will be applied to eliminate the negative impacts caused by data association. Also, the related multi-target estimation algorithms for UAV SAR missions will be proposed. Third, the theoretical relationship between the UAV-target geometry and data association problem, and the mathematical relationship between the UAV-target geometry and estimation accuracy will be found, respectively. Then, a UAV flying path optimization cost function will be designed considering both the data association and estimation accuracy. The appropriate optimization algorithms will be applied to minimize the path optimization cost function. Thus, UAV will be navigated by the results of the flying path self-optimization strategy to improve the localization performance. Finally, the effectiveness and characteristics of the proposed algorithms will be demonstrated and discussed with simulation examples. This project will develop an intelligent UAV multi-target fast and accurate localization method in which the UAV remote human controller is unnecessary. The outcomes will provide a theoretical basis and make a contribution to the practical intelligent UAV SAR applications.