This paper presents a robust and secure framework for achieving accurate and reliable mutual localization in multiple unmanned aerial vehicle (UAV) systems. Challenges of accurate localization and security threats are addressed and corresponding solutions are brought forth and accessed in our paper with numerical simulations. The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detectio (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in the mutual localization system, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying UAVs with malicious data, enhancing the security and resilience of the mutual localization

翻译：本文提出了一种鲁棒且安全的框架，用于在多无人机系统中实现精确可靠的相互定位。针对精确定位与安全威胁的挑战，我们通过数值仿真提出了相应的解决方案并进行了评估。该方案包含两个核心组件：移动自适应梯度下降算法（MAGD）与时变异常检测算法（TAD）。MAGD通过自适应调整梯度下降算法以应对相互定位系统中的构型变化，确保动态场景下的定位精度；TAD则与信誉传播（RP）方案协同工作，通过识别携带恶意数据的无人机来检测并缓解潜在攻击，从而增强相互定位的安全性与鲁棒性。