  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
