Secure physical regions (e.g., border areas, nuclear zones, or military facilities) are often patrolled by networked robotic vehicles that require the capability to rapidly verify the advertised location of a potential intruder based on received signal strengths. In this paper, we develop novel algorithms by which mobile robots can coordinate their motions in order to minimize the time required to verify the advertised location for given accuracy bounds. Our specific contributions on this paper are threefold. Firstly, we develop a framework that uses a combination of the particle filters (for position estimation) and the Cramér-Rao lower bounds (for threshold of validation) to drive the motion models for rapid verification of the reported position. We believe our approach is the first in the literature that is accurate, easy to compute, and feasible for practical implementation. Secondly, we propose a centralized coordinated motion algorithm that is optimal at each sampling time. This provides a lower bound on detection time that can be used as a benchmark for practical considerations. Thirdly, we present a practical heuristic approach that allows for distributed protocol based on the concept of the gradient vectors. Subsequently, we also advocate a sub-optimal approach, derived from our heuristic approach, which provides a good trade-off between performance and computational resources. Our results are important for the development of secure access control schemes to prevent unauthorized access of communication networks from malicious users.