🤖 AI Summary
This study addresses the problem of localizing and tracking a two-dimensional linearly moving target using only distance measurements. The work proposes a GPS-free, angle- and multi-anchor-independent approach that employs a single stationary receiver and a mobile agent. Distance information is derived from a path-loss model, and target localization is achieved through quadrant identification, iterative position estimation, and motion vector inference, requiring at most thirteen steps before autonomously transitioning to the tracking phase. Simulation results demonstrate that the method effectively captures the target and maintains bounded tracking error, significantly reducing reliance on extensive sensing infrastructure.
📝 Abstract
This paper investigates localization and pursuit of a linearly moving target in a two-dimensional plane using distance measurements only. The distance from the target is estimated from pathloss measurements and is assumed to be noise-free. The proposed system consists of a receiver that is stationed at the origin and a mobile agent. The system does not rely on GPS, angle sensing, or multiple anchor nodes to localize the target. The proposed method first identifies the target quadrant and estimates its position. Consecutive target position estimates are then used to determine the motion vector and update the agent trajectory during pursuit. We show that a maximum of thirteen steps are required to localize the target and determine its motion vector. Simulation results show that the agent can successfully make a transition from search to pursuit phase, and also maintain a bounded tracking error.