Mobile Location With Multi-Spot Measurements Model Considering Anchor Node Position Errors

Multi-spot measurements model has emerged as a powerful approach for enhancing positioning accuracy in wireless sensor networks (WSNs), particularly in dynamic environments like autonomous aerial vehicles (AAV) swarms. By leveraging multiple measurements collected over short intervals, this models can significantly improve localization performance compared to traditional single-measurement approaches. However, practical implementation of multi-spot techniques faces challenges when anchor nodes are subject to motion and position errors, which can substantially impact the accuracy of position estimation. This paper addresses this critical gap by deriving a new Cramér-Rao Lower Bound for sensor location estimation in multi-spot scenarios that explicitly accounts for anchor node movement and position uncertainties. Building on this theoretical foundation, we introduce novel closed-form solutions and iterative algorithms tailored for multi-spot measurements with imperfect anchor positions. Additionally, we propose a semi-definite programming (SDP) algorithm formulated on convex optimization principles to further enhance robustness and accuracy. Extensive simulation results validate the efficacy of our proposed methods against the derived CRLB, demonstrating the potential of enhanced multi-spot techniques to significantly improve localization performance in dynamic WSN environments, with particular relevance to AAV swarm applications. This work advances the practical implementation of multi-spot measurements models, paving the way for more accurate and reliable positioning systems in challenging real-world scenarios.