Convex Hull Optimization for Robust Localization in ISAC Systems

Received signal strength (RSS)-based localization has gained popula-rity in integrated sensing and communication (ISAC) systems due to its affordability and simplicity. However, in real-world scenarios involving unmanned aerial vehicles (UAVs), RSS measurements can be influenced by biases, introducing challenges to localization accuracy. Biases, in this context, represent systematic errors in the RSS measurements stemming from factors, such as design issues or environmental conditions. These biases significantly impact the precision of UAV localization, a critical aspect often overlooked by existing methods designed for bias-free scenarios. To address this challenge, we propose a novel convex Hull-based optimization method for RSS localization in the presence of UAV biases. Our approach involves constructing a convex hull that encapsulates the unknown location and UAV biases, effectively transforming the original nonconvex estimation problem into a convex-constrained least squares problem. This letter comprehensively explores the causes and implications of biases in RSS measurements, highlighting their potential to degrade localization accuracy. Through extensive numerical examples, we showcase the superior performance of our proposed method compared to state-of-the-art approaches, emphasizing its effectiveness in mitigating the impact of biases on ISAC system localization.