A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.