Hybrid TOA/AOA Indoor Positioning Based on Sparse Reconstruction and Map Matching

Indoor positioning technology, as a crucial foundation of location-based services, is experiencing a growing need for high precision driven by the Internet of Things (IoT). However, traditional positioning algorithms suffer from low sample utilization and susceptibility to noise. Moreover, the presence of indoor obstacles significantly affects positioning accuracy and leads to the issue of wall-penetrating positioning. To address these problems, this paper proposes a hybrid time-of-arrival/angle-of-arrival (TOA/AOA) indoor positioning algorithm based on sparse reconstruction and particle filtering-based map matching. Specifically, sparse reconstruction is employed to improve the utilization of samples, and iterative updating of the position estimation is performed during the multi-sample joint estimation process to enhance accuracy. Furthermore, to tackle the problem of wall-penetrating positioning, a particle filtering-based map matching algorithm is proposed to detect and eliminate the wall-penetrating particles using the map information matrix, which optimizes the positioning results obtained from sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed algorithm in satisfying the demand for high-precision indoor positioning.