Position estimation based on UWB swarm optimization and comparison against traditional trilateration

Abstract: Ultra-wideband (UWB) is a promising technology for indoor position estimation for various localization applications of object swarms, such as in 3D analysis of human movement with multiple on-body sensors or a swarm of drones in an indoor environment. However, most UWB-only position estimation methods are based on a star topology, where the position of a mobile node is estimated using distances from several fixed anchors. These approaches ignore the valuable inter-node distance estimates, possible in a fully-connected 'Swarm' topology, which could provide more redundancy in the set of available distance estimates used for the position estimation. This would improve the accuracy and consistency of the position estimates. Also, published studies do not analyze how input measurement errors affect the final position estimates, which makes it difficult to assess the reliability under varying conditions. Therefore, this study first proposes a UWB swarm optimization-based position estimation method that utilizes all available internode distances to enhance accuracy and compare against the traditional trilateration method that utilizes the star configuration. All validations were done with synthetic UWB data, to enable testing all input error situations. The comprehensive error sensitivity analysis was conducted to evaluate its robustness under varying noise conditions. The proposed method consistently outperformed trilateration, with position estimation error around 5.7 cm for realistic UWB distance input estimates, while for higher noise conditions, the proposed method had errors around 6 cm lower than the trilateration method, which had position estimation errors around 19 cm. This study demonstrates the general potential of the Swarm optimization-based method for position estimation as a more accurate and consistent alternative to traditional star-based trilateration methods.