Hybrid-strategy-enhanced sparrow search algorithm for UWB indoor positioning and navigation

Abstract High-precision and robust indoor positioning systems are in great demand for a variety of mobile computing applications. Ultra-wideband (UWB) technology has gained popularity due to its high accuracy and low power consumption; however, its performance is often compromised in complex environments due to non-line-of-sight (NLOS) errors. On the other hand, optimization-based algorithms such as the sparrow search algorithm (SSA) show potential in refining positioning accuracy, yet they suffer from low convergence speed and high computational complexity in large search spaces. To overcome these limitations, this paper presents an enhanced SSA tailored for UWB-based positioning, with a focus on NLOS error mitigation and computational efficiency. Key improvements include the optimization of the fitness function, the imposition of search region constraints, and enhancements to the algorithm’s search speed and accuracy. Experimental results demonstrate that the proposed algorithm achieves a root mean square error of 4.1 cm in underground garage environments, while the maximum dynamic deviation of vehicles in office environments is confined to 17 cm. These outcomes adequately validate the effectiveness of the proposed algorithm in delivering high-precision positioning.