An improved grey wolf optimization algorithm for 3-D UWB indoor positioning

Traditional Ultra-wideband (UWB) positioning algorithms cannot achieve ideal positioning results when facing multipath effects and non-line-of-sight (NLOS) factors. In order to improve the accuracy of UWB indoor positioning, this paper proposes an improved grey wolf optimization (GWO) algorithm for 3-D UWB indoor positioning in the NLOS environment. First, the Chan algorithm is used for initial tag positioning. Then, the search area of the GWO algorithm is constructed with the initial positioning result as the center. Next, the GWO algorithm’s optimization accuracy and convergence speed are improved through the improvement of Tent chaotic mapping, nonlinear convergence factor based on cosine function, and dynamic inertia weight factor. Finally, the optimized position of the tag is determined using the improved GWO algorithm. Experimental results show that this algorithm can converge to the global optimal solution faster and achieve higher positioning accuracy in complex experimental environments. Compared with the Chan, ChanTaylor, particle swarm optimization (PSO), GWO, and enhanced grey wolf optimization (AGWO) algorithms, the average positioning accuracy of the proposed algorithm is improved by 62.92%, 66.43%, 45.71%, 40.91%, and 37.76% respectively, demonstrating its high practical value.