Mitigating NLOS Error in Urban and Suburban Environment Using Enhanced CSA-PSO Algorithm

Non-Line Of Sight (NLOS) propagation introduces errors to the time-of-arrival (TOA) measurements and degrades the localization accuracy. This paper focuses on mitigating NLOS errors and improving the localization accuracy of a TOA localization system where the unknown position of a Mobile Station (MS) is estimated based on the known positions of several Base Stations (BS). We formulate the problem into a Maximum Likelihood (ML) framework and propose an enhanced Particle Swarm Optimization (PSO) algorithm where the Crow Search Algorithm (CSA) is employed to guide the initial population generation for PSO. Simulation results show that our proposed method achieves higher localization accuracy in urban and suburban environments with different NLOS conditions than the state-of-the-arts (SOTA) PSO-based method.