Improved PSO- Based Robust Location Algorithm

In view of the poor positioning accuracy and lack of robustness to environmental noise of the traditional algorithm, an improved particle swarm optimization-based location algorithm is proposed. Considering the problem of slow rate of convergence and premature convergence in traditional PSO algorithm, the particle population is optimized by giving consideration to the diversity of particle and the rate of convergence. First the particle population is optimized through grouping location, and then standard PSO is adopted iteratively to locate the mobile station. Simulation results show that the proposed algorithm, compared with the traditional algorithm, not only converges faster and requires less number of particles, but also has a high accuracy and improves robustness to noise when NLOS error occurs.