A Genetic Algorithm-Based Iterative Channel Estimation Method for Ranging Applications in Closely Spaced Multipath Environment

Radio-based positioning using terrestrial radio signal, such as 5G technology has attracted attention in past years, which strongly depends on the accuracy of the channel path parameter estimation. In real wireless propagation environment, the transmitted signal undergoes complex propagation phenomenona, including reflection, diffraction, and scattering. The multipath propagation, particularly in the dense multipath environment, significantly impacts the ranging accuracy. Traditional maximum likelihood methods, such as the space-alternating generalized expectation-maximization (SAGE), suffer from the closely spaced multipath. In this letter, we propose a genetic algorithm-based channel parameter estimation method for closely spaced multipath environment. The paths' parameters are first encoded into binary code space in which the possible solutions are searched toward the optimum. Both simulation- and measurement-based results reveal that the delay estimation performance of the proposed method is superior to that of the SAGE algorithm when paths are closely spaced.