Whale Optimization Algorithm-Based Channel Parameter Estimation for Ranging Applications in Closely Spaced Multipath Environment

Range-based positioning using wireless signal has gained remarkable attention. Accurate estimation of channel parameters including delay and complex amplitude is essential for positioning applications. However, multipath propagation of wireless signal significantly impacts the performance of channel estimation, particularly the estimation accuracy degrades in closely spaced multipath environment. In this communication, we propose a whale optimization algorithm (WOA)-based channel estimator to extract channel parameter in closely spaced multipath environment. We derive the fitness function based on the maximum likelihood criterion. Both the simulation and measurement-based validation for closely spaced multipath environment reveal that the proposed method outperforms several state-of-the-art methods including as space-alternating generalized expectation–maximization (SAGE), gray wolf optimizer (GWO), and salp swarm algorithm (SSA). The results conclusively demonstrate that the WOA is well-suited for dense multipath parameter estimation.