Adaptive Beamforming Algorithm Based on a Simulated Kalman Filter
Kelvin Lazarus, Nurul Hazlina Noordin, Mohd Falfazli Mat Jusof and Zuwairie Ibrahim (Universiti Malaysia Pahang, Malaysia); Khairul Hamimah Abas (Universiti Teknologi Malaysia & University Technology Malaysia, Malaysia)
A new population-based metaheuristic optimization algorithm named Simulated Kalman Filter (SKF) is proposed as an adaptive beamforming algorithm for adaptive array antenna. SKF optimization algorithm is inspired by the estimation capabilities of Kalman Filter. Each agent in the population of SKF acts as one Kalman Filter where it finds the solution using standard Kalman Filter framework. SKF consists of simulated measurement process and a best-so-far solution as a reference. SKF estimates the weights of individual elements in an array which maximizes the signal to interference plus noise ratio (SINR). SKF is also compared with Adaptive Mutated Boolean Particle Swarm Optimization (AMBPSO) from existing work and is proven to be better.
Journal: International journal of simulation: systems, science & technology V18
Published: Dec 30, 2017