Particle swarm optimization for GPS navigation Kalman filter adaptation

Purpose – The purpose of this paper is to conduct the particle swarm optimization (PSO)-assisted adaptive Kalman filter (AKF) for global positioning systems (GPS) navigation processing. Performance evaluation for the PSO-assisted Kalman filter (KF) as compared to the conventional KF is provided. Design/methodology/approach – The position-velocity also knows as constant velocity process model can be applied to the GPS KF adequately when navigating a vehicle with constant speed. However, when an abrupt acceleration motion occurs, the filtering solution becomes very poor or even diverges. To avoid the limitation of the KF, the PSO can be incorporated into the filtering mechanism as dynamic model corrector. The PSO is utilized as the noise-adaptive mechanism to tune the covariance matrix of process noise and overcome the deficiency of KF. In other words, PSO-assisted KF approach is employed for tuning the covariance of the GPS KF so as to reduce the estimation error during substantial maneuvering. Findings – The paper provides an alternative approach for designing an AKF and provides an example in the application to GPS. Practical implications – The proposed scheme enhances the improvement in estimation accuracy. Application of the PSO to the GPS navigation filter design is discussed. The method takes advantage of both the adaptation capability and the robustness of numerical stability. Originality/value – The PSO are employed for assisting the AKF. The use of optimization such as PSO for AKF has seldom been seen in the open literature.