Search results

1 – 1 of 1
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 3 July 2009

Dah‐Jing Jwo and Shun‐Chieh Chang

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…

1859

Abstract

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.

Details

Aircraft Engineering and Aerospace Technology, vol. 81 no. 4
Type: Research Article
ISSN: 0002-2667

Keywords

1 – 1 of 1
Per page
102050