Control Engineering Practice 12 (2004) 1531–1539 Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system D. Loebis*, R. Sutton, J. Chudley, W. Naeem Marine and Industrial Dynamic Analysis Research Group, School of Engineering, The University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK Received 17 August 2003; accepted 27 November 2003 Abstract This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University. r 2004 Elsevier Ltd. All rights reserved. Keywords: Autonomous underwater vehicles; Navigation; Sensor fusion; Kalman filters; Extended Kalman filters; Fuzzy logic 1. Introduction The development of autonomous underwater vehicles (AUVs) for scientific, military and commercial purposes in applications such as ocean surveying (St^rkersen, Kristensen, Indreeide, Seim, & Glancy, 1998), unex- ploded ordnance hunting (Wright et al., 1996) and cable tracking and inspection (Asakawa, Kojima, Kato, Matsumoto, & Kato, 2000) requires the corresponding development of navigation systems. Such systems are necessary to provide knowledge of vehicle position and attitude. The need for accuracy in such systems is paramount: erroneous position and attitude data can lead to a meaningless interpretation of the collected data or even to a catastrophic failure of an AUV. A growing number of research groups around the world are developing integrated navigation systems utilising inertial navigation system (INS) and global positioning system (GPS) (Gade & Jalving, 1999; Grenon, An, Smith, & Healey, 2001; Yun et al., 1999). However, few of these works make explicit the essential need for fusion of several INS sensors that enable the users to maintain the accuracy or even to prevent a complete failure of this part of the navigation system, before being integrated with the GPS. Kinsey and Whitcomb (2003), e.g. use a switching mechanism to prevent a complete failure of the INS. Although simple to implement, the approach may not be appropriate to use to maintain a certain level of accuracy. Several estimation methods have been used in the past for multisensor data fusion and integration purpose (Loebis, Sutton, & Chudley, 2002). To this end, simple/ extended Kalman filter (SKF/EKF) and their variants have been popular methods in the past and interest in developing the algorithms has continued to the present day. However, a significant difficulty in designing a SKF/EKF can often be traced to incomplete a priori knowledge of the process covariance matrix ðQÞ and measurement noise covariance matrix ðRÞ: In most practical applications, these matrices are initially esti- mated or even unknown. The problem here is that the optimality of the estimation algorithm in the SKF/EKF ARTICLE IN PRESS *Corresponding author. Tel.: +44-1752232633; fax: +44- 1752232638. E-mail address: d.loebis@plymouth.ac.uk (D. Loebis). 0967-0661/$-see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.conengprac.2003.11.008