Abstract
The uncertainty, complexity, and variability of the marine environment inevitably lead to a change in the measurement error resulting in erroneous estimation of navigation information. To solve this problem, this paper proposes a novel method integrating the square-root cubature Kalman filter (SCKF) with the expectation-maximization (EM) algorithm. The proposed new SCKF (NSCKF) algorithm makes better use of the advantages of SCKF and the EM online algorithm. The performance of NSCKF is verified theoretically and evaluated by experiments. The results indicate that the proposed NSCKF algorithm can better estimate predicted error covariance and measurement noise than two other comparison methods owing to the online EM method so that the more accurate attitude estimation can be obtained by the NSCKF algorithm although the measurement error has a great variation. Moreover, the accuracy and efficiency can be guaranteed by employing the SCKF. Experimental results demonstrate that the NSCKF can provide a more stable attitude estimation in different cases of measurement errors. Therefore, the NSCKF is more suitable to be used in underwater navigation than other comparison methods because of higher accuracy, more efficiency, and better robustness.
Highlights
Because of long endurance and reliability applied to different marine environments, underwater gliders have obvious advantages over other underwater vehicles with propellers in ocean applications
The results indicate that the proposed new SCKF (NSCKF) algorithm can better estimate predicted error covariance and measurement noise than two other comparison methods owing to the online EM method so that the more accurate attitude estimation can be obtained by the NSCKF algorithm the measurement error has a great variation
This system consists of a digital signal processing (DSP) unit and inertial measurement unit (IMU), which mostly involves triaxis Micro-Electro-Mechanical System (MEMS) angular rate sensors, triaxis MEMS acceleration sensors, and triaxis magnetic sensors
Summary
Because of long endurance and reliability applied to different marine environments, underwater gliders have obvious advantages over other underwater vehicles with propellers in ocean applications. The idea is motivated by the fact that models with higher order are able to offer the more precise dynamics description of inertial sensor error This is hindered by two drawbacks: (a) the computation consumes too much time because of the complex mathematical derivation and (b) the mathematical derivation may involve a few terms, and inevitably, the influence or significance of some terms is fairly low in the nonlinear model. In the process of underwater gliding, the precise measurement noise is very hard to be estimated and may be time-varying because the performance of sensors varies with the change in the environment.. The advantages of the proposed method include (a) This method can adaptively capture an unknown and timevarying noise covariance matrix, and this method can perform the optimal attitude estimation in the harsh and complex underwater environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
