Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation

Abstract

Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber’s equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.

An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

“…The Sage-Husa filter adaptively estimates system process noise and measurement noise online, including the mean vector q k , the covariance matrix Q k of the process noise, the mean vector r k , and the covariance matrix R k of the measurement noise. The estimators of the Sage-Husa adaptive filtering algorithm can be expressed as follows [6,15]:…”

“…However, in practical applications, due to the low precision of low-cost inertial/DR equipment carried on an AUV, these methods are easily affected by many uncertain factors such as external environment interference, carrier maneuver changes, internal instrument failure, and so on. At present, in order to overcome the uncertainty of the filter in AUV navigation and localization, many researchers have applied adaptive interactive multi-models, fuzzy logic, and other methods [5][6][7][8][9][10]. In Refs.…”

“…In Refs. [5,6], the adaptive filtering algorithm was applied to an AUV. When the system noise matrix remained positive or semi positive, the latest measurement data were used to adaptively estimate the changing process noise or measurement noise covariance matrix.…”

Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

“…Sage-Husa algorithm is an alternative to the ML when there are not enough data available. However, it cannot estimate the PNCM and MNCM simultaneously without causing divergence in the estimations [ 21 ].…”

“…The SH algorithm is also used with modifications in [ 22 ] to estimate the MNCM of an UKF for state estimation in a vehicle. A similar approach is followed in [ 21 ], where a robust UKF is complemented with the SH algorithm for estimating the PNCM for autonomous underwater vehicle acoustic navigation.…”