Study on GPS attitude determination system aided INS using adaptive Kalman filter

Abstract

A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.

Windowing-based random weighting fitting of systematic model errors for dynamic vehicle navigation

“…This method can be divided into two categories: the adaptive stochastic modeling and adaptive fading filter [18,30]. The adaptive stochastic modelling transforms state disturbances within a particular time period into the state disturbance at the current time point for system state estimation [3,7,24,33,34,38,41]. However, this method requires that residual vectors or predicted residual vectors at each time point be in the identical type, spatial dimension and distribution, which is difficult to achieve in a highly dynamic environment [18,30].…”

Innovation-based adaptive Kalman filter derivation

“…USING THE INNOVATION SEQUENCE FOR COVARIANCE ADAPTATION The use of k y % for k Q and k R adaptation is based upon assumption that innovations at different moments are uncorrelated so k y % cannot be predicted from 1 k − y % and therefore each observation brings new information on covariance of the process and measurement noise. Hence, the innovation sequence represents the information content in the new observation and is considered to be the most relevant source of information for the filter adaptation [2]. Perfect prior knowledge of covariance as well as covariance change is of secondary importance in this case because of its permanent tracking on the basis of k y % .…”

“…Using maximum-likelihood philosophy as in [2] and assuming Gaussian distribution for k y % we can get the following reasoning for Mk C % and (10), (11). Let k C % is unknown covariance of k y % with probability density…”

Study on FOG-SINS/GPS Integrated Attitude Determination System Using Adaptive Kalman Filter

“…Bian. et al [9] summarized and analyzed these methods for GPS/INS integrated system, then proposed a novel IAE-AKF based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The IAE-FLC (fuzzy logic control) methods decrease the computation time of the algorithm remarkably without increasing the system state dimension.…”

“…Bian. et al [9] theoretically deduced the proposed IAE-AKF algorithm in detail; the approach was tested in the developed INS/GPS integrated marine navigation system. The approach is direct without having to establish fuzzy inference rules compared with the IAE-FLC methods, but this method is necessary to test for the GPS/SINS integrated system in high dynamic environment.…”