Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration

Robust adaptive filtering method for SINS/SAR integrated navigation system

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free.

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Integration of a Multi-Camera Vision System and Strapdown Inertial Navigation System (SDINS) with a Modified Kalman Filter

To solve the unavailability of a traditional strapdown inertial navigation system (SINS) for unmanned underwater vehicles (UUVs) in the polar region, a polar grid navigation algorithm for UUVs is proposed in this paper. Precise navigation is the basis for UUVs to complete missions. The rapid convergence of Earth meridians and the serious polar environment make it difficult to establish the true heading of the UUV at a particular instant. Traditional SINS and traditional representation of position are not suitable in the polar region. Due to the restrictions of the complex underwater conditions in the polar region, a SINS based on the grid frame with the assistance of the OCTANS and the Doppler velocity log (DVL) is chosen for a UUV navigating in the polar region. Data fusion of the integrated navigation system is realized by a modified fuzzy adaptive Kalman filter (MFAKF). By neglecting the negative terms, and using T-S fuzzy logic in the adaptive regulation of the noise covariance, the proposed filter algorithm can improve navigation accuracy. Simulation and experimental results demonstrate that the polar grid navigation algorithm can effectively navigate a UUV sailing in the polar region.

https://www.mdpi.com/1424-8220/17/7/1599/pdf

Polar Grid Navigation Algorithm for Unmanned Underwater Vehicles.

To solve the navigation accuracy problems of multi-Unmanned Underwater Vehicles (multi-UUVs) in the polar region, a polar cooperative navigation algorithm for multi-UUVs considering communication delays is proposed in this paper. UUVs are important pieces of equipment in ocean engineering for marine development. For UUVs to complete missions, precise navigation is necessary. It is difficult for UUVs to establish true headings because of the rapid convergence of Earth meridians and the severe polar environment. Based on the polar grid navigation algorithm, UUV navigation in the polar region can be accomplished with the Strapdown Inertial Navigation System (SINS) in the grid frame. To save costs, a leader-follower type of system is introduced in this paper. The leader UUV helps the follower UUVs to achieve high navigation accuracy. Follower UUVs correct their own states based on the information sent by the leader UUV and the relative position measured by ultra-short baseline (USBL) acoustic positioning. The underwater acoustic communication delay is quantized by the model. In this paper, considering underwater acoustic communication delay, the conventional adaptive Kalman filter (AKF) is modified to adapt to polar cooperative navigation. The results demonstrate that the polar cooperative navigation algorithm for multi-UUVs that considers communication delays can effectively navigate the sailing of multi-UUVs in the polar region.

Polar Cooperative Navigation Algorithm for Multi-Unmanned Underwater Vehicles Considering Communication Delays.

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.

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

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

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One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform’s control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

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An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform

In order to improve the safety of Unmanned Surface Vessel (USV) sailing in the ocean with movable obstacles, a new method based on greedy algorithm, ant colony algorithm and grid method is proposed. By adding periodic repeated calculation of movable obstacles with known paths, the route is periodically replanned. The approximation method is used to solve the problem that the traditional greedy ant colony algorithm is difficult to avoid the movable obstacles in the optimal course design of unmanned surface vessel. The algorithm uses greedy algorithm to plan the optimal route and ant colony algorithm to get rid of the situation that greedy algorithm is easy to enter the local convergence state. By rationalizing the movable obstacles on the known path, the navigation water limit of unmanned surface vessel caused by movable obstacles is avoided. The simulation results show that the algorithm is practical and reasonable for optimal route design when USV avoids movable obstacles. The simulation results show that the algorithm has the disadvantage of long search time.

Path planning for ships avoiding movable obstacles based on improved greedy algorithm

The traditional Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) integrated system uses standard Kalman Filter (KF) to estimate the error states, which weakens the correlation between the different error components to directly initialed the Error Covariance Matrix (ECM) into diagonalization. This initialed method is also widely applied in State Transformation Extended Kalman Filter (STEKF) and Invariant Extended Kalman Filter (IEKF), which results in state estimation failing to achieve the optimal performance. To solve this problem, this paper first analyses the transformed relationship from traditional linear error state to the nonlinear error state redefined in STEKF and IEKF, and the strong correlation is found between the redefined error state components, namely the ECM in STEKF or IEKF no longer appears as a diagonal matrix. Then, aiming at the nonlinear error states, the transformed models of ECM in STEKF and IEKF are derived respectively, which establishes the theoretical basis for ECM initialization based on the error states correlation. Finally, the accuracy, feasibility and general applicability of the proposed method are verified by a boat-mounted field trial. 

/pdf/initialization-of-sins-gnss-error-covariance-matrix-based-on-3jk640md.pdf

Hongwei Bian

Papers

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

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform

Path planning for ships avoiding movable obstacles based on improved greedy algorithm

Initialization of SINS/GNSS Error Covariance Matrix Based on Error States Correlation