Bian Hongwei

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

Abstract A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessels altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. 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 standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However 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 fuzzy logic control method into innovation-based adaptive estimation adaptive 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 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 by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

Adaptive multi-position sensor information fusion method based on AR model

Based on the problem of the difficulty to set up the precise models for the information fuse method which bases on the Kalman filters, a new method based on the autoregression (AR) model is put forward in this paper. This method is one of the time series analysis methods, which uses the temporal correlation between the errors data to set up the AR model, and then the estimation results are utilized to fuse the location information; To detect and isolate the information fault timely, this paper suggests the fault detection and isolation method based on maximum solution separation; Finally, the feasibility of the algorithm is verified by the data collected from the actual sensors.

The application requirement analysis and system design of shipborne small multi-rotors unmanned aerial vehicle

The small multi-rotors unmanned aerial vehicle (UAV) fits for complex narrow ship environment. Using it could improve the efficiency of the lots of ship task. The tasks include the carry mission and video mission could be performed by the small multi-rotors UAV. According to the mission requirements: low weight, small size, antivibration, low power consumption, and easy interfacing, the vehicle system is designed in four subsystems: flight power subsystem, long-range wireless data link subsystem, flight control subsystem and ground control subsystem. The model vehicle is constructed and the flying experiment shows that the model vehicle could realize steady flight and Airline flight, meets the design requirements.

Optimal damping algorithm design in inertial navigation system

In order to restrain the Schuler oscillation in inertial navigation system (INS), external velocity measurement was used as a reference to design optimal damping algorithm based on the Kalman filter. The error models of INS horizontal loops were studied according to its control model and Kalman filters were adopted to estimate the platform misalignment errors, velocity errors and gyro drift. Then based on the optimal control thought, feedback correction algorithm was designed and the estimate was put forward to damp Schuler oscillation and Foucault oscillation. Compared with the traditional damping network, optimal damping algorithm developed the transient response characteristics, in addition it could be used to estimate and compensate gyro drift. Simulation results shown that this algorithm could effectively suppress the Schuler oscillation and improve accuracy of inertial navigation system.