Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages

GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method

Fault detection and exclusion are essential to ensure the integrity and reliability of the tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integrated navigation system. A fault detection and system reconfiguration scheme based on generative adversarial networks (GAN-FDSR) for tightly coupled systems is proposed in this paper. The chaotic characteristics of pseudo-range data are analyzed, and the raw data are reconstructed in phase space to improve the learning ability of the models for non-linearity. The trained model is used to calculate generation and discrimination scores to construct fault detection functions and detection thresholds while retaining the generated data for subsequent system reconfiguration. The influence of satellites on positioning accuracy of the system under different environments is discussed, and the system reconfiguration scheme is dynamically selected by calculating the relative differential precision of positioning (RDPOP) of the faulty satellites. Simulation experiments are conducted using the field test data to assess fault detection performance and positioning accuracy. The results show that the proposed method greatly improves the detection sensitivity of the system for small-amplitude faults and gradual faults, and effectively reduces the positioning error during faults.

Synthetic velocity measurement algorithm of monocular vision based on square-root cubature Kalman filter

The velocity measurement algorithm based on vision is widely used in unmanned aerial vehicle navigation. Under uneven illumination intensity distribution, the traditional Lucas-Kanade (LK) optical flow (OF) algorithm has problems arising from low computational accuracy and poor adaptability. To solve these problems, we propose a monocular vision integrated velocity measurement system based on the square-root cubature Kalman filter (SRCKF). The LK OF and the optimized oriented FAST and rotated BRIEF (ORB) algorithms are used to process the visual information obtained using a camera. The SRCKF algorithm is tasked with fusing the LK OF and optimized ORB information, thereby improving the accuracy of velocity and alleviating the sensitivity of the LK OF to variations in illumination conditions. Finally, an outdoor unmanned aerial vehicle flight test was undertaken. The experimental results show that the proposed method provides an accurate measurement of the velocity in variable illumination environments.

Research on slipping-pattern recognition and positioning error compensation of wheeled robot

The autonomous navigation and positioning system is one of the key equipment in industrial automation production. Due to slippage, the odometer (OD) data cannot reflect the real rotation of the wheeled robot, which may cause the unmanned robot to deviate from the preset route. To solve this problem, the integration of the strap-down inertial navigation system (SINS) and the OD is used, and a novel slipping recognition algorithm based on neural network learning and an error compensation method is proposed to improve the navigation accuracy when skidding. First, the output data of the SINS and OD are studied and features are extracted for neural network learning. After training and modeling, the state of the robot slipping can be identified quickly and effectively. Second, different models are designed to compensate the errors of the different slip patterns. Experimental results show that the comprehensive recognition rate of five modes within 1 s is better than 98.9%. After compensation, the positioning errors of the left and right slip modes have been reduced to 15 mm and the positioning errors of the both side and locked slip modes have been reduced to 40 mm. Compared with before compensation, the errors are reduced to less than 6%.