Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.
In implementing an INS/SLAM integrated navigation system based on the vision sensor, a suboptimal nonlinear filter is used to figure out the nonlinear characteristics in measurement and noise model. When a conventional centralized filter is used, however, the entire state vectors need to be reconfigured in every necessary cycle as the number of feature points changes, which is hard to isolate potential faults. Furthermore, any change in the number of feature points and a subsequent increase in the dimension of state variables may result in an exponential growth in computation quantities. In order to address these issues, this paper presents a distributed particle filter approach for implementing a vision sensor based INS/SLAM system. The proposed system has several local filters which are subject to change flexibly by the number of feature points, and separates state vectors into sub-states for vehicle dynamics and feature points so that minimum state vectors can be estimated in the master filter. Simulation results show that the distributed particle filter performs competitively as with the centralized particle filter and is capable of improving computation quantities.
GPS was developed for military purposes in the United States, and is a representative global navigation satellite system (GNSS) that can be used throughout the globe. Currently, a GPS modernization project is in progress. Also, GLONASS from Russia has resumed the service through a modernization project, and it can be used throughout the globe. In addition, Galileo from EU and Beidou from China have been developed for service, and thus it is expected that various GNSS signals would be available in the future. As for the GPS L1 C/A signal which is one of the civilian signals provided by GPS, the structure is open to the public, and it has been used in the major industrial fields (e.g., aviation
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