GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

Attitude determination and localization of mobile robots using two RTK GPSs and IMU

Abstract: This paper focuses on the design and test results of an adaptive variation of Kalman filter (KF) estimator based on fusing data from Inertial Measurement Unit (IMU) and two Real Time Kinematic (RTK) Global Positioning Systems (GPS) for driftless 3-D attitude determination and robust position estimation of mobile robots. GPS devices are notorious for their measurement errors vary from one point to the next. Therefore in order to improve the quality of the attitude estimates, the covariance matrix of measurement noise is estimated in real time upon information obtained from the differential GPS measurements, so that the KF filter continually is “tuned” as well as possible. No a priori knowledge on the direction cosines of the gravity vector in the inertial frame is required as these parameters can be also identified by the KF, relieving any need for calibration. Next, taking advantage of the redundant GPS measurements, a weight least-squares estimator is derived to weight the GPS measurement with the “good” data more heavily than the one with “poor” data in the estimation process leading to a robust position estimation. Test results are presented showing the performance of the integrated IMU and two GPS to estimate the attitude and location of a mobile robot moving across uneven terrain.

Data fusing algorithm in geomagnetic aided INS

Abstract: The theory of the geomagnetic aided inertial navigation system(INS) was analyzed.The unscented Kalman filtering was introduced and applied into the integrated system.In unscented Kalman filtering,the updating of the filtering based on the nonlinear state space equation was realized by designing a few sigma points and calculating the transformation of these sigma points via non-linear function.The simulation shows that it is more precise and more convenient in computing,and the performance of unscented Kalman filter is superior to that of extended one,so it can be used in Geomagnetic aided INS.

System and method for intelligent tuning of kalman filters for ins/gps navigation applications

Abstract: Disclosed is a reinforcement learning technique for online tuning of integration filters of navigation systems needing a priori tuning parameters, such as Kalman Filters and the like. The method includes receiving GNSS measurements from the GNSS unit of the navigation system; and IMU measurements from IMU of the navigation system. The method further includes providing a priori tuning parameters to tune the integration filter of the navigation system. The method further includes processing the GNSS and IMU measurements using the tuned integration filter to compute a position estimate and updating the a priori turning parameters based on the computer position estimate.

Evaluation of an integrated GPS/INS system for shallow-water AUV navigation (SANS)

Abstract: Many possible autonomous underwater vehicle (AUV) missions require a high degree of navigational accuracy. The Global Positioning System (GPS) is capable of providing this accuracy. However, intermittent reception caused by either wave action or deliberate submergence will cause the loss of GPS position fix information for periods extending from several seconds to minutes. The SANS system is designed to demonstrate the feasibility of using a low-cost strapped-down inertial measurement unit to navigate between GPS fixes. It is anticipated that navigational accuracy comparable to GPS is possible between fixes.

Fuzzy Adaptive Interacting Multiple Model Algorithm for INS/GPS

Abstract: The integration of INS and GPS is usually achieved using a Kalman filter. The precision of INS/GPS system will be reduced in condition that a priori information used in Kalman filter does not accord with the actual environmental conditions. The problem of INS/GPS navigation system with uncertain noise is considered in this paper. Fuzzy adaptive Kalman filtering algorithm (FAKF) and adaptive interacting multiple model algorithm (AIMM) is combined, named FAIMM, to address this problem. In each cycle of FAIMM, FAKF is used firstly to determine rough statistical characteristics of noise, then the AIMM algorithm completes the integration of INS/GPS data, using a limited number of subfilters formed according to the rough values obtained from the FAKF. Simulations in INS/GPS integrated navigation system demonstrate that the FAIMM algorithm can obtain better statistical estimation of noise and provide better coverage of variable noise statistical characteristics than IMM when environmental conditions change, and the accuracy is improved compared with either Kalman filter or IMM algorithms.