Tightly-coupled model for INS/WSN integrated navigation based on Kalman filter

Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.

Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements

In order to improve the accuracy of the data fusion filter, a tightly-coupled ultra wide band(UWB)/inertial navigation system(INS)-integrated scheme for indoor human navigation will be investigated in this paper. In this scheme, the data fusion filter employs the difference between the INS-measured and UWB-measured distances as the observation. Moreover, the predictive adaptive Kalman filter(PAKF) for the tightly-coupled INS/UWB-integrated human tracking model with missing data of the UWB-measured distance will be designed, which considers the missing data of the UWB-based distance and employs the predictive UWB-measured distance. Real test results will be done to compare the performance of the Kalman filter(KF), adaptive Kalman filter(AKF), and the PAKF. The test results show that the performance of the AKF is better than the KF. Moreover, the proposed PAKF is able to maintain the performance of the filter when the UWB-based measurement is unavailable.