A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning

Abstract

The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

An Improved Game Theory-Based Cooperative Localization Algorithm for Eliminating the Conflicting Information of Multi-Sensors

“…In the study by Geng et al [ 27 ], the SVD is introduced into the UKF algorithm to deliver a more complex iteration of the covariance matrix, which results in higher stability. Similarly, the SVD is introduced into the derived CKF algorithm to improve the stability in our previous research [ 28 ], which achieved good performance. In this study, the SVD is integrated with the previously introduced DUKF to form an SVD-DUKF method, followed by a simple proof and numerical experiments that verify its effectiveness.…”

Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model

“…Indoor localization using ultra-wideband (UWB) radio is a popular research topic [ 1 , 2 , 3 , 4 , 5 , 6 ] and an off-the-shelf real-time localization system [ 7 ]. Its suitability for mobile applications and its great localization accuracy provide solutions for a wide spectrum of applications.…”

An IMU/ODM/UWB Joint Localization System Based on Modified Cubature Kalman Filtering

“…Using IMU sensors to supplement UWB information, Li et al [ 24 ] successfully improved the ability of the UWB positioning system to deal with process noise by employing state enhancement, which was achieved by measuring Gaussian noise in the system speed with an accelerometer. He et al [ 25 ] minimized the impact of drift errors by fusing the data acquired by UWB and IMU sensors in the federated Kalman filtering (FKF) framework.…”