Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

Abstract

Typically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

Design of the Violin Performance Evaluation System Based on Mobile Terminal Technology

“…At the same time, if the fundamental frequencies of polyphonic music with multinote superposition overlap each other, they will affect each other in time-domain and frequency domain, which will bring greater difficulties to recognition. Elmezayen proposed a piano multinote estimation algorithm based on spectral envelope non-negative matrix decomposition, and its performance has been greatly improved on the international universal dataset maps in MIREX (Music Information Retrieval Evaluation eXchange) [6]. Mastellone used a new spectrum analysis method to realize the non-negative matrix decomposition system, which currently performs well on maps datasets [7].…”

Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments

“…where δr = δφ δλ δh T is the position error vector; δv = δv e δv n δv u T is the velocity error vector; δε = δp δr δy T is the attitude angles' error vector; δb a = δb ax δb ay δb az T is the accelerometer bias error vector; δb g = δb gx δb gy δb gz T is the gyroscope bias error The covariance matrix of the measurements R k−LI MO is assumed to be diagonal and contains the variances of the position and attitude angles received from the LIMO estimations, which are depicted by Equation (7).…”

“…A navigation system should be equipped with several integrated onboard sensors so that if one sensor malfunctions for any reason, the other sensors will allow the system to operate safely and properly [1][2][3]. The global navigation satellite system (GNSS)/inertial navigation system (INS) integration has been well-studied and adopted in a large number of studies [4][5][6][7][8][9][10][11]. Typically, the measurements of both the GNSS and the inertial measurement unit (IMU) are fused using a Kalman filter [12] that features the use of several integration methods between the GNSS and the IMU [13] (i.e., loosely coupled and tightly coupled).…”

INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments

“…The integration of the global navigation satellite system (GNSS) with the inertial navigation system (INS) has been extensively researched and implemented in numerous studies [4][5][6][7][8][9][10][11]. Typically, the GNSS and the inertial measurement unit (IMU) measurements are merged using a Kalman filter [12], which can employ various integration methods between the GNSS and the IMU, such as loosely coupled and tightly coupled integrations.…”