Performance Enhancement of Robust Cubature Kalman Filter for GNSS/INS Based on Gaussian Process Quadrature

Abstract

The sigma-point Kalman filters are generally considered to outperform extended Kalman filter in the application of GNSS/INS, where cubature Kalman filter (CKF) is widely approved because of its rigorous mathematic derivation. In order to improve the robustness of GNSS/INS under GNSS-challenged environment, a robust CKF (RCKF) is developed based on novel sigma-point update framework (NSUF) in our previous work, whereas the efficiency of NSUF is still plagued by the unknown process model uncertainty. In this paper, an enhanced RCKF is proposed based on Gaussian process quadrature (GPQ), where the uncertainty consisted in sigma points transform is processed by GPQ conditioning on the approximated posterior PDF. Experiment result on loosely coupled GNSS/INS demonstrates the superiority of proposed method, where the heading error and roll error are reduced by 60.5% and 37.5% respectively compared with RCKF, and it achieves better position result than GP-CKF under GNSS outage. INDEX TERMS Land vehicle navigation, sigma-point Kalman filter, Gaussian process quadrature, sigma points transform.

An Adaptive Cubature Kalman Filter Based on Resampling-Free Sigma-Point Update Framework and Improved Empirical Mode Decomposition for INS/CNS Navigation

“…(2) The difference ŝ(t) between the signal s(t) and the mean m(t) of these envelopes is calculated: ŝ(t) = s(t) − m(t). (3) Treat ŝ(t) as the new signal s(t) and repeat the above steps until ŝ(t) meets the IMF's two conditions: (1) The equality or a maximum difference of one must be maintained between the quantities of extreme points and zeros; (2) the time axis exhibits local symmetry in the signal. At this time, ŝ(t) becomes the first-order IMF selected by the original signal and is denoted as I MF i (t).…”

“…Inertial navigation systems (INS) and celestial navigation systems (CNS) are critical components in the field of robotics navigation. The INS relies on inertial sensors, typically gyroscopes and accelerometers, to measure the vehicle's linear and angular accelerations, from which position, velocity, and attitude information are deduced through integration and estimation algorithms [1][2][3]. This self-contained system is invaluable in environments where external signals, such as GPS, are unreliable or unavailable.…”

“…The CKF is an innovative nonlinear filtering approach that effectively estimates states in high-dimensional systems. It utilizes the spherical-radial cubature rule to enhance numerical stability and accuracy [1,21]. Additionally, CKF imposes a lower computational load compared to UKF, as it utilizes 2n cubature points instead of 2n + 1 sigma points [5].…”

A Novel Simultaneous Calibration and Localization Algorithm Framework for Indoor Scenarios

“…is the position of th observed GP beacon. Considering the high nonlinearity of (4) and the requirement for real-time performing, Cubature Kalman Filter (CKF) is adopted to locate the target [16], and ̂ represents estimated state of target by CKF at time k.…”

A MEMS-INS assisted GNSS vector tracking loop for robust navigation based on cubature Kalman Filter

“…Because of its better stability and reliability, GNSS/INS integrated navigation system has been widely recognized as a promising alternative to stand-alone GNSS for navigation in weak signal environment. 8,9 Due to the high price of high precision gyros like laser gyro and optic gyro, micro-electro mechanical-system (MEMS)-grade accelerometers and gyroscopes are being used widely as low-cost Inertial Measurement Unit (IMU) sensors. Whereas, it reduces the accuracy of the GNSS/INS navigation system.…”