A posteriori estimation of stochastic model for multi-sensor integrated inertial kinematic positioning and navigation on basis of variance component estimation

Abstract

Improving a priori stochastic models of the process and measurement noise vectors in Kalman Filer (KF) has always been a challenge. As one preferable technique to address this challenge, the variance component estimation (VCE) applied on the Kalman Filter's process and measurement noise covariance matrix (Q & R) has been proved in plenty of applications. Unsurprisingly, VCE was expected to re-establish the stochastic model about the random errors in the IMU's measurements in a multisensor integrated positioning and navigation system applying Kalman Filter. However, in the conventional error states-based GPS aided inertial navigation system (GPS/INS), the stochastic model tuning is difficult for the IMU's measurements due to the amalgamation of the observables from inertial sensor and other aiding sensors. This paper proposes a generic method for the stochastic model tuning about the random errors in IMU measurements together with other sensors. The core of this novel approach is based on an innovative multisensor integration strategy which deploys upon the vehicle's generic kinematic model and takes the IMU's output as raw measurements in Kalman Filter (IMU/GNSS Kalman Filter). As a result, the statistical orthogonality between random error vectors of any two sensors enables the separate but parallel statistics collection of each individual random error source. Given these independent statistics corresponding to each error source, the VCE technique iteratively tunes all stochastic model of the process and measurement noise vectors. The success of the VCE algorithm is shown through a real dataset involving GPS and inertial sensors.

Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point

“…These offsets should be modeled to complete the estimation of the invariant point [ 17 ]. Because several types of ground survey measurements with possibly different levels of precision are involved in the adjustment, the variance component model needs to be considered instead of a single variance component [ 27 , 34 , 35 , 36 ].…”

A Refined SNR Based Stochastic Model to Reduce Site-Dependent Effects

“…j denotes the frequency number ( j = 1, 2). The EEM model is the widely used empirical elevation model in GNSS data processing [39][40][41]. a and b are the parameters and θ is the elevation.…”

Three Attitude Models and their Characterization in the Generic Multisensor Integration Strategy for Kinematic Positioning and Navigation

“…The Generic Multisensor Integration Strategy (GMIS) [Qian, 2017;Qian et al, 2015Qian et al, , 2016Wang et al, 2015;Wang and Sternberg, 2000;Wang, 1997] models the basic kinematic states such the position, velocity and acceleration vectors associated with the attitude angles on the ground of 3D kinematics, which allows directly applying the measurements from each of the sensors feasibly through measurement updates in Kalman filtering so that their individual error behaviours could be directly studied, including the gyroscopes and accelerometers. Such studies are not possible from IMU/GNSS error measurements under the traditional integration strategy.…”