An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

Abstract

The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent prefilter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/ INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.

A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation

“…The GNSS/SINS deep-coupled system has several kinds of deep-coupled structures which have been discussed in [19,21,22]. In this paper, a federated filtering architecture with a coherent prefilter algorithm will be designed in the following subsections.…”

“…Finally, the fifth-degree Cubature Kalman filter (5th-CKF) for the prefilter, which was proposed and described in [19] in detail, is used in this paper to solve the nonlinear problem of the prefilter and get a higher filter accuracy. Besides this, the noise variance in observations can be computed as a function of C/N 0 as reported in [22,24].…”

“…Some grid-SINS-based integrated systems with the star tracker [6] and Doppler velocity logger (DVL) [7,15] have been designed, but they have limits of application such as weather conditions or underwater/shipping applications only. Although a loose-coupled GNSS/grid SINS is proposed in [16], neither the coverage and usability of satellites, the quality of GNSS signals, nor the dynamic requirements have been considered, which may greatly affect the navigation performance in polar areas.It is noted that the deep-coupled GNSS/SINS structure has the deepest information complementation and will provide preferable solutions under challenging environments [17][18][19], which will be a good solution for polar navigation. Therefore, with the improvement of the coverage and usability of the GNSS constellations in polar areas and the development of grid SINS, this paper proposes a deep-coupled navigation system composed of dual-frequency GNSS and grid SINS for polar navigation.…”

“…It is noted that the deep-coupled GNSS/SINS structure has the deepest information complementation and will provide preferable solutions under challenging environments [17][18][19], which will be a good solution for polar navigation. Therefore, with the improvement of the coverage and usability of the GNSS constellations in polar areas and the development of grid SINS, this paper proposes a deep-coupled navigation system composed of dual-frequency GNSS and grid SINS for polar navigation.…”

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

“…It provides a mathematical framework for predicting unmeasured variables from indirectly noisy measurements. As a predictive tool, Kalman filter is mainly used to estimate the state of dynamic systems, such as process control [35,36], flood forecasting [37], radar tracking [38], GNSS navigation [39,40] and performance analysis of estimation systems. Sedano et al [41] used a Kalman filter algorithm to achieve spatiotemporal fusion of existing Landsat TM and 250-m NDVI MODIS (MOD13Q1) images for predictions of synthetic Landsat NDVI values.…”