Multi-sensor Fusion Algorithm Based on GPS/MEMS-IMU Tightly Coupled for Smartphone Navigation Application

Wei, Liu; Bingcheng, Liu; Xiao, Chen

doi:10.33012/2017.15167

Cited by 2 publications

(2 citation statements)

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“…The integrated GNSS/INS attitude determination system potentially possesses higher accuracy, sensitivity, continuity, reliability and anti-jam capability in comparison to separate attitude sensors. A lot of corresponding publications are devoted to the problem of the integrated estimation of linear movement parameters: coordinates and velocity [7][8][9][10][11]. The commonly used approaches to the integrated processing of INS and GNSS signals [6] are: loosely coupled [11], tightly coupled [10], deeply integrated [7,9].…”

Section: Introductionmentioning

confidence: 99%

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Deeply Integrated GNSS/Gyro Attitude Determination System

Perov

Shatilov

2020

Sensors

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Attitude determination systems based on Global Navigation Satellite Systems (GNSS) work on principle of phase interferometer, using multiple receiving antennas. They rely on a good quality of carrier phase tracking, that is not the case in real dynamic environment with low signal-to-noise ratio (SNR), for example, in a ground vehicle moving through an urban area or forest. There is still a problem in providing a GNSS attitude in such common conditions. This research is focused on improving sensitivity (i.e., the capability of providing attitude at a low SNR) and the reliability of the GNSS attitude determination system. It is contrasted with the majority of publications, where precision or computational efficiency is the main goal, but sensitivity and reliability are out of their scope. In the proposed system, sensitivity improved by using two measures: (a) tracking only phase differences instead of tracking full carrier phases—this is more sensitive due to the lower dynamics of the underlying process, and (b) using deep integration with gyroscope, where all phase differences are tracked in a vector gyro-aided loop closed on user’s attitude in state vector. The algorithm synthesis is given, and simulation results are presented in this article. This shows that the minimal working SNR is lowered from 27–36 dBHz (typical) down to 20 dBHz, even with a low-cost MEMS gyroscope.

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Section: Introductionmentioning

confidence: 99%

“…In the majority of publications (including [1,[8][9][10][11][12][13][14][15][16]), accelerometer and gyro outputs are considered as measurements. In this article, a different method is used: the gyro measurements are substituted into the state vector (quaternion) dynamic model as a known function, as we described in [6,21].…”

Section: Introductionmentioning

confidence: 99%