Performance of deeply integrated GPS/INS in dense forestry areas

Soloviev, Andrey; Toth, Charles K.; Grejner‐Brzezinska, Dorota A.

doi:10.1515/jag-2011-0005

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…• Integration with inertial navigation systems (INSs) -combines the benefits of two heterogeneous systems into one localization system: [14], [52], [53], [54], [55], [56], [57], [58].…”

Section: Identificationmentioning

confidence: 99%

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

Breßler¹,

Obst²

2017

Adv. sci. technol. eng. syst. j.

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“…• Integration with inertial navigation systems (INSs) -combines the benefits of two heterogeneous systems into one localization system: [14], [52], [53], [54], [55], [56], [57], [58].…”

Section: Identificationmentioning

confidence: 99%

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

Breßler¹,

Obst²

2017

Adv. sci. technol. eng. syst. j.

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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%

“…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%

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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“…Approaches to NLOS mitigation are therefore needed. With the aid of receiver dynamic provided by inertial sensor, tightly and ultra-tightly coupled GPS/INS integrations are proposed to mitigate the multipath and NLOS effects [ 5 , 6 , 7 , 8 , 9 ]. A multipath estimation, which is based on integration between GNSS receiver and LiDAR sensor, is proposed [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models

Hsu

Kamijo

2015

Sensors

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Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

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Performance of deeply integrated GPS/INS in dense forestry areas

Cited by 17 publications

References 11 publications

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

Deeply Integrated GNSS/Gyro Attitude Determination System

NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models

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