Applying Time-Differenced Carrier Phase in Nondifferential GPS/IMU Tightly Coupled Navigation Systems to Improve the Positioning Performance

Zhao, Yiwei

doi:10.1109/tvt.2016.2558206

Cited by 56 publications

(23 citation statements)

References 22 publications

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“…The subscripts a and g denote the accelerometer and gyroscope, respectively. The equations for the inertial measurement unit (IMU) error can be found in [1,5,15]. In this study, as the TDCP-based relative position was used as the measurement of the LC filter, the observation matrices were defined as follows:H=[I3×303×15]J=−H…”

Section: Theorymentioning

confidence: 99%

“…However, Han and Wang [14] asserted that the modeling and integration method for utilizing TDCP measurements described by Wendel were incorrect and proposed a different approach using a dual-rate KF with pseudorange and TDCP measurements. Zhao [15] reported that the TDCP measurement modeling methods employed in previous studies produced inaccurate results and proposed a different modeling method. While previous studies have dealt with TDCP measurements such as velocity information, Li et al [5] estimated the relative position in terms of the phase-derived position increment (PDPI) using TDCP measurements and improved the positioning accuracy by applying additional PDPI measurements in the conventional TC method.…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of Time-Differenced Carrier Phase Measurement-Based Integrated GPS/INS Considering Noise Correlation

Kim

Song

et al. 2019

Sensors

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In this study, we combined a time-differenced carrier phase (TDCP)-based global positioning system (GPS) with an inertial navigation system (INS) to form an integrated system that appropriately considers noise correlation. The TDCP-based navigation system can determine positions precisely based on high-quality carrier phase measurements without difficulty resolving integer ambiguity. Because the TDCP system contains current and previous information that violate the format of the conventional Kalman filter, a delayed state filter that considers the correlation between process and measurement noise is utilized to improve the accuracy and reliability of the TDCP-based GPS/INS. The results of a dynamic simulation and an experiment conducted to verify the efficacy of the proposed system indicate that it can achieve performance improvements of up to 70% and 60%, respectively, compared to the conventional algorithm.

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Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Time-Differenced Carrier Phase Measurement-Based Integrated GPS/INS Considering Noise Correlation

Kim

Song

et al. 2019

Sensors

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show abstract

“…However, GPS and IMU systems have different sampling frequencies, and, in Ref. [63], the so-called time-difference carrier phase (TDCP) problem was solved.…”

Section: Gps + Imumentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method.

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“…The latter technique makes use of proprioceptive-based approaches, mostly involving inertial measurement units (IMUs), in which accelerometers are combined with rate gyros and magnetometers. These devices can be combined with GPS to improve the overall accuracy (9). Although a wide range of commercial IMUs are available, these sensors are still subject to long-term drift in both the static and dynamic modes, as well as being highly sensitive to the electromagnetic interferences generated by ferrous building components.…”

Section: Introductionmentioning

confidence: 99%

AntBot: A six-legged walking robot able to home like desert ants in outdoor environments

2019

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Autonomous outdoor navigation requires reliable multisensory fusion strategies. Desert ants travel widely every day, showing unrivaled navigation performance using only a few thousand neurons. In the desert, pheromones are instantly destroyed by the extreme heat. To navigate safely in this hostile environment, desert ants assess their heading from the polarized pattern of skylight and judge the distance traveled based on both a stride-counting method and the optic flow, i.e., the rate at which the ground moves across the eye. This process is called path integration (PI). Although many methods of endowing mobile robots with outdoor localization have been developed recently, most of them are still prone to considerable drift and uncertainty. We tested several ant-inspired solutions to outdoor homing navigation problems on a legged robot using two optical sensors equipped with just 14 pixels, two of which were dedicated to an insect-inspired compass sensitive to ultraviolet light. When combined with two rotating polarized filters, this compass was equivalent to two costly arrays composed of 374 photosensors, each of which was tuned to a specific polarization angle. The other 12 pixels were dedicated to optic flow measurements. Results show that our ant-inspired methods of navigation give precise performances. The mean homing error recorded during the overall trajectory was as small as 0.67% under lighting conditions similar to those encountered by ants. These findings show that ant-inspired PI strategies can be used to complement classical techniques with a high level of robustness and efficiency.

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Applying Time-Differenced Carrier Phase in Nondifferential GPS/IMU Tightly Coupled Navigation Systems to Improve the Positioning Performance

Cited by 56 publications

References 22 publications

Performance Improvement of Time-Differenced Carrier Phase Measurement-Based Integrated GPS/INS Considering Noise Correlation

Performance Improvement of Time-Differenced Carrier Phase Measurement-Based Integrated GPS/INS Considering Noise Correlation

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

AntBot: A six-legged walking robot able to home like desert ants in outdoor environments

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