Integration of GPS and an Inertial System for Precise Surveying Applications

Chen, W.; Cross, PA

doi:10.1179/003962690791625591

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Integrated navigation systems, such as INS/GPS, GPS/ DR (Dead Reckoning), are introduced mainly to overcome the limitation of each individual navigation sensor and improve the reliability (Chen and Cross 1990;Umar et al 2007;Logan et al 2003). However, such integrations are too expensive to be extensively used in low-cost navigation campaigns.…”

Section: Introductionmentioning

confidence: 99%

A windowing-recursive approach for GPS real-time kinematic positioning

Zhou

Shen

2010

GPS Solut

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All conventional Kalman filtering methods depend to a great extent on dynamic models for describing the motion state of vehicle. However, low-cost GPS navigation systems do not provide velocity and acceleration measurements to construct dynamic models. Therefore, it is rather difficult to establish reasonable dynamic models. A windowing-recursive approach (WRA) which employs previous positions to predict the current position is proposed, and the transition matrix is modeled for transforming the previous positions to the current one. Two typical transition matrices are constructed by numerical polynomial fitting and extrapolation. A real vehicular GPS experiment is carried out to demonstrate the WRA performances in two relative positioning scenarios. The data are processed by the least squares approach and by WRA using the two developed transition matrices. The results show that the WRA performed excellently in a high sampling rate data. In case of a lower sampling rate, higherorder polynomial fitting and extrapolation models work better than lower-order models for a given window. In addition, the extrapolation models can alleviate the computation burdens significantly relative to the polynomial fitting models.

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Section: Introductionmentioning

confidence: 99%

A windowing-recursive approach for GPS real-time kinematic positioning

Zhou

Shen

2010

GPS Solut

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“…Different adaptive Kalman filtering algorithms have been studied for surveying and navigation applications. Chen (1992) and Mohamed and Schwarz (1999) applied adaptive Kalman filters for the integration of GPS and inertial navigation system (INS). Wang et al (1997) applied a simplified adaptive algorithm in kinematic GPS positioning.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman Filtering for Vehicle Navigation

Hu¹,

Chen²,

Chen³

et al. 2003

J. of GPS

187

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Abstract. Kalman filters have been widely used for navigation and system integration. One of the key problems associated with Kalman filters is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS navigation, the manoeuvre of the vehicle and the level of measurement noise are environmental dependent, and hardly to be predicted. Therefore to assign constant noise levels for such applications is not realistic.In this paper, real-time adaptive algorithms are applied to GPS data processing. Two different adaptive algorithms are discussed in the paper. A number of tests have been carried out to compare the performance of the adaptive algorithms with a conventional Kalman filter for vehicle navigation. The test results demonstrate that the new adaptive algorithms are much robust to the sudden changes of vehicle motion and measurement errors.

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Measurement of railway profiles using GPS integrated with other sensors

Leahy

Judd²,

Shortis³

Proceedings of VNIS '93 - Vehicle Navigation and Information Systems Conference

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Integration of GPS and an Inertial System for Precise Surveying Applications

Cited by 3 publications

References 3 publications

A windowing-recursive approach for GPS real-time kinematic positioning

A windowing-recursive approach for GPS real-time kinematic positioning

Adaptive Kalman Filtering for Vehicle Navigation

Measurement of railway profiles using GPS integrated with other sensors

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