Reliable Positioning and Journey Planning for Intelligent Transport Systems

El‐Mowafy, Ahmed; Kubo, Nobuaki; Kealy, Allison

doi:10.5772/intechopen.90305

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…In equations ( 50) and (51), the state noise W k ∼ N (0, diag[Q]), and the observation noise V k ∼ N (0, diag [1]):…”

Section: Real-data Experimentsmentioning

confidence: 99%

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Combined adaptive robust Kalman filter algorithm

Lin

et al. 2021

Meas. Sci. Technol.

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The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.

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“…In equations ( 50) and (51), the state noise W k ∼ N (0, diag[Q]), and the observation noise V k ∼ N (0, diag [1]):…”

Section: Real-data Experimentsmentioning

confidence: 99%

“…The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology [1,2]. Global navigation satellite systems are the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF).…”

Section: Introductionmentioning

confidence: 99%

Combined adaptive robust Kalman filter algorithm

Lin

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…It is thus necessary to make urban life more sustainable, livable and efficient whilst ensuring safety, security and health. An accurate and reliable positioning service in urban areas by satellite-based navigation systems is essentially important for hundreds of civilian and military applications [1][2][3][4][5][6][7]. At present, there are four independent Global Navigation Satellite Systems (GNSS), including American GPS, Russian GLONASS, European Galileo and Chinese BeiDou Navigation System, providing an easy, efficient and cost-effective way to determine the location, time, and velocity anywhere around the globe [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data Length Method for GPS Signal Acquisition in Weak to Strong Fading Conditions

et al. 2021

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Satellite-based navigation is an essential part of all the technology-dependent applications, such as road transport, cell phones, the medical field, aviation or the shipping industry, etc. The performance of the navigation systems depends upon how quickly they can acquire and process the received signals for positioning solutions. However, in dense urban or indoor environments, signal acquisition can be a challenging task due to fading as a result of multipath and/or interference. This paper presents post-processing acquisition results on Global Positioning System (GPS) signals to study the relationship between data lengths used for signal acquisition and the achieved signal power using a Fast Fourier Transform (FFT)-based circular correlation method. Based on this study, the detection performance of the FFT-based method has also been analyzed by intentionally degrading the signal power levels. A new Adaptive Data length (ADL) method for acquisition has been proposed in this paper, which can be used for speeding up the acquisition process and uses adaptive data lengths rather than fixed data lengths. The ADL method works by estimating the threshold level based on the noise present in the signal and then comparing it with the signal power levels. Less difference between the threshold level and signal power level means less data length will be used while more difference means that more data length will be used for acquisition. The proposed algorithm can be used in commercially available receivers for adopting to an adaptive acquisition process for increased efficiency.

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Prediction of RTK positioning integrity for journey planning

El‐Mowafy

Kubo

2020

Journal of Applied Geodesy

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Positioning integrity is crucial for Intelligent Transport Systems (ITS) applications. In this article, a method is presented for prediction of GNSS positioning integrity for ITS journey planning. This information, in addition to other route information, such as distance and time, can be utilized to choose the safest and economical route. We propose to combine the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) technique, tailored for ITS, with 3D city models. Positioning is performed by GNSS Real-Time Kinematic (RTK) method, which can provide the accuracy required for ITS. A new threat model employed for computation of the protection levels (PLs) for RTK positioning is discussed. Demonstration of the proposed approach is performed through a kinematic test in an urban area in Tokyo. The comparison between the prediction method and the actual observations show that the two estimate close satellite geometry and PLs. The method produced PLs that bounds the actual position errors all the time and they were less than the preset alert limit.

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Reliable Positioning and Journey Planning for Intelligent Transport Systems

Cited by 4 publications

References 28 publications

Combined adaptive robust Kalman filter algorithm

Combined adaptive robust Kalman filter algorithm

Adaptive Data Length Method for GPS Signal Acquisition in Weak to Strong Fading Conditions

Prediction of RTK positioning integrity for journey planning

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