High-Precision Vehicle Navigation in Urban Environments Using an MEM's IMU and Single-Frequency GPS Receiver

Zhao, Shunyi; Chen, Yiming; Farrell, Jay A.

doi:10.1109/tits.2016.2529000

Cited by 95 publications

(50 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization in (19) is also solved by the NMILS method outlined in Section III; however, the dimensions are significantly smaller and only a single (linearization) iteration is required.…”

Section: B Outline Of the Cps Methodsmentioning

confidence: 99%

“…whereX is the float solution from (12), ( p , N ) is the CPS solution from (19), and (X * , N * ) is the full NMILS estimate from (11). Proof 4: See Appendix III.…”

Section: Optimality Of the Cps Methodsmentioning

confidence: 99%

“…The proof shows that certain components of the cost function have the same value when evaluated for different trajectories or trajectory sets. Taking advantage of this allows definition of the CPS algorithm described in (19) that vastly reduces the computational load, as summarized in Table I.…”

Section: Optimality Of the Cps Methodsmentioning

confidence: 99%

“…The dimensions of the corresponding A matrices in the full MILS and CPS MILS are M × N s versus 2m × 3, where M n s K + 2m K − 3 is the dimension of the residual vector, N s n s K is the total state dimension of X, and 2m is the total number of GPS measurements at a single epoch (code and carrier phase). Furthermore, while each NMILS iteration of the direct approach requires the expensive INS reintegration, the CPS NMILS of (19) does not [see Row (2a) in Table I]. In the ILS, the reduction step is cast as a QRZ decomposition, which is actually a QR factorization with column pivoting (or column reordering) [35].…”

Section: Computation Analysis Of the Cps Methodsmentioning

confidence: 99%

“…Within the CRT framework [14]- [19], the full nonlinear maximum a posteriori (MAP) estimation problem is solved considering all the information (e.g., prior, kinematics, GPS, and IMU sensor data) available within a multiepoch window [20]- [23]. The CRT approach provides a large enough set of residuals to 1063-6536 © 2015 IEEE.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Computationally Efficient Carrier Integer Ambiguity Resolution in Multiepoch GPS/INS: A Common-Position-Shift Approach

Chen

Zhao

Farrell

2016

IEEE Trans. Contr. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

Abstract-Integer ambiguity resolution is a challenging technical issue that exists in real-time kinematic (RTK) global positioning system (GPS) navigation. Once the integer vector is resolved, centimeter-level positioning estimation accuracy can be achieved using the GPS carrier phase measurements. Recently, a real-time sliding window Bayesian estimation approach to RTK GPS and inertial navigation was proposed to provide reliable centimeter accurate-state estimation, via integer ambiguity resolution utilizing a prior along with all inertial measurement unit and GPS measurements within the time window. One challenge to implementing that approach in practice is the high computation cost. This paper proposes a novel implementation approach with significantly lower computational requirements and includes a thorough theoretical analysis. The implementation results show that the proposed method resolves an integer vector identical to that of the original method and achieves state estimation with centimeter global positioning accuracy.Index Terms-Global positioning system (GPS), inertial navigation, inertial navigation system (INS), integer ambiguity, real-time kinematic (RTK), sliding window estimation.

show abstract

Section: B Outline Of the Cps Methodsmentioning

confidence: 99%

“…whereX is the float solution from (12), ( p , N ) is the CPS solution from (19), and (X * , N * ) is the full NMILS estimate from (11). Proof 4: See Appendix III.…”

Section: Optimality Of the Cps Methodsmentioning

confidence: 99%

Section: Optimality Of the Cps Methodsmentioning

confidence: 99%

Section: Computation Analysis Of the Cps Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Computationally Efficient Carrier Integer Ambiguity Resolution in Multiepoch GPS/INS: A Common-Position-Shift Approach

Chen

Zhao

Farrell

2016

IEEE Trans. Contr. Syst. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Aghapour

Farrell

2019

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

Summary This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman‐Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer‐specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving‐horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk‐averse performance‐specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH‐RAPS solution for outlier accommodation. The paper also presents implementation results. The MH‐RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH‐RAPS performance is compared with MH‐NP state estimation.

show abstract

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

View full text Add to dashboard Cite

Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an “EKF‐like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

show abstract

High-Precision Vehicle Navigation in Urban Environments Using an MEM's IMU and Single-Frequency GPS Receiver

Cited by 95 publications

References 28 publications

Computationally Efficient Carrier Integer Ambiguity Resolution in Multiepoch GPS/INS: A Common-Position-Shift Approach

Computationally Efficient Carrier Integer Ambiguity Resolution in Multiepoch GPS/INS: A Common-Position-Shift Approach

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

Contact Info

Product

Resources

About