An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

doi:10.3390/s17092032

Cited by 18 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…F r N are the clock errors transformation matrix for different receivers. The SINS transformation matrix is [20]- [22]:…”

Section: A State Modelmentioning

confidence: 99%

“…where, Zρ R i is the pseudo-range rates measurement vector, and the Eρ 1 R i is the relative velocity projected onto a unit Line-Of-Sight (LOS) vector [20], and the D XYZ ENU is the velocity conversion matrix from the E-N-U coordinates to the ECEF (earth-centered-earth-fixed) coordinates. Vρ R i is the pseudorange rate measurement noise, and the Equation (10) is the linearized version of the measurement models, more details are illustrated in the references [23], [24].…”

Section: B Measurement Modelmentioning

confidence: 99%

See 1 more Smart Citation

Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers

Zhu

Jiang

2020

IEEE Access

View full text Add to dashboard Cite

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy. INDEX TERMS GNSS, INS, tight integration, multiple receivers. II. MULTIPLE RECEIVERS/MEMS-IMU TIGHT INTEGRATION MODEL

show abstract

“…F r N are the clock errors transformation matrix for different receivers. The SINS transformation matrix is [20]- [22]:…”

Section: A State Modelmentioning

confidence: 99%

Section: B Measurement Modelmentioning

confidence: 99%

Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers

Zhu

Jiang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A relatively new method, RMNCE, used to estimate the measurement noise covariance can be applied to the systems with redundant measurements [33,34]. Assume that Z1(k) and Z2(k) are measurements of the true value ZT(k).…”

Section: An Innovative Adaptive Ukf Schemementioning

confidence: 99%

“…Nonetheless, the MAKF is valid only if one of the measurement noise covariances is relatively smaller than the other, so that it can be neglected. To extend MAKF to any redundant measurement systems, an improved MAKF named redundant measurement noise covariance estimation (RMNCE) is proposed in [33,34], which is not only immune to the system state estimation error, but can also estimate the noise variance of the redundant measurement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

Zhang

Jiang

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

show abstract

“…One is for the error covariance matrix, which may be negative on Cholesky decomposition. Here are some methods for this problem such as SR decomposition [13], singular value decomposition (SVD) [14], and adaptive noise variance [15]. SVD is more robust than SR decomposition and less complex than the adaptive noise variance method, so it is adopted in this study [16].…”

Section: Introductionmentioning

confidence: 99%

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Gao

Wang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The distributed drive articulated steering vehicle (DDASV) has a broad application prospect in the field of special operations. It is essential to obtain accurate vehicle states for better effect of active control. DDASV dynamic model is presented. To improve robustness, an adaptive strong tracking algorithm is applied to the singular value decomposition unscented Kalman filter (SVDUKF). Divided by yaw rate sensors and the tire models, two multistage estimators are established for DDASVs. Stable steering condition is simulated to investigate the influence on the estimated accuracy about the sensors and tire models. The velocities and tire forces are the key parameters to be estimated. The performance of each estimator regarding the practicability and accuracy is compared. The results show that all estimators are practicable. However, the accuracy of the estimated velocities based on yaw rate sensors is better and the transient tire model can improve the accuracy of estimated lateral forces more effectively for the estimator established with yaw rate sensors.

show abstract

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

Cited by 18 publications

References 33 publications

Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers

Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

Contact Info

Product

Resources

About