Adaptive non-holonomic constraint aiding Multi-GNSS PPP/INS tightly coupled navigation in the urban environment

Cheng, Sixiang; Cheng, Jianwei; Zang, Nan; Cai, Jing; Fan, Shilong; Song, Haoran

doi:10.1007/s10291-023-01475-9

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Inertial Navigation Systems (INS) are independent systems immune to external environmental interference [11]. However, INS errors gradually accumulate over time [12], and the Inertial Measurement Unit (IMU) data may also contain outliers due to road vibrations and other factors, as illustrated in Figure 1b. Therefore, in order to achieve more accurate navigation and positioning results, the method of integrating GNSS and INS is commonly used [13].…”

Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Yin,

Yang,

et al. 2023

Remote Sensing

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Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.

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Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Yin,

Yang,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

Enhancing TDCP-specified GNSS/INS tightly coupled filter robustness in cascaded for low-cost urban applications

Chen,

Zhao,

et al. 2024

Meas. Sci. Technol.

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Integration of global navigation satellite system (GNSS) with the inertial navigation system (INS) is commonly employed to enhance navigation performance. However, conventional integrated algorithms suffer from low-quality GNSS measurements due to either inaccurate pseudorange or difficulty in ambiguity resolution when utilizing carrier phase (CP) measurements in urban environments. We propose a new time-differenced CP (TDCP-) specified GNSS/INS tightly coupled integration algorithm for low-cost urban applications. The proposed method employs a dual-rate filter scheme, wherein the potential of TDCP observations and INS-derived position are fully harnessed for state estimation with PR outlier inhibition and CP small cycle slip repair. Field test results in urban environments demonstrate that the proposed integrated navigation scheme is superior to conventional algorithms, with an average improvement of about 13.4%, 19.2%, and 25.5% in position, velocity, and attitude, respectively.

show abstract

Adaptive non-holonomic constraint aiding Multi-GNSS PPP/INS tightly coupled navigation in the urban environment

Cited by 2 publications

References 26 publications

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Enhancing TDCP-specified GNSS/INS tightly coupled filter robustness in cascaded for low-cost urban applications

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