Evaluation of the performance of GNSS-based velocity estimation algorithms

The BeiDou-3 global navigation satellite system (BDS-3) was operated successfully in July 2020. It broadcasts PPP-B2b signals through geosynchronous earth orbit (GEO) satellites and provides real-time precise point positioning (PPP) services free of charge, which greatly meets the navigation needs of real-time users, especially warship navigation. This paper introduces the real-time positioning strategy of PPP-B2b in detail, and the performance of PPP-B2b positioning and PPP-B2b/strapdown inertial navigation system (SINS) integrated navigation is studied through marine experiments. The experimental results show the real-time dynamic positioning accuracy of PPP-B2b can reach the decimeter level, and the positioning accuracy is not significantly improved when integrated with SISN. Furthermore, dynamic alignment experiments with different yaw misalignment angles are conducted. The experimental results show after adding TDCP and Doppler velocimetry information, the yaw misalignment angle is improved by 60.4%, and 63.0% respectively when the yaw misalignment angle is 30°, 34.7%, and 38.2% respectively when the yaw misalignment angle is 90°, 60.2% and 61.4% respectively when the yaw misalignment angle is 120° compared with PPP-B2b only. The accuracy of the extended Kalman filter (EKF) algorithm is 16.7% higher than that of the factor graph optimization (FGO) algorithm after the convergence of the yaw angle error.

Section: Real-time Velocity Measurement Algorithmmentioning

confidence: 99%

Real-time marine PPP-B2b/SINS integrated navigation based on BDS-3

Chai

2023

“…The existence of truncation error and propagation error limits the application of TDCP algorithm [15]. In multiple scenarios, TDCP algorithm and Doppler algorithm have their own advantages [16]. Considering the correlation between observations as well as the time correlation between epochs, the estimation of variance components can significantly improve the accuracy of TDCP/Doppler joint velocity measurements [6,17].…”

Section: Introductionmentioning

confidence: 99%

Integrated navigation model based on TDCP constrained algorithm

Liu,

Li,

Ning

2023

The velocity information estimated by the GNSS receiver is an important element for the dynamic alignment of the inertial navigation system (INS), and it is of great significance to analyze it deeply and meticulously. A variety of GNSS velocity measurement models show different characteristics in a changeable environment, and this status quo is bound to break the monotonous situation in which the Doppler model is widely used. In this regard, this paper applies different GNSS velocity measurement models to SINS dynamic alignment and continuous observation. In addition, aiming at the shortcomings of the traditional time-differenced carrier phase (TDCP) algorithm, an optimization method is deduced from the formula level, and two effective constraint algorithms are given. Then, according to the vehicle test results, comprehensively compare the integrated navigation performance of various velocity measurement models and analyze the improvement effect of the proposed TDCP algorithm. This paper provides a summary for the comprehensive study of GNSS velocity measurement model and the application of optimized carrier phase to integrated navigation, which has certain practical value.

“…Alternatively, three-dimensional (3D) velocity can be derived from many GNSS-based methods [27,28], such as the raw Doppler method, the time-differenced pseudorange method, and the time-differenced carrier phase (TDCP) method. The methods with the Doppler or pseudorange measurements generally output velocities with relative low accuracy under low dynamic scenarios [29]. Benefiting from accurate carrier phase observations, the TDCP method can obtain precise average velocities, provided the cycle slips are correctly repaired [30].…”

Section: Introductionmentioning

confidence: 99%

Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

Zhang,

Tang,

Deng

et al. 2023

The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna GNSS baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extend Kalman filtering (REKF) is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by Institute of Geodesy and Geophysics (IGG)-Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s-1 and 1.5 m s-1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.