Autonomous Vehicles have become the focus of research around the world.Meanwhile, the continuous development of various emerging technologies in recent years has paved the way for the realization of fully autonomous driving.Furthermore, high-precision navigation and positioning systems are crucial to self-driving cars.For navigation and positioning technology of autonomous vehicles, Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is of great significance to realizing high-precision navigation and positioning of autonomous vehicles.This paper researches and summarizes the development and current situation of GNSS, INS, and GNSS/INS integrated navigation technology.GNSS/INS integrated navigation algorithms are divided into four different models of combination methods: Ultra tightly Coupled, Tightly Coupled, Semi-tightly Coupled, and Loosely Coupled.Besides, the advantages and disadvantages of these four integrated navigation algorithms are compared and discussed.The characteristics of different GNSS/INS coupling models are summarized in the last chapter, and the potential research direction of GNSS/INS in the future is proposed. 

Research on GNSS INS & GNSS/INS Integrated Navigation Method for Autonomous Vehicles: A Survey

As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.

/pdf/a-multi-gnss-imu-data-fusion-algorithm-based-on-the-mixed-5f03yg8y.pdf

A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications

Aimed at the problem of filter divergence caused by unknown noise statistical characteristics or variable noise characteristics in an MEMS/GNSS integrated navigation system in a dynamic environment, on the basis of revealing the parameter adjustment logic of covariance matching adaptive technology, a fusion adaptive filtering scheme combining innovation-based adaptive estimation (IAE) and the adaptive fading Kalman filter (AFKF) is proposed. By setting two system tuning parameters, for the process noise covariance adaptation loop and the measurement noise covariance adaptation loop, covariance matching is sped up and achieves an effective suppression of filter divergence. The vehicle-mounted experimental results show that the mean square error of the combined attitude error obtained based on the fusion filtering method proposed in this paper is better than 0.5°, and the mean square error of the heading error is better than 1.5°. The results can provide technical support for the continuous extraction of low-cost attitude information from mobile platforms.

https://www.mdpi.com/2072-666X/13/10/1787/pdf?version=1666317704

An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching

Estimating inter-system biases for tightly combined Galileo/BDS/GPS RTK

Abstract The China BeiDou Navigation Satellite System (BDS-3) is recognized for its advantages compared to BDS-2. However, the enhancement of performance through the simultaneous utilization of BDS-2 and BDS-3 in real-time kinematic (RTK) applications remains insufficiently investigated. Herein, we developed an overlapping triple-frequency (TF) BDS-3/BDS-2/inertial navigation system (INS) tightly coupled (TC) integration model that takes advantage of the BDS-3/BDS-2 overlapping frequencies of B1I/B2b(B2I)/B3I for intersystem combination and INS-assisted positioning. An analytical formula for ambiguity dilution of precision (ADOP) was derived, serving as the foundation for an exploration into how multi-frequency measurements and INS assistance affect ambiguity resolution (AR). A vehicle experiment was conducted in a city to evaluate the performance of the measurement models for various frequencies, available satellites, and INS assistance. Analysis of the double differencing errors of the pseudorange and carrier phases revealed that a robust model in kinematic situations is preferred over static situations. AR ability was assessed regarding ADOP, ratio test, and success rates, and the positioning and attitude determination results were examined. Overall, the characteristics of the ADOP analytic formula and processing results produce a similar conclusion regarding the contribution of multi-frequency, available measurements, and INS assistance on AR; further, they provide reference values for moving measuring system users to implement the optimal model in kinematic situations. 

Assessment of overlapping triple-frequency BDS-3/BDS-2/INS tightly coupled integration model in kinematic surveying

Kalman filter (KF) is an efficient approach for state estimation in integrated positioning systems of global navigation satellite systems and inertial navigation systems (GNSS/INS). Unfortunately, GNSS/INS integrated positioning systems are susceptible to state model perturbations and measurement outliers, which can severely degrade KF performance, especially in complex urban environments. To solve this problem, a robust adaptive filtering algorithm based on variational Bayesian (VBRAKF) is presented that can be employed in GNSS/INS tightly coupled positioning systems. In VBRAKF, the robust estimation approach is first introduced into the filter, followed by modeling the predicted covariance matrix using the inverse Wishart distribution, and finally the state is estimated using Variational Bayesian. The robustness is achieved by adjusting the noise by an equivalent weight matrix to reduce gross errors in observations, while the adaptiveness is realized by the variational Bayesian estimation method for the uncertain predicted noise covariance matrix. The positioning performance of the proposed algorithm in urban environments is verified through two sets of land vehicle experiments. The experimental results demonstrate that the 3D positioning accuracy of the VBRAKF algorithm is improved by 32.1 % and 23.5 % in experiment 1 and by 37.1 % and 17.3 % in experiment 2 when compared to KF and Robust Kalman Filter (RKF). The proposed VBRAKF algorithm has better positioning performance in all schemes, can simultaneously control the influence of measurement outliers and inaccurate noise statistics and improves the accuracy of filter estimation. 

Variational Bayesian-based Robust Adaptive Filtering for GNSS/INS Tightly Coupled Positioning in Urban Environments

Knowledge of differential inter-system biases (DISBs) is critical to integrate observations from mixed GNSS. If the corresponding DISB could be calibrated in advance, only one pivot satellite is sufficient for ambiguity resolution on overlapping frequencies, which is the so-called tight combining (TC) strategy. Considering that GPS and Galileo transmit signals on two identical frequencies (e.g. L1/E1 and L5/E5a), a tightly combined GPS/Galileo RTK positioning model is proposed in this paper. Traditional DD model has been slightly adjusted to avoid the hand-over problem of reference satellites. The estimation of code and fractional part of phase DISB is archived through zero and ultra-short baselines. Three long baselines were selected to verify the proposed model with DISB calibrated in advance. Moreover, to get better AR performance, a simple but robust procedure of PAR, where the satellite elevation, number of consecutive tracking, success rate and ratio test are all combined to determine the subset of ambiguity, is adopted in the long baseline experiments. Results shows that the code and fractional part of phase DISB is rather stable. The TC strategy do not significantly improve the value of ratio, but shorten the convergence time to reach the 100% success rate. Compared with results of loose combining (LC) strategy, time to first fix (TTFF) is further reduced by 54.3, 72.9, 69.0% respectively under TC strategy corresponding to different long baselines. Besides, TC strategy could slightly improve the fixing rate of epochs. In terms of accuracy, the precision in up direction is worse than that in north and east direction. Once the ambiguity is fixed correctly, both LC and TC strategy can achieve centimeter-level positioning accuracy.

A Tightly Combined GPS/Galileo Model for Long Baseline RTK Positioning with Partial Ambiguity Resolution

To take full advantages of Galileo and BDS-3 penta-frequency signals, a long-baseline RTK positioning method based on Galileo and BDS-3 penta-frequency ionosphere-reduced (IR) combinations is proposed. First, the high-quality signals with low-noise and weak-ionospheric delay characteristics of Galileo and BDS-3 are analyzed. Second, the multi-frequency extra-wide-lane (EWL)/ wide-lane (WL) combinations with long-wavelengths are constructed. Third, the IR-EWL combinations are calculated by geometry-free (GF) method, then the resolved IR-EWL combinations are used to constrain the IR-WL, of which the ambiguities can be obtained in a single epoch. There is no need to consider the influence of ionospheric parameters in the third step because the ionospheric delay factors of IR-EWL/WL combinations are close to 0. Compared with the estimated ionosphere model, the proposed method can improve the availability of positioning and reduce the number of parameters by half and the required operation time is greatly reduced. Therefore, it reduces the dimension of parameter estimation and is suitable for the use of multi-frequency and multi-system real-time RTK. The results using real data show that stepwise fixed model of the IR-EWL/WL combinations can realize long-baseline instantaneous decimeter-level positioning.

Instantaneous Real-Time Kinematic Decimeter-level Positioning with Galileo and BDS-3 Penta-Frequency Signals Over Long-Baseline

Tightly Combined BDS-3 and Galileo Penta-frequency Observations for Long-baseline RTK Positioning

Vehicular positioning in urban environments has become a research hotspot in recent years, and global navigation satellite system/inertial navigation system (GNSS/INS) tightly coupled positioning is a commonly used method. With the broadcast of BDS-3 and Galileo four-frequency signals, the multi-frequency and multi-system tightly coupled positioning method provides more possibilities for vehicular positioning in urban environments. At present, most of the studies on multi-frequency and multi-system mainly focus on static or baseline solutions, and there are few studies on the urban dynamic environment. In this paper, based on the triple-frequency GPS/BDS-2/INS tightly coupled positioning model, the BDS-3 four-frequency observation is introduced to conduct a preliminary study on the performance of GPS/BDS-2/BDS-3/INS tightly coupled positioning. Taking into account the positioning accuracy and calculation efficiency of the tightly coupled positioning, single epoch extra-wide-lane/wide-lane (EWL/WL) observation is used to participate in the modeling of tightly coupled positioning. The EWL/WL ambiguity is solved by the geometry-free (GF) method, in which triple-frequency carrier ambiguity resolution (TCAR) and four-frequency carrier ambiguity resolution (FCAR) are used for triple-frequency and four-frequency observations, respectively. Finally, the positioning performance of the tightly coupled method in this paper is evaluated through vehicular experiment. The experimental results show that in the urban dynamic environment, due to the higher quality of the linear combination of BDS-3 four-frequency, the positioning accuracy of the BDS-3/INS tightly coupled was slightly better than that of the triple-frequency BDS-2/INS. Compared with GPS/BDS-2/INS, the GPS/BDS-2/BDS-3/INS tightly coupled positioning accuracy increased by 29.1% and 58.7% in horizontal and vertical directions, respectively, which can realize decimeter positioning accuracy in urban environments.

/pdf/improving-gnss-ins-tightly-coupled-positioning-by-using-bds-10w1mmh8.pdf

Improving GNSS/INS Tightly Coupled Positioning by Using BDS-3 Four-Frequency Observations in Urban Environments

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