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

With the rise of location services applications, the need for navigation and positioning extends from isolated environments to a seamless, full domain. Aiming at the problem of low positioning accuracy in the transition area between indoor and outdoor, and it is difficult to achieve smooth switching, this article proposes an indoor and outdoor seamless positioning system for smartphones based on environment perception. Firstly, based on the hidden Markov model (HMM), we use smartphone observation data to select optimal features to construct an environmental perception model. Secondly, by increasing the magnetic field strength as an observation feature, the time delay for switching between different environments is shortened. Finally, based on the environmental perception results, a seamless indoor and outdoor positioning system is constructed. In the transition area between indoor and outdoor, to ensure the reliability of the positioning system, a Global Navigation Satellite System/ Inertial Navigation System/ Bluetooth Low Energy (GNSS/INS/BLE) integrated positioning model is proposed. In the outdoor and outdoor transition areas, we refine the stochastic model, which significantly improves the accuracy. To comprehensively evaluate the system performance, experiments are carried out in a complex environment. The experimental results show that the proposed perception model can accurately identify the changes of the environment, and the accuracy of judgment reaches more than 95%. It can support the seamless positioning system to select appropriate positioning models in different environments to achieve accurate and continuous positioning. The maximum positioning error does not exceed 0.5m. 

Environment Perception Based Seamless Indoor and Outdoor Positioning System of Smartphone

Conventional ambiguity resolution (AR) strategy of directly fixing all raw frequency ambiguities for uncombined precise point positioning (PPP) generally requires a long initial time to ensure fixing reliability. The rich signals from multi-frequency GNSS can provide new opportunities for rapid PPP AR. In this paper, based on a unified multi-frequency uncombined PPP model, the raw frequency ambiguities are linearly transformed to the between-satellite-single-differenced extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) ambiguities, and a cascading ambiguity resolution (CAR) method of fixing EWL/WL/NL ambiguities sequentially is proposed. Meanwhile, a partial ambiguity fixing (PAF) strategy with ambiguity subset adaptively selected based on the successively increased elevations is also adopted in each step to improve the fixing rate. Further, experiments with globally distributed stations are carried out to verify the algorithm. With the constraints of EWL/WL AR, the precision of NL ambiguity and its variance-covariance matrix can be effectively optimized, so the time to first fix (TTFF) is significantly shortened, and the ratio value is also improved to varying degrees. As for BDS-only solution, the average TTFF is shortened from 23.7 min to 10.9 min, with an improvement over 50%; for GPS/BDS, GPS/Galileo, and GPS/Galileo/BDS combined solutions, the TTFF is shortened from 14.3 min, 9.4 min, and 6.7 min to 8.1 min, 2.6 min, and 1.8 min, which are, respectively, shortened by 43.4%, 72.3%, and 73.1%. In general, the proposed CAR strategy can shorten the TTFF of multi-GNSS multi-frequency PPP to about 2 min. The performance of EWL/WL/NL ambiguity-fixed solutions is also analyzed. The NL solution is generally at centimeter-level accuracy over the entire period; however, limited by the atmosphere errors during the convergence stage, the EWL/WL solutions can only obtain decimeter-level accuracy, and the difference between them and NL solution gradually decreases with the continuous improvement of atmosphere accuracy. 

/pdf/multi-gnss-fast-precise-point-positioning-with-multi-2h7h7lfi.pdf

Multi-GNSS Fast Precise Point Positioning with Multi-Frequency Uncombined Model and Cascading Ambiguity Resolution

Conventional ambiguity resolution (AR) strategy of directly ﬁxing all raw frequency ambiguities for uncombined precise point positioning (PPP) generally requires a long initial time to ensure ﬁxing reliability. The rich signals from multi-frequency GNSS can provide new opportunities for rapid PPP AR. In this paper, based on a uniﬁed multi-frequency uncombined PPP model, the raw frequency ambiguities are linearly transformed to the between-satellite-single-diﬀerenced extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) ambiguities, and a cascading ambiguity resolution (CAR) method of ﬁxing EWL/WL/NL ambiguities sequentially is proposed. Meanwhile, a partial ambiguity ﬁxing (PAF) strategy with ambiguity subset adaptively selected based on the successively increased elevations is also adopted in each step to improve the ﬁxing rate. Further, experiments with globally distributed stations are carried out to verify the algorithm. With the constraints of EWL/WL AR, the precision of NL ambiguity and its variance-covariance matrix can be eﬀectively optimized, so the time to ﬁrst ﬁx (TTFF) is signiﬁcantly shortened, and the ratio value is also improved to varying degrees. As for BDS-only solution, the average TTFF is shortened from 23.7min to 10.9 min, with an improvement over 50%; for GPS/BDS, GPS/Galileo, and GPS/Galileo/BDS combined solutions, the TTFF is shortened from 14.3min, 9.4 min, and 6.7min to 8.1 min, 2.6min, and 1.8min, which are, respectively, shortened by 43.4%, 72.3%, and 73.1%. In general, the proposed CAR strategy can shorten the TTFF of multi-GNSS multi-frequency PPP to about 2 min. The performance of EWL/WL/NL ambiguity-ﬁxed solutions is also analyzed. The NL solution is generally at centimeter-level accuracy over the entire period; however, limited by the atmosphere errors during the convergence stage, the EWL/WL solutions can only obtain decimeter-level accuracy, and the diﬀerence between them and NL solution gradually decreases with the continuous improvement of atmosphere accuracy.

To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter

Quad-frequency signals have thus far been available for all satellites of BeiDou-3 and Galileo systems. The major benefit of quad-frequency signals is that more extra-wide-lane (EWL) combinations can be formed with quad-frequency than with triple- or dual-frequency, of which the ambiguities can be fixed instantaneously in medium and long baselines. In this paper, the long-baseline positioning algorithm based on optimal triple-frequency EWL/wide-lane (WL) combinations of BeiDou-3 and Galileo is proposed. First, the theoretical precision of multi-frequency combinations of BeiDou-3 and Galileo is studied, and EWL/WL combinations with a small noise amplitude factor and a small ionospheric scalar factor are selected. Then, geometry-free methods are used to estimate the a priori precision of EWL/second EWL/WL signals for different combination schemes. Second, the double-differenced (DD) geometry-based function models of quad-frequency configurations and three different triple-frequency configurations are given, and the DD ionospheric delays are estimated as unknown parameters. In the end, the real BeiDou-3 and Galileo data are used to evaluate the positioning preference. The results show that, when using fixed EWL observations to constrain WL ambiguities, the proposed triple-frequency EWL/WL signals composed of (B1I,B3I,B2a) of BeiDou-3 and (E1,E5a,E6) of Galileo can achieve the same precision as the quad-frequency signals. Therefore, the method proposed in this article can realize long-baseline instantaneous decimeter-level positioning while reducing the dimension of matrix and improving calculation efficiency.

Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations

As one of the commonly used solutions for vehicular dynamic positioning, the stability of Global Navigation Satellite Systems (GNSS)/ Inertial Navigation System (INS) integrated positioning when applied in an urban environment is still struggling. Generally, carrier-phase ambiguity fixing requires continuous several epochs, the stability and continuity of integrated positioning are significantly reduced if the signal is frequently blocked. To reduce the impact of frequent signal blockage, an improved tightly-coupled algorithm is proposed in this paper. Firstly, a step-wise ambiguity processing method is introduced to form instantaneous fixed Wide-Lane (WL) observations for calibrating INS measurement. Secondly, by estimating the differential inter-system bias (DISB) parameter, the pivot satellite can be shared between different constellations, to increase the number of usable satellites under the limited observation conditions and improve the positioning performance. The proposed method is verified with vehicular experiments on semi-simulated and actual urban canyon scenarios. In the artificial GNSS outage experiment, in the situation of 4 satellites that can be observed in BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS), the positioning accuracy of the proposed algorithm can achieve $4.1cm$ horizontally and $15.2cm$ vertically. An improvement of 26.9% horizontally and 20.4% vertically is gained accordingly compared to the conventional method. In the actual urban environment experiment, in case of insufficient satellite and low signal-to-noise ratio (SNR), when the conventional method can no longer maintain a fixed solution of GNSS/INS integrated positioning, the proposed algorithm can still achieve an accuracy of $49.7cm$ horizontally and $67.0cm$ vertically.

/pdf/an-improved-single-epoch-gnss-ins-positioning-method-for-343xv1nkr3.pdf

An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation

Vehicular dynamic positioning based on tightly coupled (TC) Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integration in urban areas is due to either low accuracy of pseudorange or poor continuity of carrier phase, resulting in insufficient positioning performance. To enhance the stability while ensuring positioning accuracy, this paper proposed a tightly coupled Beidou Navigation Satellite System (BDS)/INS integration scheme by improving measurement modelling with triple-frequency observations: first, a stepwise single-epoch ambiguity resolution of extra-wide-lane (EWL)/wide-lane (WL) combined observations and then modelling the measurement equation with fixed WL observation instead of conventional pseudorange or carrier phase. Experiments were carried out for verification with data collected in real traffic by a measurement vehicle. The proposed method achieved single-epoch output with an RMS statistical accuracy of decimetre level of 0.152 m horizontally and 0.196 m vertically. The signal outage experiment verified that the proposed algorithm is restoring high-accuracy positioning output in single-epoch once the signal is recaptured. The proposed method obtained a positioning accuracy improvement of 43.6% horizontally and 6.2% vertically in signal outage sections compared to the conventional method. This avoids the multiepoch ambiguity searching to fix with conventional carrier-phase processing, thereby improving the positioning stability.

/pdf/a-tightly-coupled-bds-ins-integrated-positioning-algorithm-3eqpl43cd5.pdf

A Tightly Coupled BDS/INS Integrated Positioning Algorithm Based on Triple-Frequency Single-Epoch Observations

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Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations

An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation

A Tightly Coupled BDS/INS Integrated Positioning Algorithm Based on Triple-Frequency Single-Epoch Observations