Design and Performance Evaluation of the Improved INS-Assisted Vector Tracking for the Multipath in Urban Canyons

Yan, Zhe; Chen, Xiyuan; Tang, Xinhua; Zhu, Xuefen

doi:10.1109/tim.2022.3204107

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…Currently, global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) are the most typical and widely used navigation methods in the field of the Internet of Things (IoT), such as the Internet of Vehicles (IoV) [1][2][3][4]. However, as a result of the rapid developments in bionic technology [5], some new types of bionic autonomous navigation technologies have emerged that are based on biological principles and can help unmanned platforms including unmanned aerial vehicles (UAVs) [6], unmanned vehicles (UVs) [7][8][9], and autonomous underwater vehicles (AUVs) to realize autonomous navigation.…”

Section: Introductionmentioning

confidence: 99%

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Zhao,

Shen,

Cao

et al. 2024

Micromachines

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The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

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Section: Introductionmentioning

confidence: 99%

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Zhao,

Shen,

Cao

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

“…They have achieved this by developing new signal processing methods, optimizing satellite orbit prediction models, and refining clock calibration algorithms [19,20]. Previous studies [21][22][23] have focused on the pseudo-range positioning algorithm. However, the accuracy of pseudo-range positioning is limited to approximately 1 m [24], whereas precision single-point positioning based on carrier-phase measurement can achieve milli-meter-level accuracy [25].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

Xie,

Zhang,

Wang

2024

Agriculture

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Integrating modern information technology with traditional agriculture has made agricultural machinery navigation essential in PA (precision agriculture). However, agricultural equipment faces challenges such as low positioning accuracy and poor algorithm adaptability due to the complex farmland environment and various operational requirements. In this research, we proposed a generalized ranging theoretical framework with multi-scenario variable-state fusion to improve the GNSS (Global Navigation Satellite System) observation exchange performance among agricultural vehicles, and accurately measure IVRs (inter-vehicular ranges). We evaluated the effectiveness of three types of GNSS observations, including PPP-SD (precise single point positioning using single difference), PPP-TCAR (precise single point positioning using double difference based on three-carrier ambiguity resolution), and PPP-LAMBDA (precise single point positioning using double difference based on least-squares ambiguity decorrelation adjustment). Moreover, we compared the accuracy of IVRs measurements. Our framework was validated through field experiments in different scenarios. It provides insights into the appropriate use of different positioning algorithms based on the application scenario, application objects, and motion states.

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“…Recent work presented a graph Fourier transform (GFT) filter denoising the complex correlator outputs to replace the old GNSS tracking loop, which can be considered a direct way to steer the code phase in challenging cases [9]. Except for the separation using the state-of-the-art SRAs or the GNSS antenna changes [10][11][12], modeling the superposition signals formed with LOS and NLOS rays is mainstream in the current GNSS community as a means to overcome multipath interference [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios

et al. 2023

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Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desirable way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Inertial navigation systems (INSs) have been used as effective complementary dead reckoning (DR) sensors for GNSS receivers in kinematic scenarios, resisting various forms of interference. However, little work has focused on whether INSs can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information and integrated solutions of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Results of the time-of-arrival (TOA) and positioning based on real-world experiments demonstrated the proposed SDR.

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Design and Performance Evaluation of the Improved INS-Assisted Vector Tracking for the Multipath in Urban Canyons

Cited by 10 publications

References 46 publications

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios

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