scholarly journals A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5149 ◽  
Author(s):  
Bingbing Gao ◽  
Gaoge Hu ◽  
Xinhe Zhu ◽  
Yongmin Zhong
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Intelligent Transportation Systems ◽  
System Integration ◽  
Satellite System ◽  
Transportation Systems ◽  
Scaling Factor ◽  
Cubature Kalman Filter ◽  
Noise Covariance ◽  
Global Navigation Satellite

INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations. The proposed robust CKF can effectively resist the influence of abnormal observations on navigation solution and thus improves the robustness of CKF for vehicular INS/GNSS integration. Simulation and experimental results have demonstrated the effectiveness of the proposed robust CKF for vehicular navigation with INS/GNSS integration.

A Cooperative Positioning Scheme Based on Extended Kalman Filter for Curve Warning Systems

2014 ◽  
Vol 915-916 ◽  
pp. 1189-1193 ◽  
Author(s):  
Lei Du ◽  
Nan Liu ◽  
Rui Fang ◽  
Xiang Hui Song
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Intelligent Transportation Systems ◽  
Black Spot ◽  
Satellite System ◽  
Transportation Systems ◽  
Cooperative Positioning ◽  
Roadside Unit ◽  
Time Position ◽  
Global Navigation Satellite

Cooperative positioning (CP) is one of the core features in intelligent transportation systems (ITS) which is used to increase the positioning accuracy via wireless communication between vehicles and infrastructures. The global navigation satellite system (GNSS) is always unavailable near black spot such as the curve which needs to be solved. So, in this paper, a novel CP scheme is proposed for the curve warning scenario with limited GNSS by utilizing the information of received signal strength and pointer angular of the roadside unit which is in a special dual-transmitter outphasing architecture. An extended Kalman filter is founded to estimate the real-time position of the vehicle in the curve section. The whole warning scenario is analyzed by computer simulation, and the result shows the feasibility of the method.

scholarly journals Improvement of Anomalous Behavior Detection of GNSS Signal Based on TDNN for Augmentation Systems

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3800 ◽  
Author(s):  
Daehee Kim ◽  
Jeongho Cho
Keyword(s):  
Intelligent Transportation Systems ◽  
Satellite System ◽  
Anomalous Behavior ◽  
Transportation Systems ◽  
Civil Aviation ◽  
Integrity Monitoring ◽  
Delayed Neural Networks ◽  
Stringent Performance ◽  
Integrity Assurance ◽  
Global Navigation Satellite

The reliability of a navigation system is crucial for navigation purposes, especially in areas where stringent performance is required, such as civil aviation or intelligent transportation systems (ITSs). Therefore, integrity monitoring is an inseparable part of safety-critical navigation applications. The receiver autonomous integrity monitor (RAIM) has been used with the global navigation satellite system (GNSS) to provide integrity monitoring within avionics itself, such as in civil aviation for lateral navigation (LNAV) or the non-precision approach (NPA). However, standard RAIM may not meet the stricter aviation availability and integrity requirements for certain operations, e.g., precision approach flight phases, and also is not sufficient for on-ground vehicle integrity monitoring of several specific ITS applications. One possible way to more clearly distinguish anomalies in observed GNSS signals is to take advantage of time-delayed neural networks (TDNNs) to estimate useful information about the faulty characteristics, rather than simply using RAIM alone. Based on the performance evaluation, it was determined that this method can reliably detect flaws in navigation satellites significantly faster than RAIM alone, and it was confirmed that TDNN-based integrity monitoring using RAIM is an encouraging alternative to improve the integrity assurance level of RAIM in terms of GNSS anomaly detection.

scholarly journals A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2491
Author(s):  
Mauro Tropea ◽  
Angelo Arieta ◽  
Floriano De Rango ◽  
Francesco Pupo
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Propagation Delay ◽  
Satellite System ◽  
Transportation Systems ◽  
Navigation Systems ◽  
Vehicle Positioning ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite

Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.

A Cooperative Positioning Scheme Based on Beam-Forming for Intelligent Transportation Systems

2014 ◽  
Vol 945-949 ◽  
pp. 3255-3259 ◽  
Author(s):  
Lei Du ◽  
Nan Liu ◽  
Rui Fang ◽  
Nan Li ◽  
Xiang Hui Song
Keyword(s):  
Wireless Communication ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Least Squares Method ◽  
Satellite System ◽  
Transportation Systems ◽  
Beam Forming ◽  
Main Process ◽  
Cooperative Positioning ◽  
Global Navigation Satellite

Cooperative positioning (CP) originating in wireless sensor networks (WSN) is expected to enhance the accuracy of real-time positioning by exchanging location related information in vehicular network via wireless communication. A novel CP system based on beam-forming for vehicular networks is proposed by this work. Its application includes several roadside units equipped with a kind of transceiver based on an special dual-transmitter outphasing architecture which are utilized to broadcast the spatial directivity and correct receive angle information to vehicles with onboard wireless communication units in desired areas. The goal of enhancement positioning via vehicle-to-infrastructure communication can be acquired by a data fusion means based on the extended Kalman filter when GNSS is available and a cooperative solution based on the least-squares method under the condition that the global navigation satellite system (GNSS) is available respectively. The main process of positioning and all the key technical points of the system's application are modeled and analyzed mathematically. And the results of computer simulation confirm the technical practicability for the proposed method.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 280
Author(s):  
Farzan Farhangian ◽  
Hamza Benzerrouk ◽  
Rene Landry
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Flight Test ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Measurement Unit ◽  
Starting Point ◽  
Leo Satellites ◽  
Positioning Method ◽  
Global Navigation Satellite

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

scholarly journals C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 739 ◽  
Author(s):  
Shiming Liu ◽  
Sihai Li ◽  
Jiangtao Zheng ◽  
Qiangwen Fu ◽  
Yanhua Yuan
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Signal Amplitude ◽  
Satellite System ◽  
Static Field ◽  
Weak Signal ◽  
Important Indicator ◽  
Vector Receiver ◽  
Averaging Time ◽  
Global Navigation Satellite

The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.

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