Land Vehicle Navigation System Research Articles

A Hybrid RISS/GNSS Method During GNSS Outage in the Land Vehicle Navigation System

The integrated navigation system of an inertial navigation system (INS) and a global navigation satellite system (GNSS) is a standard solution in land vehicle navigation applications. Considering a low-cost solution, the reduced inertial sensor system (RISS) is adopted in place of INS to provide better navigation performance for land vehicles with fewer inertial sensors and lower computations. However, low-cost sensors can quickly deteriorate the navigation solution during GNSS outage. Hence, a novel RISS /GNSS method with the assistance of the long short-term memory (LSTM) neural network (NN), which has the ability of adaptive memorizing, is proposed to bridge GNSS outage by means of data fusion. In addition, zero-velocity detection is applied to advance the navigation performance provided by the LSTM algorithm during GNSS outage. We examined the performance of this method by using real road test experiments in a land vehicle equipped with GNSS receivers and inertial sensors in addition to a high-end GNSS /INS to provide the reference solution. During 300-s GNSS outage, the experimental results illustrate that this hybrid method based on the LSTM algorithm can enhance the navigation accuracy by 50% when compared with the standalone RISS algorithm, and provide 30% improvements in comparison with the nonlinear autoregressive with exogenous input (NARX) algorithm.

A New Robust Filtering Method of GNSS/MINS Integrated System for Land Vehicle Navigation

Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> . The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists.

Land Vehicle Navigation Using Low-Cost Integrated Smartphone GNSS Mems and Map Matching Technique

Abstract The demand for smartphone positioning has grown rapidly due to increased positioning accuracy applications, such as land vehicle navigation systems used for vehicle tracking, emergency assistance, and intelligent transportation systems. The integration between navigation systems is necessary to maintain a reliable solution. High-end inertial sensors are not preferred due to their high cost. Smartphone microelectromechanical systems (MEMS) are attractive due to their small size and low cost; however, they suffer from long-term drift, which highlights the need for additional aiding solutions using road network that can perform efficiently for longer periods. In this research, the performance of the Xiaomi MI 8 smartphone’s single-frequency precise point positioning was tested in kinematic mode using the between-satellite single-difference (BSSD) technique. A Kalman filter algorithm was used to integrate BSSD and inertial navigation system (INS)-based smartphone MEMS. Map matching technique was proposed to assist navigation systems in global navigation satellite system (GNSS)-denied environments, based on the integration of BSSD–INS and road network models applying hidden Marcov model and Viterbi algorithm. The results showed that BSSD–INS–map performed consistently better than BSSD solution and BSSD–INS integration, irrespective of whether simulated outages were added or not. The root mean square error (RMSE) values for 2D horizontal position accuracy when applying BSSD–INS–map integration improved by 29% and 22%, compared to BSSD and BSSD–INS navigation solutions, respectively, with no simulated outages added. The overall average improvement of proposed BSSD–INS–map integration was 91%, 96%, and 98% in 2D horizontal positioning accuracy, compared to BSSD–INS algorithm for six GNSS simulated signal outages with duration of 10, 20, and 30 s, respectively.

Motion model-assisted GNSS/MEMS-IMU integrated navigation system for land vehicle

Motion model-assisted GNSS/MEMS-IMU integrated navigation system for land vehicle

Implementation of a MEMS-Based GNSS/INS Integrated Scheme Using Supported Vector Machine for Land Vehicle Navigation

The performance of the integrated global navigation satellite system (GNSS) and inertial navigation system (INS) has been proven to be more accurate, reliable, and continuous than stand-alone systems. The development of micro-electronic mechanical system (MEMS) enables inertial measurement unit (IMU) to meet the low-cost and small-size requirements of vehicular navigation. However, the stochastic error characteristics of the inertial sensors and the instability caused by the GNSS signal outages pose a threat to the MEMS-based GNSS/INS land vehicle navigation system. Within this context, we propose the following two-step GNSS/INS integrated architecture at two levels: 1) enhancing the signal-to-noise ratio (SNR) of MEMS-INS raw measurements utilizing a hybrid denoising algorithm with wavelet transform and support vector machine (SVM) and 2) improving the positioning accuracy by a SVM-based data fusion approach which could predict the accumulated error of the MEMS inertial sensors during the GNSS outages. A rotation platform experiment and a field test were carried out, which suggests that the proposed method can effectively eliminate the stochastic errors of MEMS-IMU, and significantly improve the overall positioning accuracy in land vehicle navigation.

INVESTIGATION OF DIFFERENT LOW-COST LAND VEHICLE NAVIGATION SYSTEMS BASED ON CPD SENSORS AND VEHICLE INFORMATION

Abstract. Recently, many companies and research centres have been working on research and development of navigation technologies for self-driving cars. Many navigation technologies were developed based on the fusion of various sensors. However, most of these techniques used expensive sensors and consequently increase the overall cost of such cars. Therefore, low-cost sensors are now a rich research topic in land vehicle navigation. Consumer Portable Devices (CPDs) such as smartphones and tablets are being widely used and contain many sensors (e.g. cameras, barometers, magnetometers, accelerometers, gyroscopes, and GNSS receivers) that can be used in the land vehicle navigation applications.This paper investigates various land vehicle navigation systems based on low-cost self-contained inertial sensors in CPD, vehicle information and on-board sensors with a focus on GNSS denied environment. Vehicle motion information such as forward speed is acquired from On-Board Diagnosis II (OBD-II) while the land vehicle heading change is estimated using CPD attached to the steering wheel. Additionally, a low-cost on-board GNSS/inertial integrated system is also employed. The paper investigates many navigation schemes such as different Dead Reckoning (DR) systems, Reduced Inertial Sensor System (RISS) based systems, and aided loosely coupled GNSS/inertial integrated system.An experimental road test is performed, and different simulated GNSS signal outages were applied to the data. The results show that the modified RISS system based on OBD-II velocity, onboard gyroscopes, accelerometers, and CPD-based heading change provides a better navigation estimation than the typical RISS system for 90s GNSS signal outage. On the other hand, typical inertial aided with CPD heading change, OBD-II velocity updates, and Non-Holonomic Constraint (NHC) provide the best navigation result.

A Robust Fusion Methodology for MEMS-Based Land Vehicle Navigation in GNSS-Challenged Environments

How to effectively blend global navigation satellite system (GNSS) and inertial navigation system (INS) data to achieve an optimal solution in harsh environments has always been an urgent task. The main challenges for low-cost GNSS/INS integrated land vehicle navigation system are poor accuracy of GNSS observations in complex urban environments and time-growing position errors of stand-alone micro-electro mechanical system (MEMS)-based inertial sensors during GNSS outages. This paper aims to enhance the positioning accuracy and reliability of low-cost integrated system. To attain this, we propose a two-tier robust fusion scheme with different aspects: 1) The GNSS and INS information are fused through a support vector regression-based adapted Kalman filter (SVR-AKF), with which scaling factors are generated to tune the covariance parameters of KF. 2) The position errors of MEMS-INS during GNSS outages are predicted and compensated by modeling INS error characteristics utilizing an adaptive neuro fuzzy inference system (ANFIS) due to its effectiveness in dealing with the nonlinear and uncertainty problems. To verify the feasibility of the proposed methodology, experimental road tests were performed, which suggested that the proposed methodology can significantly improve the overall reliability and positioning performance of land vehicle navigation in GNSS-challenged environments.

A GNSS/INS-integrated system for an arbitrarily mounted land vehicle navigation device

For the traditional implementation of inertial navigation system, aligning the inertial sensor axes with the vehicle body frame is a necessary process. While the development of micro-electromechanical system brings considerable cost and size advantages, the undesirable alignment process is still a challenge for widespread civil use of portable inertial devices. Aimed to avoid complicated manual mounting of an inertial device used in land vehicle navigation systems, an algorithm is proposed to automatically estimate the misalignment angles between the sensor platform and the vehicle body frame, which enables the device to be mounted in an arbitrary orientation. The foundation of this method is formed by two facts. The first is that the accelerometer can exactly estimate its own posture when it is stationary on the platform. The second is the yaw calculated from the horizontally aligned device has a constant error when the horizontal component of the angular velocity is zero. A robust motion mode recognition technique, which compares the statistical characteristics of the measurements with an empirical threshold, is applied to detect whether the vehicle is parking, turning, or moving straight. Validation experiments show that the error of the coarse estimation algorithm is within 2° when the heading misalignment is less than 45°. This guarantees that the arbitrarily mounted device achieves the equivalent performance as the well-aligned one, whenever the global navigation satellite system (GNSS) signal is available. In addition, the positioning error of the misaligned device during short GNSS signal blockage is within 7 m with the application of auxiliary velocity updates.

Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles

When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.

Integrated GPS/DR Vehicle Navigation System Based on Sequential and Square Root Kalman Filters

Global Positioning System (GPS) has become part of many applications in life. In mountainous terrains and around buildings, GPS reception is compromised. In dense urban canyons, signals bounce off the buildings creating multipath reception and provide erroneous measurements. To overcome GPS bandwidth and signal fading problems, Navigation solutions are built on GPS measurements fused with inertial sensors to provide dead reckoning (DR) based position solution. Solution for land vehicle Navigation System using GPS, inertial sensor and odometer is presented. The sensors fusion is performed based on conventional, sequential (SKF) and square root Kalman (SRKF) filters. SRKF based on Cheolesky factorization for covariance matrix P. Simulations are performed on real data, with precisely known covariance’s to simulate mathematical stability, performance and processing time required by each method on a high end microprocessor. The results demonstrate integrated system using SRKF has better performance in stability and estimation accuracy than conventional and sequential filter.

Magnetometer Error Models of Low-Cost Land Vehicle Navigation System

This article implies a method of magnetometer error determination. Using solid-state magnetic sensors, vehicle odometer and GPS sensor, low-cost complex navigation system for land vehicles can be realized. The methods and algorithms of magnetometer calibration are shown. Results of magnetometer calibration experiments and possibility to detect magnetometer error parameters in process of car driving are analysed. To acquire vehicle heading output, navigation system described here is developed. DOI: http://dx.doi.org/10.5755/j01.eie.22.6.17225

The use of odometer in land vehicle navigation system

The paper analyzes the principles of integrated navigation system of land vehicle. There are shown some technical schemes and algorithmic solutions that provide combination of odometer sensor with inertial and satellite navigation systems. Experiment results of integrated system, designed for installation on lift truck, carrying cargo at seaport, are shown.

An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors.

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance.

An integrated land vehicle navigation system based on context awareness

In the complex urban environments, land vehicle navigation purely relying on GNSS cannot satisfy user needs due to the loss of satellite signals caused by obstructions such as buildings, tunnels, and trees. To solve this problem, we introduce a GPS-/MSINS-/magnetometer-integrated urban navigation system based on context awareness. In this system, the data from the Micro Strapdown Inertial Navigation System (MSINS) are used to analyze and detect the context knowledge of vehicles, whose sensor errors can be compensated by the heuristic drift reduction algorithm for different motion situations. When GPS is available, the vehicle position can be estimated by unscented Kalman Filter, whereas in the case of GPS outages, the vehicle attitude is provided by an attitude and heading reference system and the motion constraints-aided algorithm is used to complete the positioning. In the experiment validation, the integrated navigation system is set up by low-cost inertial sensors. The result shows that the proposed system can achieve high accuracy when GPS is available. For most of the time without GPS, the system can guarantee the positioning precision of 10 m and compensate the errors of MSINS effectively, which fully satisfies positioning needs in complex urban environments.

A Simple Observer for Gyro and Accelerometer Biases in Land Navigation Systems

In various applications of land vehicle navigation and automatic guidance systems, Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning performance crucially depends on the attitude determination accuracy affected by gyro and accelerometer bias instabilities. Traditional bias estimation approaches based on the Kalman filter suffer from implementation complexity and require non-intuitive tuning procedures. In this paper we propose, as an alternative, a simple observer that estimates inertial sensor biases exclusively in terms of quantities with obvious geometrical meaning. By this, any multidimensional vector-matrix operations are avoided and observer tuning is substantially simplified. The observer has been successfully tested in a farming vehicle navigation system.

An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

Application of Kalman Filter Technology to Integrated INS/DR Navigation System for Land Vehicle

Strap-down INS is composed of fiber gyroscope. Position error propagation equation and position update algorithm of dead reckoning is deduced in this paper. The Kalman filter is proposed for compensation error of integrated system. The difference of velocity between INS and DR is used as the input of Kalman filter, attitude error, velocity error, position error and scale factor error are to be estimated which compensate and rectify the errors of integrated navigation system. By carrying out experiment upon vehicular navigation system in use of Kalman filter, the errors of integrated navigation system are estimated accurately. Experiment result show that the method not only can effectively improve precision of the system, but also is simple and convenient, so it is more suitable for practical application.

GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Scoring Scheme

Global navigation satellite system (GNSS) positioning is widely used in land vehicle and pedestrian navigation systems. Nevertheless, in urban canyons GNSS remains inaccurate due to building blockages and reflections, especially in the cross-street direction. Shadow matching is a new technique, recently proposed for improving the cross-street positioning accuracy using a 3D model of the nearby buildings. This paper presents a number of advances in the shadow-matching algorithm. First, a positioning algorithm has been developed, interpolating between the top-scoring candidate positions. Furthermore, a new scoring scheme has been developed that accounts for signal diffraction and reflection. Finally, the efficiency of the process used to generate the grid of building boundaries used for predicting satellite visibility has been improved. Real-world GNSS data has been collected at 22 different locations in central London to provide the first comprehensive and statistical performance analysis of shadow matching. © 2013 Institute of Navigation.

An Integrated GPS/INS Navigation System for Land Vehicle

A precise guided system needs an efficient control depending on a precise navigation algorithm, with the ability of getting an accurate initial attitude determination to guarantee the mission success. A navigation system is presented in this paper based on integration between inertial measuring unit and Global Positioning System via Kalman filter approach to satisfy an acceptant accuracy. The two well known Euler and Quaternion attitude determination techniques are implemented to evaluate the body orientation during motion. The carried out system is validated using both simulation data and experimental work. The simulation data is obtained using a six-degree-of-freedom model for a 122mm artillery rocket to obtain all ballistic trajectory parameters during flight. The experimental work is done using a land vehicle taking into consideration the initial attitude determination problem. The results showed high accuracy improvements with high data rates 200 Hz for full state navigation information (position, velocity and attitude).

An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.