Moon-Beom Heo

GNSS integration with vision-based navigation for low GNSS visibility conditions

In urban canyons, buildings and other structures often block the line of sight of visible Global Navigation Satellite System (GNSS) satellites, which makes it difficult to obtain four or more satellites to provide a three-dimensional navigation solution. Previous studies on this operational environment have been conducted based on the assumption that GNSS is not available. However, a limited number of satellites can be used with other sensor measurements, although the number is insufficient to derive a navigation solution. The limited number of GNSS measurements can be integrated with vision-based navigation to correct navigation errors. We propose an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GNSS measurements. An integrated model was designed to apply the GNSS range and range rate to vision-based navigation. The possibility of improved navigation performance was evaluated during an observability analysis based on available satellites. According to the observability analysis, each additional satellite decreased the number of unobservable states by one, while vision-based navigation always has three unobservable states. A computer simulation was conducted to verify the improvement in the navigation performance by analyzing the estimated position, which depended on the number of available satellites; additionally, an experimental test was conducted. The results showed that limited GNSS measurements can improve the positioning performance. Thus, our proposed method is expected to improve the positioning performance in urban canyons.

GPS/DR Error Estimation for Autonomous Vehicle Localization

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

Selective Integration of GNSS, Vision Sensor, and INS Using Weighted DOP Under GNSS-Challenged Environments

Accurate and precise navigation solution can be obtained by integrating multiple sensors such as global navigation satellite system (GNSS), vision sensor, and inertial navigation system (INS). However, accuracy of position solutions under GNSS-challenged environment occasionally degrades due to poor distributions of GNSS satellites and feature points from vision sensors. This paper proposes a selective integration method, which improves positioning accuracy under GNSS-challenged environments when applied to the multiple navigation sensors such as GNSS, a vision sensor, and INS. A performance index is introduced to recognize poor environments where navigation errors increase when measurements are added. The weighted least squares method was applied to derive the performance index, which measures the goodness of geometrical distributions of the satellites and feature points. It was also used to predict the position errors and the effects of the integration, and as a criterion to select the navigation sensors to be integrated. The feasibility of the proposed method was verified through a simulation and an experimental test. The performance index was examined by checking its correlation with the positional error covariance, and the performance of the selective navigation was verified by comparing its solution with the reference position. The results show that the selective integration of multiple sensors improves the positioning accuracy compared with nonselective integration when applied under GNSS-challenged environments. It is especially effective when satellites and feature points are posed in certain directions and have poor geometry.

GBAS ionospheric anomaly monitoring strategy using Kullback-Leibler divergence metric

A significant anomaly of great importance for ground-based augmentation systems (GBAS) is the steep gradient in ionospheric delays caused by rare ionospheric storms. To avoid such hazardous errors, code-carrier divergence (CCD) monitors have been applied to support the use of GBAS in Category I (CAT I) operation. However, to meet the stricter requirements for CAT II/III operation, it may be necessary to greatly improve the speed with which existing CCD monitors detect ionospheric gradients or develop an innovative divergence monitor. One way of obtaining stricter integrity is to detect anomalous divergence behavior more rapidly than the CCD monitor does. Therefore, we attempted to devise a divergence monitor in which the detection time is reduced by using the Kullback-Leibler information. In simulations and experimental results, we demonstrate that the proposed divergence measure is quite capable of detecting ionospheric anomalies that are hazardous to GBAS and is a very promising alternative for the detection of deviations. A comparison with existing CCD monitors shows its increased fault detectability, which enables it to sense even small ionospheric gradients more rapidly than typical monitors.

GPS Integrity Monitoring Method Using Auxiliary Nonlinear Filters with Log Likelihood Ratio Test Approach

Reliability is an essential factor in a navigation system. Therefore, an integrity monitoring system is considered one of the most important parts in an avionic navigation system. A fault due to systematic malfunctioning definitely requires integrity reinforcement through systematic analysis. In this paper, we propose a method to detect faults of the GPS signal by using a distributed nonlinear filter based probability test. In order to detect faults, consistency is examined through a likelihood ratio between the main and auxiliary particle filters (PFs). Specifically, the main PF which includes all the measurements and the auxiliary PFs which only do partial measurements are used in the process of consistency testing. Through GPS measurement and the application of the autonomous integrity monitoring system, the current study illustrates the performance of the proposed fault detection algorithm

Automated determination of fault detection thresholds for integrity monitoring algorithms of GNSS augmentation systems

The reference stations of GNSS augmentation systems including GBAS (Ground Based Augmentation System) and SBAS (Space Based Augmentation System) are designed to provide correction and integrity information for airborne users so that they can estimate the positions accurately and reliably using GNSS signal measurements. Therefore the integrity monitoring algorithms of the reference stations should detect and exclude GNSS signals with faults or anomalies which could cause user position errors. Normally, the detection thresholds are determined based on the nominal data collected during the site assessment and installation process assuming that there will be no critical environmental changes that impact on the test statistics of integrity monitoring algorithms. However, the characteristics of the test statistics could change in case of new constructions or RF interference nearby, which could induce higher continuity and integrity risk than allocated to the monitor. In order to prevent it and establish updated thresholds, this paper proposes an automated fault detection threshold determination procedure and environment change indicators. Examples with real GPS data show that the proposed procedure detects the environment change and determines new thresholds successfully.

GNSS Carrier Phase Anomaly Detection and Validation for Precise Land Vehicle Positioning

A carrier phase anomaly detection and validation method is proposed for precise vehicle positioning in dynamic environments. Given that carrier phase measurement is affected by satellite and user dynamics as well as unexpected anomalies, we propose four sequential processes to detect and identify an anomaly: 1) dynamics separation; 2) anomaly detection; 3) validation; and 4) position domain test. First, terms related to the satellite and user dynamics are estimated individually and removed from the carrier phase measurement to yield an error term, which possibly includes an anomaly and should be detected. The error term is examined with respect to a threshold level, and the measurement values are divided into anomaly candidates and normal candidates. Anomaly candidates are reexamined and validated one-by-one using a normal measurement set and the anomaly is determined. Finally, the position domain is evaluated so that position errors can be used as additional criteria for detecting the anomaly attributed to satellite deployment. The proposed algorithm is verified by simulation analyses and an experimental test. The proposed methods can be effective in detecting anomalies and increasing the reliability of precise vehicle positioning.

Performance Improvement of Inertial Navigation System by Using Magnetometer with Vehicle Dynamic Constraints

A navigation algorithm is proposed to increase the inertial navigation performance of a ground vehicle using magnetic measurements and dynamic constraints. The navigation solutions are estimated based on inertial measurements such as acceleration and angular velocity measurements. To improve the inertial navigation performance, a three-axis magnetometer is used to provide the heading angle, and nonholonomic constraints (NHCs) are introduced to increase the correlation between the velocity and the attitude equation. The NHCs provide a velocity feedback to the attitude, which makes the navigation solution more robust. Additionally, an acceleration-based roll and pitch estimation is applied to decrease the drift when the acceleration is within certain boundaries. The magnetometer and NHCs are combined with an extended Kalman filter. An experimental test was conducted to verify the proposed method, and a comprehensive analysis of the performance in terms of the position, velocity, and attitude showed that the navigation performance could be improved by using the magnetometer and NHCs. Moreover, the proposed method could improve the estimation performance for the position, velocity, and attitude without any additional hardware except an inertial sensor and magnetometer. Therefore, this method would be effective for ground vehicles, indoor navigation, mobile robots, vehicle navigation in urban canyons, or navigation in any global navigation satellite system-denied environment.

Analysis of GNSS Performance Index Using Feature Points of Sky-View Image

When sky-view factor (SVF) is used to predict the positioning performance of the global navigation satellite system (GNSS), it is easy to use the SVF as a performance index without a specific database, as it is used for topographic maps, not only in an open-sky land but also in regions where there are many tall buildings. However, conventional SVF is only able to express the degree of openness of the sky as a ratio, and it is limited to being used as a performance index for the positioning that uses the GNSS. When the conventional SVF is used in a land transportation environment, the predicted value for the positioning performance of the GNSS is often not consistent with the actual positioning error, but when sky-view-based dilution of precision (SVDOP) is applied, we confirmed that it was substantially close to the actual positioning error. This confirms our expectation that the utilization of the proposed method rather than the utilization of SVF alone in land transportation environments will make the analysis easier. In this paper, the SVDOP is calculated with real Global Positioning System data, and its usefulness is validated by comparing it with the conventional SVF and the DOP.

GNSS Precise Positioning Design for Intelligent Transportation System

In this paper, a structure of precise positioning based on satellite navigation system is proposed. The proposed system is consisted with three parts, range domain filter, navigation filter and position domain filter. The range domain filter generates carrier phase-smoothed-Doppler and Doppler-smoothed-code measurements. And the navigation filter calculates position and velocity using double-differenced code/carrier phase/Doppler measurements. Finally, position domain filter smooth position error, and it means enhancement of positioning performance. The proposed positioning method is evaluated by trajectory analysis using precise map date. As a result, the position error occurred by multipath or cycle slip was reduced and the calculated trajectory was in true lane.