Dae Hee Won

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.

Weighted DOP With Consideration on Elevation-Dependent Range Errors of GNSS Satellites

This paper proposes the weighted dilution of precision (WDOP) with consideration of the satellite elevation angle in order to improve the performance of dilution of precision (DOP), which is a standard tool to quantify the positional precision of the Global Navigation Satellite System (GNSS). The WDOP is calculated by assigning different weights to visible GNSS satellites depending on their elevation angles. In order to demonstrate the effectiveness of WDOP, the conventional DOP and WDOP were mathematically analyzed and a comparative analysis was conducted using actual Global Positioning System data. Results showed that WDOP represents the position error trends more accurate than the conventional DOP, particularly when low-elevation measurements were used for positioning calculation. Therefore, the WDOP could be a promising replacement of DOP as a tool for representing and quantifying errors in GNSS positioning.

Improving mobile robot navigation performance using vision based SLAM and distributed filters

In this paper, we suggest a vision-based SLAM (simultaneous localization and mapping) method to improve navigation performance of mobile robot, which is used 2 encoders to calculate its position. If mobile robot is in building, tunnel or under ground facility, it is difficult to obtain navigation information from GPS only navigation system, because there are not enough visible GPS satellites. To overcome this limitation, DR (dead reckoning) system is required. However, as DR operation time goes by, the navigation error is increased because of accumulation of sensor error and noise. There are variety kinds of methods to reduce these errors. In this paper, we use a vision sensor and particle filter. Some clear points on vision sensor image are selected and tracked for error compensation. That is called a SLAM (simultaneous localization and mapping) method. In this paper, distributed particle filter is used to cope with nonlinear observation model and to deal with changing the number of measurements. Computer simulations are conducted to demonstrate the performance of suggested filter.

Orbit Ephemeris Failure Detection in a GNSS Regional Application

To satisfy civil aviation requirements using the Global Navigation Satellite System (GNSS), it is important to guarantee system integrity. In this work, we propose a fault detection algorithm for GNSS ephemeris anomalies. The basic principle concerns baseline length estimation with GNSS measurements (pseudorange, broadcasted ephemerides). The estimated baseline length is subtracted from the true baseline length, computed using the exact surveyed ground antenna positions. If this subtracted value differs by more than a given threshold, this indicates that an ephemeris anomaly has been detected. This algorithm is suitable for detecting Type A ephemeris failure, and more advantageous for use with multiple stations with various long baseline vectors. The principles of the algorithm, sensitivity analysis, minimum detectable error (MDE), and protection level derivation are described and we verify the sensitivity analysis and algorithm availability based on real GPS data in Korea. Consequently, this algorithm is appropriate for GNSS regional implementation.

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.

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

High Assurance GPS Integrity Monitoring System Using Particle Filtering Approach

The reliability of a navigation system is one of the critical requirements for air or automotive navigation. In order to increase the reliability of the navigation system, failures or faults resulting from the malfunctions of the system should be detected and repaired to keep the system possessing high integrity. This leads to a need for developing high assurance GPS monitoring system which should be an absolutely necessary part of the system. The particle filter (PF) is a method based on Monte Carlo technique designed for nonlinear and non- Gaussian state estimation. In this paper, the PF is employed to GPS signal integrity monitoring. The primary objective of this research is over the safety in navigation with GPS and possibility that a GPS satellite transmits an erroneous navigation signal to the user. The monitoring system has to provide timely warnings to the user and indicates when it should not be used.

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.

UKF based vision aided navigation system with low grade IMU

When integrating single vision sensor and low grade IMU for 6-DOP navigation, nonlinearity of observation model makes a problem to estimate position, velocity and attitude. Conventional Kalman Filter could not estimate states correctly because it uses linearized model. Due to these reasons, nonlinear estimation should be used to figure out the nonlinear characteristics. By applying Unscented Kalman Filter, this paper copes with the nonlinearity. The estimation performance is demonstrated by numerical simulation. The RMS error of estimated position is analyzed by comparing Extended Kalman Filter results.

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.