Je Young Lee

Adaptive GPS/INS integration for relative navigation

Abstract Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.

Simultaneous estimation of ionospheric delays and receiver differential code bias by a single GPS station

In this paper, we propose an efficient Kalman filter algorithm for simultaneous estimation of absolute ionospheric delay and receiver differential code bias. It is well known that the two quantities are mixed in global positioning system (GPS) measurements causing a rank-deficiency problem. The proposed method requires only the broadcast navigation message and basic measurements provided by a single dual-frequency GPS receiver. Thus, it would be convenient to monitor local ionospheric activities autonomously in real time with minimum hardware. An observability analysis was performed in detail why the simultaneous estimation is possible in spite of the rank deficiency occurring in epoch-by-epoch measurements. Experimental results based on real GPS measurements demonstrate the feasibility of the proposed method.

Lever Arm Error Compensation of GPS/INS Integrated Navigation by Velocity Measurements

In GPS(Global Positioning System)/INS(Inertial Navigation System) integrated navigation systems, GPS antennas and an inertial measurement units are usually installed outside and inside of the vehicle, respectively. By the difference of installed locations, performance of GPS/INS integrated navigation systems is affected by lever arm errors. For more accurate navigation, lever arm errors need to be compensated correctly since it directly affects the accuracy of navigation states. This paper proposes an effective lever arm error compensation method that utilizes velocity measurements of GPS and INS. By an experiment, feasibility of the proposed algorithm is verified. It is also shown that lever arm compensation is especially important when vehicles are experiencing rotational movements.

Relative Positioning of Vehicles Carrying Hazardous Materials Using Real-Time Kinematic GPS

It is well known that the accident of vehicles carrying hazardous materials incurs huge losses economically and socially. To detect and respond rapidly against the accident of a vehicle carrying hazardous materials, it is essential to estimate the relative navigation information between the forward tractor module and the backward trailer module of the vehicle reliably and accurately. In this paper, a precise relative positioning system based on GPS is designed, implemented, and evaluated as a prerequisite to design an effective relative navigation system for the vehicle carrying hazardous materials. An experiment using field-collected 10 Hz real GPS measurements showed that the designed relative positioning system achieves 22 cm accuracy within 15 epochs by float solutions. Also, it was found that cm-level integer solutions can be generated reliably after the quick convergence of float solutions.

A Study on GPS/INS Integration Considering Low-Grade Sensors

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

Estimation of Relative Distance from Surrounding Buildings by a Vision Sensor in Urban Area

The existing GNSS (Global Navigation Satellite System) has the weakness in availability and continuity for the vehicles in urban area since the number of visible satellites decreases abruptly due to large buildings and other structures. In this paper, a new method is proposed to estimate the relative lateral distance of a vehicle from surrounding buildings by a vision sensor to supplement the shortage of satellite-based positioning. In the proposed method, it is assumed that a vehicle, equipped with a vision sensor and a low-cost inertial measurement unit, is moving along a straight road segment in urban area. The common feature points contained in the two successive image frames from the single vision sensor are extracted. Based on the common feature points, the lateral depth information from buildings is estimated and utilized to improve availability and continuity of position solutions. To verify the conceptual feasibility of the proposed method, a simple experiment was performed.

Estimation of Angular Acceleration By a Monocular Vision Sensor

Recently, monitoring of two-body ground vehicles carrying extremely hazardous materials has been considered as one of the most important national issues. This issue induces large cost in terms of national economy and social benefit. To monitor and counteract accidents promptly, an efficient methodology is required. For accident monitoring, GPS can be utilized in most cases. However, it is widely known that GPS cannot provide sufficient continuity in urban cannons and tunnels. To complement the weakness of GPS, this paper proposes an accident monitoring method based on a monocular vision sensor. The proposed method estimates angular acceleration from a sequence of image frames captured by a monocular vision sensor. The possibility of using angular acceleration is investigated to determine the occurrence of accidents such as jackknifing and rollover. By an experiment based on actual measurements, the feasibility of the proposed method is evaluated.