Thanh Trung Duong

The Performance Analysis of a Real-Time Integrated INS/GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination

The integration of an Inertial Navigation System (INS) and the Global Positioning System (GPS) is common in mobile mapping and navigation applications to seamlessly determine the position, velocity, and orientation of the mobile platform. In most INS/GPS integrated architectures, the GPS is considered to be an accurate reference with which to correct for the systematic errors of the inertial sensors, which are composed of biases, scale factors and drift. However, the GPS receiver may produce abnormal pseudo-range errors mainly caused by ionospheric delay, tropospheric delay and the multipath effect. These errors degrade the overall position accuracy of an integrated system that uses conventional INS/GPS integration strategies such as loosely coupled (LC) and tightly coupled (TC) schemes. Conventional tightly coupled INS/GPS integration schemes apply the Klobuchar model and the Hopfield model to reduce pseudo-range delays caused by ionospheric delay and tropospheric delay, respectively, but do not address the multipath problem. However, the multipath effect (from reflected GPS signals) affects the position error far more significantly in a consumer-grade GPS receiver than in an expensive, geodetic-grade GPS receiver. To avoid this problem, a new integrated INS/GPS architecture is proposed. The proposed method is described and applied in a real-time integrated system with two integration strategies, namely, loosely coupled and tightly coupled schemes, respectively. To verify the effectiveness of the proposed method, field tests with various scenarios are conducted and the results are compared with a reliable reference system.

GPS/MEMS INS Data Fusion and Map Matching in Urban Areas

This paper presents an evaluation of the map-matching scheme of an integrated GPS/INS system in urban areas. Data fusion using a Kalman filter and map matching are effective approaches to improve the performance of navigation system applications based on GPS/MEMS IMUs. The study considers the curve-to-curve matching algorithm after Kalman filtering to correct mismatch and eliminate redundancy. By applying data fusion and map matching, the study easily accomplished mapping of a GPS/INS trajectory onto the road network. The results demonstrate the effectiveness of the algorithms in controlling the INS drift error and indicate the potential of low-cost MEMS IMUs in navigation applications.

On-line smoothing for an integrated navigation system with low-cost MEMS inertial sensors.

The integration of the Inertial Navigation System (INS) and the Global Positioning System (GPS) is widely applied to seamlessly determine the time-variable position and orientation parameters of a system for navigation and mobile mapping applications. For optimal data fusion, the Kalman filter (KF) is often used for real-time applications. Backward smoothing is considered an optimal post-processing procedure. However, in current INS/GPS integration schemes, the KF and smoothing techniques still have some limitations. This article reviews the principles and analyzes the limitations of these estimators. In addition, an on-line smoothing method that overcomes the limitations of previous algorithms is proposed. For verification, an INS/GPS integrated architecture is implemented using a low-cost micro-electro-mechanical systems inertial measurement unit and a single-frequency GPS receiver. GPS signal outages are included in the testing trajectories to evaluate the effectiveness of the proposed method in comparison to conventional schemes.

The Performance Analysis of the Tactical Inertial Navigator Aided by Non-GPS Derived References

The Inertial Navigation System (INS) is now widely applied in many navigation and mobile mapping applications due to its high sampling rates, high accuracy in short-term cases, and no limitations caused by interference or signal obstructions. In addition, the INS can continuously provide the position, velocity and attitude of a vehicle. Conversely, the disadvantage of the stand-alone INS is that its accuracy degrades rapidly with time because of the accumulations of systematic errors and noises from accelerometers and gyroscopes. Therefore, this research aims to implement an integrated system with specific 3D position updates using non-GPS derived references to aid a tactical inertial navigator to provide seamless navigation solutions in the specific area without Global Positioning System (GPS) signals. An Extended Kalman Filter (EKF) is applied as the core estimator to provide superior performance and output the navigation solutions in real-time. The INS is updated by position from references such as the digital map, land mark, Digital Terrain Model (DTM) as well as waypoint to improve navigation accuracy in the long-term. In order to evaluate the performance of the proposed algorithm, field tests including land scenario in freeway and airborne scenario with an unmanned aerial test platform have been conducted. The preliminary results demonstrate that the proposed algorithm with non-GPS derived references aiding from digital map and waypoint for onboard aerial camera trigger to provide uninterrupted navigation solutions and better performance which can achieve the meter-level accuracy without GPS aiding for land and aerial scenarios, respectively.