The Performance Analysis of a Real-Time Integrated INS/GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination
نویسندگان
چکیده
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.
منابع مشابه
GPS/INS Integration for Vehicle Navigation based on INS Error Analysis in Kalman Filtering
The Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in the recent decade. The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with the traditional INS. Therefore...
متن کاملPerformance Enhancement of GPS/INS Integrated Navigation System Using Wavelet Based De-noising method
Accuracy of inertial navigation system (INS) is limited by inertial sensors imperfections. Before using inertial sensors signals in the data fusion algorithm, noise removal method should be performed, in which, wavelet decomposition method is used. In this method the raw data is decomposed into high and low frequency data sets. In this study, wavelet multi-level resolution analysis (WMRA) techn...
متن کاملA Hierarchical SLAM/GPS/INS Sensor Fusion with WLFP for Flying Robo-SAR's Navigation
In this paper, we present the results of a hierarchical SLAM/GPS/INS/WLFP sensor fusion to be used in navigation system devices. Due to low quality of the inertial sensors, even a short-term GPS failure can lower the integrated navigation performance significantly. In addition, in GPS denied environments, most navigation systems need a separate assisting resource, in order to increase the avail...
متن کاملImprovement of Navigation Accuracy using Tightly Coupled Kalman Filter
In this paper, a mechanism is designed for integration of inertial navigation system information (INS) and global positioning system information (GPS). In this type of system a series of mathematical and filtering algorithms with Tightly Coupled techniques with several objectives such as application of integrated navigation algorithms, precise calculation of flying object position, speed and at...
متن کاملNeural Network Aided Kalman Filtering For Integrated GPS/INS Navigation System
Kalman filter (KF) uses measurement updates to correct system states error and to limit the errors in navigation solutions. However, only when the system dynamic and measurement models are correctly defined, and the noise statistics for the process are completely known, KF can optimally estimate a system’s states. Without measurement updates, Kalman filter’s prediction diverges; therefore the p...
متن کامل