Development and implementation of INS/GPS/GNSS integrated system for high speed Air Vehicle’s using Telemetry system
ALAA Moustafa Fekry Mohammed;
Abstract
The Global Positioning System (GPS) and the Inertial Navigation System (INS) are two basic navigation. Through their complementary characters in many respects, the GPS / INS integrated navigation system in the past decade has become a hot topic. Both these strengths and weaknesses are analyzed for individual systems. Micro Electromechanical Sensor (MEMS) successfully solves the traditional INS cost, size and weight problem.
Therefore, they are typically used in GPS / INS integrated systems. The biggest problem is the large MEMS sensor error, the navigation performance degrades exponentially rapidly. Using another method, i.e Gaussian Markov process and Alan dispersion, we analyze random errors in the MEMS sensor. In actual testing, it is running on the MEMS based inertial measurement unit for each method.
Various methods appear to give an equivalent estimate of the random source of errors from the results. This error factor allows better Kalman filtering of better navigation performance after the GPS signal is lost.
Two levels of GPS / IMU integration structure; it is introduced with a loose and tight GPS / IMU navigation, a simple analysis of each character. The principle of loose integration is given to basic INS navigation principles as well as detailed comparisons. The extended Kalman filter (EKF) is also the core of the entire navigation system and should be introduced and presented as a basic data fusion algorithm. A theoretical analysis of some restricted Kalman filter is given to show the improvement of navigation performance.
The detailed implementation process of the GPS / IMU integrated system is given based on the system model. We display the playing position of the standard error of tight integration structure under various scenarios. Less than four observable satellites can contribute to an integrated system. In particular the scanning of the two satellites keeps the bearing error at a reasonable level due to tight integration.
Therefore, they are typically used in GPS / INS integrated systems. The biggest problem is the large MEMS sensor error, the navigation performance degrades exponentially rapidly. Using another method, i.e Gaussian Markov process and Alan dispersion, we analyze random errors in the MEMS sensor. In actual testing, it is running on the MEMS based inertial measurement unit for each method.
Various methods appear to give an equivalent estimate of the random source of errors from the results. This error factor allows better Kalman filtering of better navigation performance after the GPS signal is lost.
Two levels of GPS / IMU integration structure; it is introduced with a loose and tight GPS / IMU navigation, a simple analysis of each character. The principle of loose integration is given to basic INS navigation principles as well as detailed comparisons. The extended Kalman filter (EKF) is also the core of the entire navigation system and should be introduced and presented as a basic data fusion algorithm. A theoretical analysis of some restricted Kalman filter is given to show the improvement of navigation performance.
The detailed implementation process of the GPS / IMU integrated system is given based on the system model. We display the playing position of the standard error of tight integration structure under various scenarios. Less than four observable satellites can contribute to an integrated system. In particular the scanning of the two satellites keeps the bearing error at a reasonable level due to tight integration.
Other data
| Title | Development and implementation of INS/GPS/GNSS integrated system for high speed Air Vehicle’s using Telemetry system | Other Titles | تصميم و تطوير لنظام تكامل الاقمار الصناعية و اجهزة الملاحة الذاتية للمركبات ذات السرعات العاليــــة باستخدام انظمـــة القياســـات | Authors | ALAA Moustafa Fekry Mohammed | Issue Date | 2017 |
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