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Drones Navigation in Mission Critical Applications
Published in Fadi Al-Turjman, Drones in IoT-enabled Spaces, 2019
In this chapter, the real-time INS and GPS integration in UAV navigation utilizing a two-level KF has been simulated and examined. This approach is based on predicting the INS position error and continuously removing it from its corresponding INS position, in addition to a second level of applied KF for the overall integrated INS/GPS errors. Results showed the ability of the KF-based module to reduce the INS position error and prevent its growth even in the long term. We saw in the cases, error is stabilized and takes a pattern in time. In addition, the proposed KF module was able to accurately predict the INS position errors during GPS outages. This was the objective of the system, as introduced early in this chapter. Results point out to success in achieving these objectives, of being able to predict, in real time, the proper position of UAV, based on learning the dynamics of the most recent GPS signal, with good degree of accuracy. The simulation proved that the integration technique is a reliable, robust, and self-adaptive system that requires no prior knowledge of the navigation system utilized. We conclude from this study that several factors can affect the performance of the KF output (prediction). But still we can achieve a high-performance navigation system by integrating INS with GPS s. It outperforms the unaided INS or GPS system even if we used Xbow sensors that have lower accuracy than Novatel.
Georeferencing Component of LiDAR Systems
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
The integration of GPS and INS has been investigated for several years in various applications including navigation, mobile mapping, airborne gravimetry, and guidance and control. Both systems are complimentary, and their integration overcomes their individual limitations. In GPS/INS systems, the GPS provides position and velocity, and the INS provides attitude information. In addition, the INS can provide very accurate position and velocity with a high data rate between GPS measurement fixes. Therefore, INS is used to detect and correct GPS cycle slips and also for navigation during GPS signal loss of lock. Finally, the GPS is used for the in-motion calibration of the INS accelerometer and gyro-sensor residual errors. For all INS/GPS applications, navigation information parameters are obtained using kinematic modeling. Thus, the state-space representation is implemented in the mathematical modeling of INS, GPS, and INS/GPS systems. In this context, the Kalman filter (KF) has been commonly used as an optimal estimator and compensator of the INS/GPS system errors.
Georeferencing Component of LiDAR Systems
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2017
The integration of GPS and INS has been investigated for several years in various applications including navigation, mobile mapping, airborne gravimetry, and guidance and control. Both systems are complimentary and their integration overcomes their individual limitations. In GPS/INS systems, the GPS provides position and velocity and the INS provides attitude information. In addition, the INS can provide very accurate position and velocity with a high data rate between GPS measurement fixes. Therefore, INS is used to detect and correct GPS cycle slips and also for navigation during GPS signal loss of lock. Finally, the GPS is used for the in-motion calibration of the INS accelerometer and gyro sensor residual errors. For all INS/GPS applications, navigation information parameters are obtained using kinematic modeling. Thus, the state-space representation is implemented in the mathematical modeling of INS, GPS and INS/GPS systems. In this context, the Kalman Filter (KF) has been commonly used as an optimal estimator and compensator of the INS/GPS system errors.
Self-calibration and compensation of residual gyro drifts for rotation inertial navigation system with fibre optic gyro
Published in Journal of Modern Optics, 2019
Jie Sui, Lei Wang, Wei Wang, Tianxiao Song
The inertial navigation system (INS) employs three gyros and three accelerometers to measure angular velocities and specific force respectively, and obtains the real-time velocities, positions and attitudes of the vehicle according to the dead reckoning principle. The constant drifts of gyros and the biases of accelerometers are the key error sources of the INS. The rotation technique, i.e. periodical rotation of the inertial sensor, is one of the most effective ways to reduce the impacts of these errors (1,2). With the rotation technique, the drifts in the body frame are modulated into the sine or cosine form with an average value of zero in theory (3,4). Therefore, the navigation accuracy can be greatly improved.