Experimental setup implements the concept of degree of observability (DoO) adequate a land-vehicle navigation application with noised inertial measurement unit (IMU) and global positioning system (GPS) sensors based on a loosely coupled approach. The navigation systems such as IMU-GPS require extensive evaluations of nonlinear equations as used in an extended Kalman filter (EKF). According to DoO and during our test, we have implemented a method for measuring the DoO of all states continuously. Where, the results showed that applying the fusion IMU-GPS system based on EKF be enhanced the DoO measure. The real dataset consists of outputs a high sampling rate for IMU sensor at each (0.01s) and GPS receiver at each (1s). In addition, an aloft category IMU was put together with differential GPS (DGPS) information to produce a real trajectory. GPS has acceptable long-term accuracy, it is used to update the position and velocity in IMU outputs before processing in the EKF algorithm. The implementation consists of three main algorithms: Strapdown (dead reckoning DR), DoO and EKF algorithms. The results are shown, implementation of both approaches based on EKF and the concept of DoO in GPS/INS integrated systems are sufficient robustness to use with low-cost sensors.

Controlling the degree of observability in GPS/INS integration land-vehicle navigation based on extended kalman filter

Eliseo Clementini;
2022

Abstract

Experimental setup implements the concept of degree of observability (DoO) adequate a land-vehicle navigation application with noised inertial measurement unit (IMU) and global positioning system (GPS) sensors based on a loosely coupled approach. The navigation systems such as IMU-GPS require extensive evaluations of nonlinear equations as used in an extended Kalman filter (EKF). According to DoO and during our test, we have implemented a method for measuring the DoO of all states continuously. Where, the results showed that applying the fusion IMU-GPS system based on EKF be enhanced the DoO measure. The real dataset consists of outputs a high sampling rate for IMU sensor at each (0.01s) and GPS receiver at each (1s). In addition, an aloft category IMU was put together with differential GPS (DGPS) information to produce a real trajectory. GPS has acceptable long-term accuracy, it is used to update the position and velocity in IMU outputs before processing in the EKF algorithm. The implementation consists of three main algorithms: Strapdown (dead reckoning DR), DoO and EKF algorithms. The results are shown, implementation of both approaches based on EKF and the concept of DoO in GPS/INS integrated systems are sufficient robustness to use with low-cost sensors.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/183052
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