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Performance Evaluation and Requirements Assessment for Gravity Gradient Referenced Navigation.

Lee J, Kwon JH, Yu M - Sensors (Basel) (2015)

Bottom Line: It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance.Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present.However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformatics, University of Seoul, Seoul 130-743, Korea. leejs@uos.ac.kr.

ABSTRACT
In this study, simulation tests for gravity gradient referenced navigation (GGRN) are conducted to verify the effects of various factors such as database (DB) and sensor errors, flight altitude, DB resolution, initial errors, and measurement update rates on the navigation performance. Based on the simulation results, requirements for GGRN are established for position determination with certain target accuracies. It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance. In particular, a DB and sensor with accuracies of 0.1 E and 0.01 E, respectively, are required to determine the position more accurately than or at a level similar to the navigation performance of terrain referenced navigation (TRN). In most cases, the horizontal position error of GGRN is less than 100 m. However, the navigation performance of GGRN is similar to or worse than that of a pure inertial navigation system when the DB and sensor errors are 3 E or 5 E each and the flight altitude is 3000 m. Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present. However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available.

No MeSH data available.


Diagram of gravity gradient referenced navigation.
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sensors-15-16833-f001: Diagram of gravity gradient referenced navigation.

Mentions: Figure 1 shows the principles of GGRN. First, the INS computes the current position, velocity, and attitude by integrating the measurement from the inertial measurement unit (IMU). The geophysical sensors, including the GGI, altimeter, and compass, are assumed to acquire gravity gradient, height, and yaw information for the vehicle at every epoch. When the geophysical measurements are obtained, the gravity gradient values corresponding to the INS-indicated position are extracted from the gravity gradient DB. Then, the difference between the gravity gradient obtained from the GGI and that obtained from the DB is used as the measurement in the EKF. Additionally, the differences between the observations from the altimeter and the height from the INS and those between the observations from the compass and the yaw from the INS are used as measurements in the EKF. Using these processes, the EKF estimates 15-state vectors, composed of the position (), velocity (, attitude (), and accelerometer and gyroscope biases (). In Figure 1,denote the latitude, longitude, and height, respectively;denotes the bearing; andanddenote the gravity gradients from the DB and gradiometer, respectively. The gravity gradient is the spatial rate of change of gravity vector and form a second-order tensor with nine components. In the local North East Down navigation frame, it is given as a symmetric tensor as Equation (1).(1)Γ=(ΓNNΓNEΓNDΓNEΓEEΓEDΓNDΓEDΓDD)


Performance Evaluation and Requirements Assessment for Gravity Gradient Referenced Navigation.

Lee J, Kwon JH, Yu M - Sensors (Basel) (2015)

Diagram of gravity gradient referenced navigation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541910&req=5

sensors-15-16833-f001: Diagram of gravity gradient referenced navigation.
Mentions: Figure 1 shows the principles of GGRN. First, the INS computes the current position, velocity, and attitude by integrating the measurement from the inertial measurement unit (IMU). The geophysical sensors, including the GGI, altimeter, and compass, are assumed to acquire gravity gradient, height, and yaw information for the vehicle at every epoch. When the geophysical measurements are obtained, the gravity gradient values corresponding to the INS-indicated position are extracted from the gravity gradient DB. Then, the difference between the gravity gradient obtained from the GGI and that obtained from the DB is used as the measurement in the EKF. Additionally, the differences between the observations from the altimeter and the height from the INS and those between the observations from the compass and the yaw from the INS are used as measurements in the EKF. Using these processes, the EKF estimates 15-state vectors, composed of the position (), velocity (, attitude (), and accelerometer and gyroscope biases (). In Figure 1,denote the latitude, longitude, and height, respectively;denotes the bearing; andanddenote the gravity gradients from the DB and gradiometer, respectively. The gravity gradient is the spatial rate of change of gravity vector and form a second-order tensor with nine components. In the local North East Down navigation frame, it is given as a symmetric tensor as Equation (1).(1)Γ=(ΓNNΓNEΓNDΓNEΓEEΓEDΓNDΓEDΓDD)

Bottom Line: It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance.Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present.However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformatics, University of Seoul, Seoul 130-743, Korea. leejs@uos.ac.kr.

ABSTRACT
In this study, simulation tests for gravity gradient referenced navigation (GGRN) are conducted to verify the effects of various factors such as database (DB) and sensor errors, flight altitude, DB resolution, initial errors, and measurement update rates on the navigation performance. Based on the simulation results, requirements for GGRN are established for position determination with certain target accuracies. It is found that DB and sensor errors and flight altitude have strong effects on the navigation performance. In particular, a DB and sensor with accuracies of 0.1 E and 0.01 E, respectively, are required to determine the position more accurately than or at a level similar to the navigation performance of terrain referenced navigation (TRN). In most cases, the horizontal position error of GGRN is less than 100 m. However, the navigation performance of GGRN is similar to or worse than that of a pure inertial navigation system when the DB and sensor errors are 3 E or 5 E each and the flight altitude is 3000 m. Considering that the accuracy of currently available gradiometers is about 3 E or 5 E, GGRN does not show much advantage over TRN at present. However, GGRN is expected to exhibit much better performance in the near future when accurate DBs and gravity gradiometer are available.

No MeSH data available.