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Long-term stability of the SGA-WZ strapdown airborne gravimeter.

Cai S, Zhang K, Wu M, Huang Y - Sensors (Basel) (2012)

Bottom Line: The test results reveal a quadratic drift in the strapdown airborne gravimeter data.After compensating for the drift, the drift effect improved from 70 mGal to 3.46 mGal with a standard deviation of 0.63 mGal.The quadratic curve better reflects the drift's real characteristics.

View Article: PubMed Central - PubMed

ABSTRACT
Accelerometers are one of the most important sensors in a strapdown airborne gravimeter. The accelerometer's drift determines the long-term accuracy of the strapdown inertial navigation system (SINS), which is the primary and most critical component of the strapdown airborne gravimeter. A long-term stability test lasting 104 days was conducted to determine the characteristics of the strapdown airborne gravimeter's long-term drift. This stability test was based on the first set of strapdown airborne gravimeters built in China, the SGA-WZ. The test results reveal a quadratic drift in the strapdown airborne gravimeter data. A drift model was developed using the static data in the two end sections, and then this model was used to correct the test data. After compensating for the drift, the drift effect improved from 70 mGal to 3.46 mGal with a standard deviation of 0.63 mGal. The quadratic curve better reflects the drift's real characteristics. In comparison with other methodologies, modelling the drift as a quadratic curve was shown to be more appropriate. Furthermore, this method allows the drift to be adjusted throughout the course of the entire campaign.

No MeSH data available.


Related in: MedlinePlus

The power spectral density of the gravimeter data before filtering (27 May).
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f3-sensors-12-11091: The power spectral density of the gravimeter data before filtering (27 May).

Mentions: Step 1: Filter the gravimeter data using a low-pass filter to remove the high-frequency noise. In this test, the simple segmented averaging was adopted. This simple low-pass filter can help to reduce the data collected from the long time testing. Take the data collected on 27 May for example. The raw gravimeter data is shown in Figure 2. The power spectral density of this data is shown in Figure 3.


Long-term stability of the SGA-WZ strapdown airborne gravimeter.

Cai S, Zhang K, Wu M, Huang Y - Sensors (Basel) (2012)

The power spectral density of the gravimeter data before filtering (27 May).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472875&req=5

f3-sensors-12-11091: The power spectral density of the gravimeter data before filtering (27 May).
Mentions: Step 1: Filter the gravimeter data using a low-pass filter to remove the high-frequency noise. In this test, the simple segmented averaging was adopted. This simple low-pass filter can help to reduce the data collected from the long time testing. Take the data collected on 27 May for example. The raw gravimeter data is shown in Figure 2. The power spectral density of this data is shown in Figure 3.

Bottom Line: The test results reveal a quadratic drift in the strapdown airborne gravimeter data.After compensating for the drift, the drift effect improved from 70 mGal to 3.46 mGal with a standard deviation of 0.63 mGal.The quadratic curve better reflects the drift's real characteristics.

View Article: PubMed Central - PubMed

ABSTRACT
Accelerometers are one of the most important sensors in a strapdown airborne gravimeter. The accelerometer's drift determines the long-term accuracy of the strapdown inertial navigation system (SINS), which is the primary and most critical component of the strapdown airborne gravimeter. A long-term stability test lasting 104 days was conducted to determine the characteristics of the strapdown airborne gravimeter's long-term drift. This stability test was based on the first set of strapdown airborne gravimeters built in China, the SGA-WZ. The test results reveal a quadratic drift in the strapdown airborne gravimeter data. A drift model was developed using the static data in the two end sections, and then this model was used to correct the test data. After compensating for the drift, the drift effect improved from 70 mGal to 3.46 mGal with a standard deviation of 0.63 mGal. The quadratic curve better reflects the drift's real characteristics. In comparison with other methodologies, modelling the drift as a quadratic curve was shown to be more appropriate. Furthermore, this method allows the drift to be adjusted throughout the course of the entire campaign.

No MeSH data available.


Related in: MedlinePlus