Related Experiment Video
Updated: Dec 12, 2025
07:14
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
Published on: May 1, 2018
8.1K
ANALYSIS OF NON-STATIONARY RADIOMETER GAIN VIA ENSEMBLE DETECTION
Mustafa Aksoy1, Paul E Racette2, John W Bradburn1
1University at Albany, State University of New York.
IEEE International Geoscience and Remote Sensing Symposium Proceedings. International Geoscience and Remote Sensing Symposium
|August 11, 2020
Summary
Radiometer gain, often assumed stable, actually fluctuates. A new Ensemble Detection method characterizes this non-stationary gain using voltage time series, revealing its dependence on calibration.
Area of Science:
- Physics
- Signal Processing
- Radiometry
Background:
- Radiometer gain is typically assumed to be stationary and Gaussian.
- However, real-world radiometer gain often exhibits non-stationary properties.
- Analyzing these non-stationary features is difficult due to the inability to observe the radiometer signal independently.
Purpose of the Study:
- To present a novel technique, Ensemble Detection, for analyzing fluctuating radiometer gain.
- To analytically retrieve the standard deviation of stationary Gaussian radiometer gain.
- To find an equivalent stationary Gaussian process representing non-stationary radiometer gain under various calibration schemes.
Main Methods:
- Utilizing time series of post-gain voltages as an ensemble set to characterize radiometer gain.
- Developing the Ensemble Detection technique for analytical retrieval.
- Investigating the influence of calibration structure and observation times on the equivalent Gaussian process.
Main Results:
- Demonstrated that Ensemble Detection can characterize non-stationary radiometer gain.
- Showed that the equivalent Gaussian process is highly dependent on calibration structure.
- Highlighted the impact of observation times for both measurands and calibration references.
Conclusions:
- Ensemble Detection offers a robust method for analyzing non-stationary radiometer gain.
- The characterization of radiometer gain is significantly influenced by calibration protocols.
- Understanding these dependencies is crucial for accurate radiometer measurements.
Related Concept Videos
01:05Difference from Background: Limit of Detection
7.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
7.9K
01:15Receiver Operating Characteristic Plot
419
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
419
01:13Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview
1000
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
The ATR process begins by directing a beam...
1000
01:19Precipitation Titration: Endpoint Detection Methods
3.7K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
In the Volhard method, a standard excess of AgNO3 is first added to the...
3.7K
01:04Random Error
6.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
6.9K
01:20Maxwell-Boltzmann Distribution: Problem Solving
2.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.7K