“AVT Statistical filtering algorithm”的意思、由来-开放百科全书

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AVT Statistical filtering algorithm

释义

Averaging Algorithm
Median Algorithm
AVT Algorithm
Filtering algorithms comparison
AVT algorithm variations
Cascaded AVT Reverse AVT
Possible applications and uses
References

AVT Statistical filtering algorithm is an approach to improving quality of raw data collected from various sources. It is most effective in cases when there is inband noise present. In those cases AVT is better at filtering data then, band-pass filter or any digital filtering based on variation of.

Conventional filtering is useful when signal/data has different frequency than noise and signal/data is separated/filtered by frequency discrimination of noise. Frequency discrimination filtering is done using Low Pass, High Pass and Band Pass filtering which refers to relative frequency filtering criteria target for such configuration. Those filters are created using passive and active components and sometimes are implemented using software algorithms based on FFT.

AVT filtering is implemented in software and its inner working is based on statistical analysis of raw data.

When signal frequency/(useful data distribution frequency) coincides with noise frequency/(noisy data distribution frequency) we have inband noise. In this situations frequency discrimination filtering does not work since the noise and useful signal are indistinguishable and where AVT excels. To achieve filtering in such conditions there are several methods/algorithms available which are briefly described below.

Averaging Algorithm

Collect n samples of data
Calculate average value of collected data
Present/record result as actual data

Median Algorithm

Collect n samples of data
Sort the data in ascending or descending order. Note that order does not matter
Select the data that happen to be in n/2 position and present/record it as final result representing data sample

AVT Algorithm

AVT algorithm stands for Antonyan Vardan Transform and its implementation explained below.

Collect n samples of data
Calculate the standard deviation and average value
Drop any data that is greater or less than average ± one standard deviation
Calculate average value of remaining data
Present/record result as actual value representing data sample

This algorithm is based on amplitude discrimination and can easily reject any noise that is not like actual signal, otherwise statistically different then 1 standard deviation of the signal. Note that this type of filtering can be used in situations where the actual environmental noise is not known in advance.

Filtering algorithms comparison

Using a system that has signal value of 1 and has noise added at 0.1% and 1% levels will simplify quantification of algorithm performance. The R^[1] script is used to create pseudo random noise added to signal and analyze the results of filtering using several algorithms. Please refer to "Reduce Inband Noise with the AVT Algorithm" ^[2] article for details.

This graphs show that AVT algorithm provides best results compared with Median and Averaging algorithms while using data sample size of 32, 64 and 128 values. Note that this graph was created by analyzing random data array of 10000 values. Sample of this data is graphically represented below.

AVT algorithm variations

Cascaded AVT

In some situations better results can be obtained by cascading several stages of AVT filtering. This will produce singular constant value which can be used for equipment that has known stable characteristics like thermometers, thermistors and other slow acting sensors.

Reverse AVT

Collect n samples of data
Calculate the standard deviation and average value
Drop any data that is within one standard deviation ± average band
Calculate average value of remaining data
Present/record result as actual data

This is useful for detecting minute signals that are close to background noise level.

Possible applications and uses

Used in planet detection to filter out raw data from Kepler (spacecraft)
Filter out noise from sound sources where all other filtering methods (Low-pass filter, High-pass filter, Band-pass filter, Digital filter) fail.
Preprocess scientific data for data analysis (Smoothness) before plotting see (Plot (graphics))
Used in SETI (Search for extraterrestrial intelligence) for detecting/distinguishing extraterrestrial signals from cosmic background

References

1. ^{{cite web|url=http://www.r-project.org/ |title=The R Project for Statistical Computing |publisher=r-project.org|accessdate=2015-01-10}}
2. ^{{cite web|url=http://electronicdesign.com/embedded/reduce-inband-noise-avt-algorithm |title=Reduce Inband Noise with the AVT Algorithm | Embedded content from Electronic Design |publisher=electronicdesign.com|accessdate=2015-01-10}}

1 : Algorithms

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