Radar Clutter Filter Bias Correction
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Solution Overview
Problem
Existing radar data filtering techniques attenuate weather signals, leading to biased data and increased standard deviations, making it difficult to accurately remove ground clutter without compromising weather data integrity.
Innovation Solution
A method and system that apply a clutter filter to time series radar data, followed by a discrete Fourier transform, and then correct the filtered frequency domain data by adding a filter bias to remove ground clutter while preserving weather data, thereby reducing signal attenuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a clutter filter is applied to time series radar data to remove ground clutter, then ground clutter is reduced, but the weather signal is attenuated and biased
Solution Approach 1:
The patent pre-calculates the filter bias in the frequency domain before applying the clutter filter in the time domain. By determining the frequency response of the filter and computing the bias correction values in advance, the system prepares compensation data that will be applied after filtering to restore the weather signal spectrum, thus preventing the attenuation bias from compromising measurement precision
Solution Approach 2:
The patent implements a feedback mechanism where the filtered time series data is transformed back to the frequency domain, and the pre-calculated filter bias is added to correct the spectral attenuation. This feedback loop ensures that the weather signal is restored after clutter removal, maintaining measurement accuracy while achieving clutter reduction
2Object-affected harmful factors
If a clutter filter is applied to remove ground clutter, then ground clutter is reduced, but the standard deviation of weather signal estimates increases
Solution Approach 1:
The system pre-calculates the filter bias across the frequency spectrum before applying the clutter filter. By having the bias correction values ready in advance, the system can immediately compensate for the increased standard deviation effect after filtering, thus maintaining reliability of weather signal estimates while achieving clutter removal
Solution Approach 2:
The patent uses feedback by transforming the filtered data back to frequency domain and adding the pre-computed bias correction. This feedback process directly addresses the increased standard deviation by restoring the attenuated spectral components, thereby maintaining the reliability of weather signal estimates
3Object-affected harmful factors
If a Doppler spectrum notch filter is applied to eliminate zero velocity components, then ground clutter is removed, but low velocity weather data is also removed
Solution Approach 1:
The patent applies local quality by using a time domain clutter filter that selectively targets only the ground clutter components in the time series data, rather than applying a broad notch filter that removes all low velocity components. This localized filtering approach preserves low velocity weather data while removing clutter, and the subsequent bias correction further ensures that no weather information is lost
Solution Approach 2:
The feedback mechanism transforms filtered data back to frequency domain and adds pre-calculated bias corrections, which restores any attenuated weather signal components including low velocity data that might have been affected by the filtering process, thus preventing information loss
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces ground clutter without increasing the standard deviation of weather signal estimates, providing more accurate and robust weather radar data.
Implementation Method 1
applying a discrete Fourier transform to the filtered time series radar data to generate a filtered frequency domain data
Data Source
AI summary
A method and system for removing ground clutter data from time series radar data are provided. The method comprises receiving the time series radar data, applying a clutter filter to the time series radar data to generate a filtered time series radar data, applying a discrete Fourier transform to the filtered time series radar data to generate a filtered frequency domain data, determining a filter bias for one or more filter biased frequency domain frequencies of the filtered frequency domain data based on a frequency response of the clutter filter, and correcting the filtered frequency domain data by adding the filter bias to the filtered frequency domain data at the one or more filter biased frequency domain frequencies to generate a filtered and bias corrected frequency domain data.