FMCW Radar Dispersion Correction for Pipe Distance Measurement
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Solution Overview
Problem
Radar measurement in pipes faces challenges due to dispersion effects caused by frequency-dependent propagation velocities, leading to blurring and divergence of reflected signals, which complicates accurate distance measurement.
Innovation Solution
A method using FMCW radar that applies dispersion correction to the intermediate frequency signal, allowing for precise peak detection in the frequency spectrum by introducing frequency support points and using discrete Fourier transformation or the Goertzel algorithm, reducing computational effort and enabling sub-millimeter accuracy in distance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full Fourier transformation is performed on the dispersion-corrected signal to achieve precise peak detection, then measurement precision improves, but computational time and processing load increase significantly
Solution Approach 1:
The frequency spectrum is segmented into regions of interest around detected peak positions. Instead of performing a full Fourier transformation, the method focuses computational resources only on specific frequency regions where peaks are expected to occur, thereby reducing overall processing time while maintaining precision in critical areas.
Solution Approach 2:
A rough peak detection is performed first using a simplified or reduced Fourier transformation. Based on these preliminary results, frequency support points are pre-selected in the vicinity of detected peaks. This preliminary action guides the subsequent more precise evaluation, avoiding the need for a complete high-precision transformation.
2Measurement precision
If dispersion correction is applied to remove frequency-dependent propagation effects, then measurement accuracy improves, but device complexity and processing steps increase
Solution Approach 1:
Frequency support points serve as intermediaries between the raw spectrum and the final peak detection. These selected frequency points act as sampling points that capture essential spectral information without requiring complete spectral analysis. The intermediary approach simplifies the processing chain while preserving measurement accuracy.
Solution Approach 2:
Instead of processing the entire dispersion-corrected signal spectrum, the method creates a simplified representation by evaluating only specific frequency support points. This copying approach extracts the necessary information from the full spectrum, reducing computational complexity while maintaining the essential measurement capabilities.
3Measurement precision
If multiple frequency support points are introduced and evaluated using discrete Fourier transformation or Goertzel algorithm, then peak position detection accuracy improves, but computational effort increases
Solution Approach 1:
The method applies partial action by evaluating frequency support points selectively rather than performing a complete Fourier transformation across the entire spectrum. The Goertzel algorithm is used to compute individual frequency components efficiently, performing only the necessary calculations for detected peak regions rather than exhaustive spectral analysis.
Solution Approach 2:
The approach changes the evaluation parameter from a full spectral transformation to discrete frequency point evaluation. By transforming the problem from evaluating all frequency components to evaluating only selected support points, the computational complexity is reduced from O(N log N) to O(N) or better, depending on the number of support points relative to the full spectrum size.
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 significantly reduces the time required for spectral evaluation, providing quick and accurate distance measurements by minimizing the impact of dispersion effects, resulting in precise distance determination within the sub-millimeter range.
Implementation Method 1
transmitting within the pipe a radar transmission signal frequency-modulated according to the FMCW principle
Implementation Method 2
mixing the radar received signal with the radar transmission signal or a signal derived therefrom and producing an intermediate signal
Implementation Method 3
determining a frequency spectrum of the intermediate signal or a signal derived therefrom by means of fast Fourier transformation and detecting the position of the frequency peak in the frequency spectrum
Implementation Method 4
determining a dispersion correction for removing, respectively lessening, dispersion effects, applying the dispersion correction to the intermediate frequency signal or a signal derived therefrom
Implementation Method 5
determining the respective frequency amplitudes selectively at the newly introduced frequency support points by means of discrete Fourier transformation or by means of the Goertzel algorithm
Implementation Method 6
determining the respective frequency amplitudes selectively at the newly introduced frequency support points by means of discrete Fourier transformation or by means of the Goertzel algorithm
Data Source
AI summary
A method for determining a distance to a surface of a medium or to another radar target in a pipe by means of a radar measurement apparatus. Transmitting within the pipe a radar transmission signal frequency modulated according to the FMCW principle, receiving a radar received signal reflected on the surface of the medium or on the other radar target in the pipe back to the radar measurement apparatus, mixing the radar received signal with the radar transmission signal or a signal derived therefrom and producing an intermediate signal. Determining a frequency spectrum of the intermediate signal or a signal derived therefrom by means of fast Fourier transformation and detecting the position of the frequency peak in the frequency spectrum. Determining a dispersion correction for removing, respectively lessening, dispersion effects, applying the dispersion correction to the intermediate frequency signal or to a signal derived therefrom and producing a dispersion corrected signal, and determining the position of the frequency peak in the frequency spectrum of the dispersion corrected signal anew by introducing a number of frequency support points in the region of the previously detected frequency peak, determining the respective frequency amplitudes selectively at the newly introduced frequency support points, and ascertaining the position of the frequency peak in the frequency spectrum of the dispersion corrected signal using the frequency amplitudes at the newly introduced frequency support points.