FrFT Spectrum Analysis for Weak Signal Separation in RF Noise
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Solution Overview
Problem
Conventional spectrum analyzers are limited to viewing RF signals in the frequency or time domain, making it difficult to detect and separate weak signals from interference and noise, especially in non-stationary environments, and cannot identify unknown signals effectively.
Innovation Solution
Implementing Fractional Fourier Transform (FrFT)-based spectrum analyzers that rotate the signal axes to different dimensions, allowing for improved signal detection and separation by displaying signals above the noise floor and enabling identification of signal types through the Wigner Distribution (WD).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional FFT-based spectrum analysis is used, then the device complexity is low and ease of operation is high, but signal separation capability deteriorates in non-stationary environments
Solution Approach 1:
The patent applies Fractional Fourier Transform (FrFT) which introduces a continuous parameter alpha (α) to rotate the time-frequency plane by different angles. This parameter change allows the system to adapt to non-stationary signals by finding optimal rotation angles that maximize signal separation, resolving the contradiction between improved separation capability and increased complexity through parameterized transformation.
Solution Approach 2:
The patent transitions from conventional 1D frequency domain analysis to 2D time-frequency domain analysis using FrFT. By rotating the analysis plane in the time-frequency domain, the system creates additional dimensional information that enhances signal separation capability for non-stationary signals, effectively adding a rotational dimension to the traditional spectral analysis.
2Measurement precision
If FrFT-based spectrum analysis is implemented, then signal detection capability improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary time-frequency transformation using FrFT before spectral analysis. By pre-rotating the signal in the time-frequency domain to align with the signal of interest, the system simplifies subsequent detection operations and improves measurement precision, effectively preparing the data structure in advance to reduce overall computational burden.
3Reliability
If repeated FrFT domain filtering is applied, then signal separation capability improves, but processing time increases
Solution Approach 1:
The patent employs iterative FrFT filtering where the transformation is applied repeatedly with different rotation angles. This periodic application of FrFT at multiple angles allows the system to progressively enhance signal separation by capturing signal energy at optimal orientations, achieving improved separation capability through systematic repeated operations.
Data Source
AI summary
Fractional Fourier Transform (FrFT)-based spectrum analyzers and spectrum analysis techniques are disclosed. Rather than using the standard Fast Fourier Transform (FFT), the FrFT may be used to view the signal content contained in a particular bandwidth. Usage of the FrFT in place of the frequency or time domain allows viewing of the signal in different dimensions, where “spectral” features of interest, or signal content, may appear where they were not visible in these domains before. This may allow signals to be identified and viewed in any domain within the continuous time-frequency plane, and may significantly enhance the ability to detect and extract signals that were previously hidden under interference and/or noise, provide or enhance the ability to extract signals from a congested environment, and enable operation in a signal-dense environment.