Blind Signal Extraction via Modified Wavelet Transform
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Solution Overview
Problem
Existing signal processing technologies, such as Fourier transforms and standard discrete wavelet transforms, are ineffective in extracting target information from time domain signals contaminated by unknown interfering signals and random background noise, especially in non-ideal and arbitrary test environments.
Innovation Solution
A method that utilizes a modified wavelet transform to convert decomposed components at all scales into the first scale in the time domain, allowing for the analysis of individual features and characteristics in measured data, enabling the extraction of target information without prior knowledge of the test environment or signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard discrete wavelet transform is used to decompose signals into various scales, then signal decomposition is achieved, but it becomes difficult to analyze the behaviors of various components and features of the measured data
Solution Approach 1:
The patent segments the wavelet transform process into two distinct phases: decomposition phase (breaking down the signal into multiple scales) and reconstruction phase (converting selected scale components back to time domain). This segmentation allows selective analysis of specific frequency components while maintaining the ability to examine their time-domain behaviors independently, thus resolving the contradiction between comprehensive decomposition and analytical difficulty.
2Measurement precision
If Fourier transform or short-time Fourier transform is used for signal processing, then frequency analysis is achieved, but they cannot handle signals that are varying in time and frequency in an arbitrary manner
Solution Approach 1:
The patent changes the fundamental parameters of the transform approach by using wavelet transform instead of Fourier transform. Wavelet transform uses variable window sizes that adapt to different frequency components, allowing both precise frequency analysis and handling of arbitrary time-frequency variations. The method dynamically adjusts the analysis parameters (wavelet scales) to match the signal characteristics, thereby resolving the contradiction between frequency analysis precision and adaptability to arbitrary signals.
3Adaptability or versatility
If blind extraction method is used without prior knowledge of test environment and signal characteristics, then the method is universally applicable, but the quality of extracted information is reduced
Solution Approach 1:
The patent performs preliminary wavelet decomposition of the mixed signal before extraction, organizing the signal into distinct scale components. This preliminary action creates a structured representation that facilitates subsequent blind extraction. By pre-processing the signal in this manner, the method maintains universal applicability while improving extraction quality, as the decomposition reveals hidden structures that aid in separating target signals from interference without requiring prior environmental knowledge.
Data Source
AI summary
A system and method allow for extracting target signals from the overall measurement data without knowing how and where these data are collected, the locations and characteristics of target sources as well as those of random background noise sources. In essence, it uses an innovative and advanced signal processing technique to reveal certain critical information from a mixture of data that is hard to obtain otherwise. In particular, it allows for denoising the measured data that have been contaminated by various interfering and background noise, making it possible to extract certain target information that may be otherwise difficult to observe. The only assumption made in this method is that the target signal is incoherent with respect to all interfering signals and background noise. The more information about a target signal is available, the more complete the extracted signal becomes.