Radio Wave Feature Value Computation for Emission Source Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radio wave emission source identification methods are hindered by multipath fading, which changes with the surrounding environment, making it difficult to specify the emission source accurately and requiring reacquisition of learning data when the environment changes, and they are also influenced by reception noise, leading to poor accuracy especially at low signal-to-noise ratios.
Innovation Solution
A radio wave feature value computation apparatus that designates and Fourier-transforms specific signal sections within a baseband signal, computing a feature value that isolates nonlinearity present in the wireless communication terminal, independent of multipath fading and noise, by using sections that are shorter than fading fluctuations, allowing stable identification of the emission source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If power spectral density is used as a feature value to identify radio wave emission sources, then the identification can be performed using available signal data, but the identification accuracy deteriorates when multipath fading changes due to environmental changes
Solution Approach 1:
The patent segments the received signal into multiple measurement sections and processes each section independently through Fourier transformation. By dividing the signal processing into discrete segments that can be handled separately, the system can adapt to changing environmental conditions while maintaining identification accuracy through localized analysis of signal characteristics.
Solution Approach 2:
The patent transforms the signal from time domain to frequency domain through Fourier transformation, changing the parameter representation from temporal to spectral. This parameter transformation allows the system to extract features that are invariant to multipath fading effects, thereby maintaining identification accuracy across different environmental conditions.
2Measurement precision
If feature values are extracted from repeated signals in the presence of reception noise, then the identification process can proceed with available data, but the accuracy deteriorates at low signal-to-noise ratios
Solution Approach 1:
The patent extracts specific feature values from the Fourier-transformed signal sections by isolating and removing noise components. Through spectral analysis, the system separates the desired signal characteristics from reception noise, extracting only the relevant features needed for identification while discarding noise-contaminated portions.
Solution Approach 2:
The patent converts the harmful effect of reception noise into a beneficial filtering process. By performing Fourier transformation and analyzing the spectral characteristics, the system identifies and exploits the structured nature of the transmitted signal versus the random nature of noise, thereby converting noise presence into an opportunity for selective feature extraction that enhances rather than degrades identification accuracy.
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
The solution enables accurate and stable identification of radio wave emission sources regardless of environmental changes and signal quality, as the feature value is not influenced by multipath fading or noise, improving the reliability and consistency of source specification.
Implementation Method 1
an information-1 section signal processing unit configured to perform Fourier transform on and output an information-1 section designated by the signal detection unit within the baseband signal; an information-2 section signal processing unit configured to perform Fourier transform on and output an information-2 section designated by the signal detection unit within the baseband signal
Data Source
AI summary
A signal detection unit detects, from a baseband signal of a received radio wave, two types of fixed signal sections that have different communication information and are present in sufficiently shorter time than a time period in which influence of fading fluctuates, and designate the detected two types of fixed signal sections as an information-1 section and an information-2 section. An information-1 section signal processing unit clips the information-1 section from the baseband signal, and performs Fourier transform on the clipped information-1 section. An information-2 section signal processing unit clips the information-2 section from the baseband signal, and performs Fourier transform on the clipped information-2 section. A feature value computation unit computes a feature value, based on an output of the information-1 section signal processing unit and an output of the information-2 section signal processing unit.