Machine Learning RF Band Segmentation for Accurate Signal Detection
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Solution Overview
Problem
Challenges exist in efficiently sampling and detecting radio frequency signals due to the complexity of radio spectrum sensing, particularly in licensed spectrum enforcement and physical perimeter security.
Innovation Solution
A machine learning model is trained using labeled radio frequency signal samples to detect, segment, and classify signals by transforming energy into a time-frequency representation, employing techniques like FFT and wavelet transforms, and using neural networks for bounding box regression to accurately identify signal types and likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radio spectrum sensing methods are used to detect and classify signals, then the system can identify signal presence, but the detection accuracy is insufficient and false positives/negatives occur
Solution Approach 1:
The patent transforms the radio frequency signal from time-domain to time-frequency domain using Fourier transform and wavelet transform, changing the representation parameters to enable better signal characterization and classification, thereby improving detection accuracy and reducing false positives
Solution Approach 2:
The patent replaces traditional signal processing methods with machine learning models (neural networks, support vector machines, random forests) that automatically learn signal features and classification rules from training data, improving both detection accuracy and reliability without manual feature engineering
2Measurement precision
If comprehensive signal analysis is performed to improve detection accuracy, then signal classification improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preprocessing operations (Fourier transform, wavelet transform, feature extraction) before classification to transform the signal into a more suitable representation, making the subsequent classification faster and more accurate by reducing the computational burden during real-time detection
Solution Approach 2:
The patent divides the signal processing into distinct stages: transformation to time-frequency domain, feature extraction, and classification. This segmentation allows each stage to be optimized independently, balancing accuracy and processing time
3Adaptability or versatility
If machine learning models are trained with diverse radio frequency signals to improve adaptability, then the model can handle varying radio systems and environments, but the training data requirements and system complexity increase
Solution Approach 1:
The patent trains machine learning models with diverse training data representing multiple radio systems, signal types, and environmental conditions, creating a universal classifier that can adapt to varying radio systems and environments without requiring system-specific customization
Solution Approach 2:
The patent uses simulated and synthetic signal data in addition to real measurements for training, creating virtual copies of various signal scenarios to expand training data diversity without proportionally increasing measurement complexity
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
Improves signal detection accuracy, reduces false positives and negatives, and enhances the efficiency of spectrum enforcement and physical perimeter security by enabling rapid identification of signal sources across varying radio systems and environments.
Implementation Method 1
transforming the sampled energy from a time-series representation to a time-frequency representation includes computing at least one of a fast Fourier transform (FFT), a digital Fourier transform (DFT), or a wavelet transform with the sampled energy
Implementation Method 2
transforming the sampled energy from a time-series representation to a time-frequency representation includes computing at least one of a fast Fourier transform (FFT), a digital Fourier transform (DFT), or a wavelet transform with the sampled energy
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for radio frequency band segmentation, signal detection and labelling using machine learning. In some implementations, a sample of electromagnetic energy processed by one or more radio frequency (RF) communication receivers is received from the one or more receivers. The sample of electromagnetic energy is examined to detect one or more RF signals present in the sample. In response to detecting one or more RF signals present in the sample, the one or more RF signals are extracted from the sample, and time and frequency bounds are estimated for each of the one or more RF signals. For each of the one or more RF signals, at least one of a type of a signal present, or a likelihood of signal being present, in the sample is classified.