ML 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 system utilizing machine learning models trained on labeled datasets to perform radio frequency band segmentation and signal detection, capable of classifying signal types and likelihoods through time-frequency analysis and bounding box regression, adaptable to different radio systems and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional radio spectrum sensing methods are used to detect signals, then signal detection can be performed, but the process becomes complex and inefficient
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with machine learning models. Specifically, neural networks and other ML algorithms are used to automatically detect, classify, and analyze radio frequency signals, substituting complex manual or rule-based spectrum sensing mechanisms with adaptive computational models that learn optimal detection strategies from training data.
Solution Approach 2:
The patent transforms the radio frequency signal data from time-domain representations to frequency-domain representations through Fourier transforms. This parameter transformation enables the machine learning models to operate more effectively on spectral features, changing the state of the data to make it more suitable for automated analysis and classification.
2Measurement precision
If machine learning models are trained on diverse radio signal data, then detection accuracy improves, but training data requirements and processing complexity increase
Solution Approach 1:
The patent performs preliminary data preparation and augmentation before training the machine learning models. This includes collecting diverse radio frequency signal samples, annotating them with ground truth labels, and preparing comprehensive training datasets in advance. By performing these actions beforehand, the system reduces the computational burden during actual signal detection and improves model accuracy without requiring excessive real-time processing.
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.