Self-Supervised Neural Network for Radar Signal Detection in Noise
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Solution Overview
Problem
Localizing a radar signal in noise is challenging, especially in spectrum monitoring settings where the radar pulse shape is unknown, and large labeled datasets required for machine learning are often unavailable.
Innovation Solution
A self-supervised machine-learning system comprising a receiver, an encoding neural network, a decoding neural network, and a gating neural network identifies radar or intermittent signals by compressing and decompressing sampled sequences while suppressing noise, using a confidence sequence to determine signal presence without prior knowledge of the pulse shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radar signal detection methods are used with known pulse shape, then detection accuracy is improved, but the system cannot detect unknown radar signals
Solution Approach 1:
The system uses self-supervised learning where the neural network learns to detect radar signals by training on unlabeled data without requiring prior knowledge of pulse shapes. The model serves itself by automatically learning features from raw data, eliminating the need for manual annotation or predefined signal characteristics.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed parameter-based detection (relying on known pulse shapes) to a data-driven parameter learning approach where the neural network automatically discovers relevant parameters and features from the input data, enabling detection of unknown signal types.
2Reliability
If large labeled datasets are used for machine learning training, then model performance is improved, but such datasets are often unavailable for radar applications
Solution Approach 1:
The system employs self-supervised learning where the model generates its own training signals through contrastive learning on unlabeled data. By creating positive and negative samples from the same data distribution without requiring external labels, the system achieves reliable performance despite the unavailability of large labeled datasets.
Solution Approach 2:
The patent introduces an intermediary contrastive learning mechanism that bridges the gap between unlabeled data and meaningful training signals. The intermediary loss function creates artificial supervision signals from the data itself, enabling effective training without external labels.
3Measurement precision
If noise is present in the sampled sequence, then signal detection becomes more difficult, but the system must still identify radar signals accurately
Solution Approach 1:
The neural network extracts the radar signal components from the noisy sampled sequence by learning to separate signal from noise through contrastive learning. The model identifies and extracts meaningful signal features while filtering out noise patterns that do not contribute to signal identification.
Solution Approach 2:
The system converts the presence of noise into a beneficial training opportunity by using contrastive learning where noise-corrupted samples serve as negative examples. This forces the model to learn robust features that are invariant to noise, ultimately improving detection accuracy in noisy environments.
Data Source
AI summary
A self-supervised machine-learning system identifies whether an intermittent signal is present. The system includes a receiver, an encoding neural network, a decoding neural network, and a gating neural network. The receiver detects radiation and from the detected radiation generates a sampled sequence including sampled values describing the intermittent signal and noise. The encoding neural network is trained to compress each window over the sampled sequence into a respective context vector having a fixed dimension less than an incoming dimension of the window. The decoding neural network is trained to decompress the respective context vector for each window into an interim sequence describing the intermittent signal while suppressing the noise. The gating neural network is trained to produce a confidence sequence from a sigmoidal output based on the interim sequence. Despite the noise, the confidence sequence identifies whether the intermittent signal is present in each sampled value in the sampled sequence.