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Automatic Modulation Recognition for Radio Mixed Proximity Sensor Signals Based on a Time-Frequency Image Enhancement

Jinyu Zhang¹, Xiaopeng Yan¹, Xinhong Hao¹

¹Beijing Institute of Technology, School of Mechatronics Engineering, Beijing 100081, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This study introduces an enhanced method for automatic modulation recognition (AMR) of low probability intercept (LPI) signals using a TFI enhancement network. The approach significantly improves recognition accuracy, especially in low signal-to-noise ratios (SNRs).

Keywords:

LPI radio frequency proximity sensors deep convolutional neural network image restoration signal recognition

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Area of Science:

Signal Processing
Machine Learning
Electronic Warfare

Background:

Automatic modulation recognition (AMR) is crucial for electronic reconnaissance of low probability intercept (LPI) signals.
Traditional methods struggle with accuracy under low signal-to-noise ratios (SNRs).
Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise for signal classification using time-frequency images (TFIs).

Purpose of the Study:

To propose an improved AMR method for radio frequency proximity sensor signals, focusing on enhancing accuracy in low SNR conditions.
To develop a TFI enhancement network for denoising and improving the quality of input features for classification.

Main Methods:

Utilized a per-pixel kernel prediction network (KPN) for denoising time-frequency images (TFIs), enhancing signal quality.
Employed a RetinalNet-based classifier to process the enhanced TFIs for modulation recognition.
Compared denoising performance against traditional methods like sparse representation and low-rank approximation.

Main Results:

The KPN-based TFI enhancement achieved comparable denoising performance to traditional methods with significantly lower computational overhead.
The proposed method demonstrated high accuracy in identifying modulation types of radio frequency proximity sensors, even when aliased in the time-frequency domain.
Achieved an average recognition accuracy rate exceeding 97% at SNRs above -10 dB.

Conclusions:

The TFI enhancement network effectively improves the quality of input features for AMR, crucial for low SNR environments.
The proposed deep learning-based AMR method offers a robust and computationally efficient solution for complex signal recognition tasks.
The method shows significant potential for applications in electronic reconnaissance and signal intelligence.