A radar signal intelligent recognition and parameter estimation method based on time-frequency mask

By constructing the TFM-RSPEN network, the problems of accuracy and versatility in radar signal identification and parameter estimation under noisy scenarios were solved, achieving high-precision identification and parameter estimation of radar signals with various modulation types, and improving the effect of radar signal processing.

CN122430795APending Publication Date: 2026-07-21UNIV OF ELECTRONICS SCI & TECH OF CHINA +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Filing Date
2026-04-24
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies for radar signal identification and parameter estimation in noisy scenarios suffer from poor universality and low accuracy in parameter estimation, especially for non-stationary signals and radar signals with multiple modulation types, making it difficult to achieve high-precision identification and estimation.

Method used

A radar signal intelligent identification and parameter estimation method based on time-frequency masking is adopted. By constructing a TFM-RSPEN network, signal features are extracted using adaptive thresholding and multi-scale convolutional submodules. The signal modulation type is identified and parameters are estimated by combining a time-frequency-parameter space mapping module. Weighted cross-entropy loss and Dice function are used to optimize network training.

Benefits of technology

It achieves accurate identification and high-precision parameter estimation of various signal modulation types in noisy scenarios. In particular, the identification accuracy is close to 100% under low signal-to-noise ratio conditions, and the bandwidth and pulse width estimation errors are less than 0.1, which is significantly better than traditional methods.

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Abstract

The application belongs to the technical field of radar signal processing, and proposes a radar signal intelligent recognition and parameter estimation method based on time-frequency mask: firstly, original radar signals containing multiple signal modulation types under multiple signal-to-noise ratios are simulated and modulation parameters are modulated; secondly, time-frequency features of the simulated radar signals are extracted, and a training set and a test set are formed based on the time-frequency features; then, signal pixel labels of each signal modulation type are obtained by using an adaptive threshold; next, a TFM-RSPEN network is constructed and trained based on the training set, and the TFM-RSPEN network is supervised and learned by using the signal pixel labels; finally, the radar signal modulation types under multiple signal-to-noise ratios in the test set are identified and parameter estimation is performed by using the trained TFM-RSPEN network, and signal modulation type identification results and parameter estimation results are obtained. The application can improve the recognition rate and parameter estimation accuracy of multiple radar signals in a noise environment.
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