Enhancing LPI Radar Signal Classification Through Patch-Based Noise Reduction

This letter presents a novel patch-based noise reduction framework designed to enhance the performance of Low Probability of Intercept (LPI) radar waveform classification. The proposed approach capitalizes on the unique characteristic of the waveform's Time-Frequency Images (TFIs) being concentrated in the central region of the image. By partitioning the noisy image into multiple patches, each patch is independently processed using convolutional autoencoders. This method effectively eliminates noise and restores the signal in low Signal-to-Noise Ratio (SNR) environments, thus mitigating interference between the signal and noise components. Simulation results demonstrate the superior performance of the proposed method, achieving an 11% improvement in accuracy compared to classifying noisy images at a -10 dB SNR.