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Noise Robust Radar HRRP Target Recognition Based on Scatterer Matching Algorithm

Since the signal-to-noise ratio (SNR) directly relates to the distance between the target and the radar for a given noise power and radar power, the noise robustness of a recognition algorithm is very important to increase the recognition distance between the target and the radar in the real application. In this paper, a novel noise-robust recognition method for high-resolution range profile (HRRP) data is proposed to enhance its recognition performance under the test condition of low SNR. The target dominant scatterers are first extracted based on the scattering center model of complex HRRP data via the orthogonal matching pursuit algorithm. Then, a scatterer matching recognition algorithm based on Hausdorff distance is developed with the magnitudes and locations of extracted dominant scatterers used as the feature patterns. Here, the noise reduction is accomplished based on the sparse distribution property of dominant scattering centers in a target. Experimental results on the synthetic and measured HRRP data demonstrate that the proposed method can improve the recognition performance under the relatively low SNR condition for both orthogonal and superresolution representations of scattering center model.