Radar range profile statistics and recognition method based on PPCA model in strong noise background

Abstract

The invention discloses a radar range profile statistics and recognition method based on a PPCA model in the strong noise background, which relates to the technical field of radar automatic target recognition and mainly solves the problem that the current statistics and recognition methods based on the PPCA model are not robust to noises. The training phase comprises the following steps: framing, translating, aligning and strength-normalizing radar HPPR continuously, learning the parameters of each azimuth frame of the PPCA model by adopting the processed HRRP and storing a template. The test phase comprises the following steps: first strength-normalizing, translating and aligning the samples to be tested and then estimating the range of the signal-to-noise ratios (SNR) of the samples; computing the distance value of each frame of each target and deciding the category attribute if the SNR is more than 30dB, and rewriting the distance value, solving the noise energy under SNR condition by minimizing the distance value, finally computing the distance value of each frame of each target and deciding the category attribute if the SNR is less than 30dB. The method has the advantages of robustness to noises and less computation and is applied to identifying radar targets.

Description

technical field [0001] The invention belongs to the technical field of radar automatic target recognition, in particular to a statistical recognition method for radar range images under strong noise background based on PPCA model. Background technique [0002] The automatic targeting technology of one-dimensional high-resolution range profile can be traced back to the 1980s. Because the one-dimensional high-resolution range image HRRP can provide the geometric structure information of the target along the distance direction, and has the unique advantage of being easy to obtain and process; at the same time, the radar has the characteristics of all-weather and all-weather, and the radar one-dimensional high-resolution range image automatic target recognition received widespread attention. [0003] The recognition method based on statistical model is an important radar HRRP automatic target recognition method. The statistical identification of one-dimensional high-resolution...

AI Technical Summary

Problems solved by technology

[0017] Although the traditional PPCA model considers the statistical modeling of noise components, the actual battlefield environment is complex, and the noise intensity in the radar echo is affected by the distance from the target to the radar, the reflection characteristics of the specific azimuth of the target, and atmospheric conditions. It is usually difficult to maintain the signal of the test sample. The noise ratio is exactly the same as the training sample, especially for long-distance non-cooperative targets in the battlefield environment, the HRRP signal-to-noise ratio is lower

Therefore, the noise components of the actual test samples and the training samples are mismatched: in addition, since the range image samples are preprocessed by energy normalization to overcome the intensity sensitivity, the signal components of the test samples and the training samples are also mismatched, so directly Using the traditional PPCA model to identify samples under the condition of low signal-to-noise ratio will cause the recognition rate to drop, especially when the noise is large, the recognition rate is lower

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