A Scanning Radar Target Detection Method

Abstract

The invention provides a scanning radar target detection method, which belongs to the technical field of radar target detection. The present invention first uses the deconvolution technology to perform super-resolution processing on the acquired real beam image, and performs a sliding window preprocessing operation on the super-resolution result. Then, using the low-rank characteristics of the preprocessing results and the sparse characteristics of the target of interest, the objective function is constructed, the sparse target detection problem is converted into an optimization problem of matrix low-rank sparse decomposition, and the sparse target is finally achieved by iteratively solving the objective function detection. Compared with existing methods, this method can effectively reduce false alarms and achieve better detection of sparse objects of interest.

Description

Technical field [0001] The present invention belongs to the field of radar target detection, and in particular, the present invention relates to a scanning radar target detection method. Background technique [0002] Scanning radar has all day, all-weather work ability, has been widely used in multiple fields of military and civil, including sea surface monitoring, search rescue, and more. Target detection is one of the important functions of scanning radar. There are many existing target detection algorithms, which can be roughly divided into two categories: sequence image detection algorithm and single image detection algorithm. The sequence image detection algorithm is estimated the target by processing a continuous multi-frame image, but the background rapid change in the mobile platform and the discontinuous discontinuity of the target make the sequence image detection algorithm have decreased significantly, and the class algorithm is required to continuously multi-frame ima...

AI Technical Summary

Problems solved by technology

The sequence image detection algorithm is to estimate the target by processing continuous multi-frame images, but the rapid change of the background and the discontinuity of the target caused by the mobile platform make the performance of the sequence image detection algorithm drop significantly. processing, resulting in higher computational complexity

[0003] For the single image target detection algorithm, the prior knowledge of the background image is used to realize the target detection by means of background cancellation, but for the moving platform, the lack of relevant background knowledge, and the scene changes quickly, resulting in fewer false alarms after cancellation. many

The target detection method based on the two-dimensional least mean square filter based on neighborhood analysis, because this method is to enhance the target by filtering and estimating the background to achieve target detection, in the case of low sex-to-noise ratio, the estimation of the background by this method is seriously inaccurate. Accuracy makes the target detection performance drop rapidly, causing serious false alarms

There is also a mean square error constant false alarm detection algorithm for SAR images. The detection effect of this method is related to the distribution of background clutter. However, there is currently no standard clutter model to choose from. When there is a difference between the sample and the clutter model The detection performance drops significantly when

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