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Radar target recognition method based on dpKMMDP model

A radar target and recognition method technology, applied in the radar field, can solve problems such as the reduction of recognition rate, the separability of difficult data, and the impact on classification performance, and achieve the effect of reducing complexity, obvious effect, and improving recognition rate

Active Publication Date: 2016-10-26
XIDIAN UNIV
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Problems solved by technology

However, the traditional clustering methods such as K-means used in this system need to manually determine the number of subsets (clustering), and the generation of each subset is independent of the back-end classification tasks, so it is difficult to ensure the separability of data in each subset , which affects the global classification performance, but leads to a reduction in the recognition rate

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  • Radar target recognition method based on dpKMMDP model
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Embodiment Construction

[0036] Below in conjunction with accompanying drawing and embodiment, the present invention is described in further detail:

[0037] refer to figure 1 , the present invention comprises the following steps:

[0038] Step 1, receiving the SAR image data of the radar target to generate training data.

[0039] Step 1a) Extract the region of interest ROIs from the entire SAR image, and then preprocess the region, eliminate non-target regions, obtain D-type target region slices, perform feature extraction on the target region, and obtain the training sample set X={x 1 ,x 2 ,...,x n ,...,x N},x n For the nth training sample in the training set X, n=1,2,...,N, N represents the total number of samples in the training sample set X;

[0040] Step 1b) with y={y 1 ,y 2 ,...,y n ,...,y N} to record the category label of each training sample in the training sample set X, y n ∈ {1,2,...,D}, denoting x n The corresponding category label, D indicates the number of types of radar tar...

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Abstract

The invention provides a radar target recognition method based on a dpKMMDP model, and is used for solving the technical problem of low recognition rate of the existing radar target recognition method based on a classifier model. The realization steps are that 1. a radar SAR image training sample set X is extracted; 2. the kernel function matrix G of the training sample set is calculated; 3. the dpKMMDP model is constructed and joint condition posterior distribution of all the parameters of the model is calculated; 4. condition posterior distribution of all the parameters is calculated; 5. the initial value of each parameter is set; 6. cyclic sampling of all the parameters is performed, and the sampling result of the parameters required by the T0 time test phase is saved; 7. a test sample set X<^> is extracted and the hidden variable set Z<^><n> of the test sample set X<^> is calculated; and 8. the hidden variable set Z<^><n> is substituted in an LVSVM classifier to calculate and output the target category number of the test samples. Classification of complex nonlinear classifiable radar SAR image data can be realized, and the method is used for recognition of radar targets.

Description

technical field [0001] The invention belongs to the technical field of radar, and relates to a radar target recognition method based on a classifier model, in particular to a radar target recognition method based on a dpKMMDP model, which can be used to recognize aircraft, warships and tanks in radar SAR images. Background technique [0002] Radar target recognition is a technical means used by radar to identify targets that have been found in its search volume. The principle is to use the radar echo signal of the target to realize the judgment of the target type. With the diversification of war weapons and equipment such as aircraft, warships and tanks, the challenges faced by radar target recognition are becoming more and more severe. [0003] Synthetic Aperture Radar (SAR) is a two-dimensional coherent imaging radar with high resolution. Different from traditional visible light and infrared remote sensing, SAR has the characteristics of all-weather, all-time, low azimuth ...

Claims

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Application Information

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IPC IPC(8): G01S13/90
CPCG01S13/9005G01S13/9027
Inventor 陈渤李晨阳文伟
Owner XIDIAN UNIV
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