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Variant target high-resolution range profile recognition method based on block sparse Bayesian learning

A high-resolution range image and Bayesian learning technology, applied in the field of radar, can solve the problems of low signal recognition accuracy and insufficient use of probability distribution information, and achieve the effect of improving performance

Active Publication Date: 2019-01-11
XIDIAN UNIV
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Problems solved by technology

However, this method does not make full use of the probability distribution information of the high-resolution range image of the variant target, so the recovered signal recognition accuracy is low

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  • Variant target high-resolution range profile recognition method based on block sparse Bayesian learning
  • Variant target high-resolution range profile recognition method based on block sparse Bayesian learning
  • Variant target high-resolution range profile recognition method based on block sparse Bayesian learning

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Embodiment Construction

[0027] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0029] Step 1. Model the high-resolution range image of the variant target.

[0030] Establish the high-resolution range image mathematical model of the variant target, which is expressed as follows:

[0031] y=Dx+w,

[0032] where the high-resolution distance image y∈R M×1 , M is the dimension of the high-resolution range image, R M×1 Represents a set of real matrix sets with M rows and 1 columns, D∈R M×N is a dictionary matrix that sparsely represents the variant high-resolution range image, N is the number of columns of the dictionary matrix, and N=M+50, x∈R N×1 is the sparse representation of y on the dictionary D, w∈R M×1 for noise.

[0033] Step 2, construct the dictionary matrix D.

[0034] 2a) Take 500 M-dimensional high-resolution range im...

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Abstract

The invention provides a variant target high-resolution range profile recognition method based on block sparse Bayesian learning, which mainly solves the problem that the prior art does not fully utilize the probability distribution information of the variant target high-resolution range profile and leads to low variant target recognition rate. The realization scheme is as follows: 1. establishingthe mathematical model of a high-resolution range profile of a variant object; 2. defining the priori probability of each variable and the priori distribution of the priori probability parameter in the mathematical model of the variant target; 3. obtaining that variant component in the high-resolution range profile of the variant object by iteratively solving the model through the block sparse Bayesian learning; 4. removing variant components from the variant high resolution range profile, recovering variant-free high resolution range profile, recognizing the recovered variant-free high resolution range profiles with an adaptive Gaussian classifier, and obtaining the target class. The invention improves the accuracy of variant target recognition and can be used for radar automatic targetrecognition.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a high-resolution range image recognition method for a variant target, which can be used for radar target recognition. Background technique [0002] With the continuous development of high-resolution radar, radar target recognition becomes more feasible. The existing technology mainly uses three kinds of signals for radar target recognition: synthetic aperture radar image signal, inverse synthetic aperture radar image signal, and radar high-resolution range image signal. Signal. Among them, the SAR image and the inverse SAR image are two-dimensional images, and the lateral resolution of the scattering center of the target is obtained by analyzing the Doppler frequency shift generated by the relative rotation between the target and the radar. The radar high-resolution distance is like a one-dimensional image, which is the vector sum of the echoes of the target scatterin...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2136G06F18/24155
Inventor 王鹏辉刘宏伟孟亦然宋晓龙纠博王英华
Owner XIDIAN UNIV