Airborne radar foresight super-resolution imaging method utilizing sparse statistical property

A super-resolution imaging, airborne radar technology, applied in the application of re-radiation, computing, special data processing applications, etc., can solve the loss of signal-to-noise ratio, complex engineering applications, loss of high-frequency detail information and other problems

Active Publication Date: 2014-06-25
XIDIAN UNIV
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Problems solved by technology

However, due to the low-pass characteristics of the antenna pattern, the high-frequency detail information of the original scene outside the cutoff frequency is l

Method used

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  • Airborne radar foresight super-resolution imaging method utilizing sparse statistical property
  • Airborne radar foresight super-resolution imaging method utilizing sparse statistical property
  • Airborne radar foresight super-resolution imaging method utilizing sparse statistical property

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with accompanying drawing:

[0041] refer to figure 1 , is a flow chart of an airborne radar forward-looking super-resolution imaging method using sparse statistical properties of the present invention. The airborne radar forward-looking super-resolution imaging method utilizing sparse statistical properties includes the following steps:

[0042] S1: The airborne radar receives the original echo signal, performs range pulse compression and range migration correction on the original echo signal in turn, and obtains the data s after range migration correction; constructs the antenna pattern vector according to the measured antenna pattern, and passes The antenna pattern vector is cyclically shifted to obtain the antenna pattern matrix H. The specific instructions are as follows:

[0043] After the airborne radar receives the original echo signal, the original echo signal is subjected to range-wise mat...

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Abstract

The invention belongs to the technical field of airborne radar foresight super-resolution imaging and discloses an airborne radar foresight super-resolution imaging method utilizing sparse statistical property. The method comprises the steps that an antenna pattern matrix H of data s obtained after range migration correction is obtained; airborne radar comprises M distance units; in the data s obtained after range migration correction, the datum corresponding to the ith distance unit is si, and i is a number between 1 and M; si can be represented as the format: si = H.sigma i + n, wherein sigma i represents the foresight scene target scattering coefficient corresponding to the ith distance unit to be solved, and n is set noise; sigma 1 to sigma M are obtained based on the conjugate gradient method according to the Bayes criterion and the airspace sparse property of a target scene, and then super-resolution imaging of the airborne radar under the foresight scene is achieved.

Description

technical field [0001] The invention belongs to the technical field of airborne radar forward-looking super-resolution imaging, in particular to an airborne radar forward-looking super-resolution imaging method using sparse statistical characteristics, which can be used for aircraft ground reconnaissance attacks, aircraft autonomous navigation, ocean search and The end of the missile matches the guidance. Background technique [0002] Airborne radar improves range resolution by transmitting a large time-width-bandwidth product signal, and improves azimuth resolution by using the Doppler frequency shift caused by the relative motion between the airborne platform and the ground target, such as synthetic aperture radar (SAR ) and Doppler Beam Sharpening (DBS), etc. When the airborne radar works under forward-looking conditions, the track direction of the airborne aircraft coincides with the antenna beam pointing, the Doppler bandwidth of the echo signal is zero, and the azimut...

Claims

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Application Information

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IPC IPC(8): G01S13/89G01S7/41G06F19/00
CPCG01S7/41G01S13/904G01S13/9043
Inventor 李明陈洪猛吴艳张鹏卢云龙贾璐张晓伟刘高峰
Owner XIDIAN UNIV
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