Method for detecting small targets of sea surface by utilizing least squares support vector machine (LS-SVM) on basis of wavelet noise reduction

A technology of small target detection and wavelet noise reduction, applied to radio wave measurement systems, instruments, etc., to reduce prediction time, simplify calculation complexity, and improve detection performance

Inactive Publication Date: 2012-07-04
HARBIN ENG UNIV
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Problems solved by technology

[0004] Although there have been many studies on the detection of small targets on the sea surface, none of them have been able to provide a comp...

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  • Method for detecting small targets of sea surface by utilizing least squares support vector machine (LS-SVM) on basis of wavelet noise reduction
  • Method for detecting small targets of sea surface by utilizing least squares support vector machine (LS-SVM) on basis of wavelet noise reduction
  • Method for detecting small targets of sea surface by utilizing least squares support vector machine (LS-SVM) on basis of wavelet noise reduction

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Embodiment Construction

[0022] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0023] to combine Figure 1~4 , the specific implementation process is as follows:

[0024] (1) Select a piece of original sea clutter data x 0 , first use the wavelet decomposition method to denoise, and then use the GP method or the Wolf method to judge its chaos, judge that it is a chaotic sequence, and then extract the embedding dimension n and time delay τ;

[0025] (2) Take another piece of original sea clutter data x 1 , perform wavelet decomposition and denoising, filter sea clutter noise, especially sea spike noise that is likely to cause false alarms, and perform normalization processing;

[0026] (3) The particle swarm optimization algorithm searches for the optimal parameters c and g of the support vector machine regression. First initialize the population and speed, then continuously update the particle speed according to the fitness function valu...

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Abstract

The invention aims to provide a method for detecting small targets of a sea surface by utilizing a least squares support vector machine (LS-SVM) on the basis of wavelet noise reduction. The method comprises the steps that: a section of original sea clutter data is selected, is de-noised and is then subjected to chaos judgment, and embedded dimensions and time delays are extracted; another sectionof original sea clutter data is selected, is subjected to wavelet decomposition and denoising and is normalized; the best regression parameters of the SVM are searched; obtained data are trained by using the LS-SVM as a tool; another group of sea clutter data is selected, is preprocessed and is then predicted by using an obtained sea clutter model, so short-term prediction data corresponding to the data are obtained; the obtained short-term prediction data are subtracted from a data value which is only preprocessed, and absolute values of the difference values are obtained, so as to obtain absolute errors; the absolute errors are classified by using the LS-SVM which is optimized by using particle swarms, two-dimensional information classification is equivalent to threshold limitation, andsmall target detection in a strong sea clutter background is finally realized. By adopting the method, the false-alarm probability is greatly reduced, and thereby the detection performance is improved.

Description

technical field [0001] The invention relates to a target detection method in the technical field of radar signal processing. Background technique [0002] Sea surface target detection (especially small target detection on the sea surface) technology of radar plays a very important role in civilian applications. In various detection methods, it is very important to deal with the noise in the background environment where the target is located, which directly affects the performance of target detection. The difficulty of correctly describing the motion of sea clutter makes small target detection in sea clutter one of the most complex problems in radar signal processing. [0003] Reports relevant to the present invention include: 1. "Research on chaotic time series forecasting based on least squares support vector regression" (the 3rd phase of the 24th volume of "Academy of Naval Aeronautical Engineering" in 2009), which introduces the core theory of support vector machine And...

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Application Information

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IPC IPC(8): G01S7/41
Inventor 马惠珠李莹
Owner HARBIN ENG UNIV
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