A fuzzy recognition method of ship target based on isar image

A technology of fuzzy recognition and target classification, applied in character and pattern recognition, instruments, calculations, etc., can solve the problems of mean value and coding error, insufficient description of superstructure curve, low recognition accuracy, etc., and achieve stable mean value, The segmentation results are reasonable and the recognition effect is improved

Active Publication Date: 2019-03-12
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the prior art using uniform segmentation, the segmentation of the upper curve is too simple, the curves in the same structure are divided into different segments, resulting in the error of the mean value and encoding in the segment, and the structural encoding The description of the superstructure curve is not sufficient, resulting in the problem of low recognition accuracy, and a fuzzy recognition method for ship targets based on ISAR images is proposed

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  • A fuzzy recognition method of ship target based on isar image
  • A fuzzy recognition method of ship target based on isar image
  • A fuzzy recognition method of ship target based on isar image

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specific Embodiment approach 1

[0035] Specific implementation mode one: combine figure 1 Describe this embodiment, a kind of ship target fuzzy recognition method based on ISAR image of this embodiment, specifically prepare according to the following steps:

[0036] Step 1, adopt Radon transformation (Radon transformation), extract Centreline (center line) to target ISAR image (inverse synthetic aperture radar), and extract superstructure curve;

[0037] Step 2, using the mean gradient method to adaptively segment the extracted superstructure curve, and calculate the segmented relative mean feature after segmenting;

[0038] Step 3, using training samples to estimate and construct target category templates;

[0039] The known intelligence information of training samples and target categories is set by the user;

[0040] Step 4, using the membership degree principle for the target to be identified and the target category template, calculating and obtaining the fuzzy set form and the membership degree functi...

specific Embodiment approach 2

[0042] Specific embodiment two: the difference between this embodiment and specific embodiment one is: adopt Radon transform (Radon transform) in the described step one, extract Centreline (centreline) to target ISAR image (inverse synthetic aperture radar), and extract the upper layer Structural curve; the specific process is:

[0043] Step 11, extract Centreline;

[0044] Perform full-angle and full-intercept Radon transformation on the target ISAR image to obtain the transformation domain, and find the inclination angle and projection point of Centreline through peak search in the transformation domain; figure 2 shows such a process. Among them, the θ axis represents the rotation angle, and the number of projection direction units represents the number of pixels from the projection point to y′; extract and draw the Centreline; image 3 shows the pair figure 2 Extract the measured data and draw the results of Centreline;

[0045] This method takes advantage of the fact...

specific Embodiment approach 3

[0053] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in the step 2, the mean gradient method is used to perform adaptive segmentation on the extracted superstructure curve, and calculate the segment after the adaptive segmentation Relative mean characteristics; the specific process is:

[0054] 2. Adaptive Segmentation of Superstructure Curve and Improved Coding Features

[0055] 1. The significance of adaptive segmentation

[0056] Superstructure Curve Adaptive Segmentation is an improvement over traditional superstructure coding features. The traditional coding feature is to divide the superstructure curve of the ship into three sections, and use the mean value of the curve in each section to binary encode each section, so as to obtain a fixed-length feature to represent the superstructure of the ship. Compared with directly using superstructure curves as features, ship coding has the advantage of fixed feature dimension, which avoids t...

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Abstract

A ship target fuzzy recognition method based on an ISAR image, the invention relates to a ship target fuzzy recognition method based on an ISAR image. The purpose of the present invention is to solve the prior art using uniform segmentation, the segmentation of the upper curve is too simple, the curves in the same structure are divided into different segments, resulting in the error of the mean value and encoding in the segment, and the structural encoding The description of the superstructure curve is not sufficient, resulting in the problem of low recognition accuracy. The specific process is: 1. Extract the superstructure curve; 2. Calculate the segmented relative mean feature after segmentation; 3. Use training samples to estimate and construct the target category template; 4. Calculate and obtain the fuzzy set of the target to be identified and the target category template Form and membership function; 5. Calculate the degree of closeness between the target to be recognized and the fuzzy set of different category templates, and determine the target to be recognized as the category with the largest degree of closeness. The invention is applied in the field of ship target recognition.

Description

technical field [0001] The invention relates to a ship target fuzzy recognition method based on an ISAR image. Background technique [0002] ISAR is a radar imaging technology that uses stationary radar to image synthetic apertures formed by the relative motion of moving targets. The radar is fixed on the ground or mounted on a ship (corresponding to shore-based ISAR and ship-based ISAR respectively), and there are many targets. as a non-cooperative target. The morphological features of the target are well preserved in high-resolution ISAR images, so the ship target recognition technology based on ISAR images has broad prospects in both military and civilian fields. [0003] The existing research on ship targets based on ISAR images mainly uses the shape and outline features of the target. For ship targets, especially those with similar length and shape, the most obvious difference is reflected in the superstructure of the ship—the ship deck above part. Someone proposed a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/44
CPCG06V10/34G06F18/22
Inventor 王勇朱鹏凯谢俊好李绍滨
Owner HARBIN INST OF TECH
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