Spacecraft classifying and identifying method based on generalized Hough transformation

Abstract

The invention discloses a spacecraft classifying and identifying method based on generalized Hough transformation, which comprises the following steps of: drawing a radar cross-section (RCS) sequence image aiming at the RCS sequence of a certain radar observation arc section, calculating the compactness of the pixels of the RCS sequence image, carrying out initial classification, filtering the RCS sequence image, also generating a profile diagram and determining the image of a coarse positioning area by using generalized Hough transformation; extracting the skeleton characteristics of the image of the coarse positioning area and carrying out fine matching with the skeleton characteristic of the image of a template; and finally, carrying out matching verification based on the compactness degree of the pixels. According to the spacecraft classifying and identifying method based on the generalized Hough transformation, the aim of automatic target identification can be realized, and the identification rate is also enhanced.

Description

technical field [0001] The invention belongs to the field of aerospace measurement and control, and relates to a method for classifying and identifying spacecraft using RCS sequences. Background technique [0002] At present, most of the active radars in our country adopt the narrow-band radar system, so exploring and researching the space target recognition problem under the narrow-band radar system has important practical significance and extensive practical value. Target RCS contains rich target information and is the main data of target radar characteristics that narrowband radar can obtain. Therefore, target classification and recognition using RCS measurement data is an important aspect of space target radar recognition. Since the radar has different observation angles for the spacecraft in different observation arcs, and the RCS is sensitive to the change of the attitude angle, the statistical characteristics and transformation characteristics of the RCS sequence of e...

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Problems solved by technology

Based on the radar measurement data of 7 targets and 10 observation arcs, Jin Sheng, Gao Meiguo and others used the nearest neighbor method to identify them using the above statistical features. The results showed that this method can only be used for preliminary classification of spacecraft with large differences in shape Recognition, and the recognition rate is low, roughly 70%

In the transformation feature, the traditional Fourier transform is a global transformation, which cannot express the time-frequency local properties of the RCS sequence. Although the short-time Fourier transform has been improved, it is still a single resolution in essence. The signal analysis method still has limitations in the application of RCS sequences showing non-stationary signal characteristics. Compared with the above methods, the time-frequency analysis method has great advantages, but in practical applications, the recognition performance of the target is not Well, it is basically in the stage of theoretical exploration. In practice, it is mainly used to judge the stability of the target attitude

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