Improved One-Class SVM (Support Vector Machine) algorithm based multi-mode composite detection target identifying method

Abstract

The invention provides an improved One-Class SVM (Support Vector Machine) algorithm based multi-mode composite detection target identifying method. The method is carried out through the following steps: on the pretreatment of the signals outputted from a passive millimeter wave detector, a long infrared detector and an FMCW radar in steadily scanning state, selecting the features with high distinguishing degree as classification features; using the improved One-Class SVM algorithm based algorithm to train the training data; constructing a classifier with a high target identification rate; extracting the features of a to-be-tested signal; sending the processed feature data to the classifier for target identification; and obtaining the determination result afterwards. According to the invention, the method considers the rapid development of the existing high speed real time signal processing system and the large amount of information to be processed in composite detection, and starting from the perspective of feature layer integration, uses a One-Class SVM algorithm featuring the sample and feature weights, which greatly improves the target identification rate of a multi-mode composite detector.

Description

technical field [0001] The invention belongs to the technical field of target recognition, in particular to a multi-mode compound detection target recognition method and system based on an improved One-Class SVM algorithm weighted by samples and features. Background technique [0002] The fundamental principle of composite detection technology is to use the respective advantages of various sensors such as radar, radiometer, and infrared detector to achieve more accurate identification of targets by fusing and judging sensor information according to certain rules. In the field of data fusion, the feature layer fusion is located between the data layer and the decision-making layer in terms of real-time and information volume, and with the rapid development of high-speed real-time processing technology, it has become a trend of data fusion processing. [0003] The problem of composite detector target recognition is a single-classification problem. The single-classification supp...

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Problems solved by technology

[0003] The problem of composite detector target recognition is a single-classification problem. The single-classification support vector machine introduced by Wang Hongbo and others in the study of the learning method of the single-classification support vector machine can effectively solve the single-classification problem. The values ​​are all of the same importance, but in reality, the importance of samples and eigenvalues ​​is somewhat biased, which is not conducive to the identification of targets

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