The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Extreme learning machine (ELM) is a competitive machine learning technique, which is much more efficient and usually lead to better generalization performance compared to the traditional classifiers. In order to further improve its performance, we proposed a novel ELM called ELM+ which introduces the privileged information to the traditional ELM method. This privileged information, which is ignored...
Radar emitter recognition is an important and challenging subject in radar signal analysis and processing. In this work, an ambiguity function (AF) representative-slice based feature extraction and optimization algorithm is presented for unintentional modulation recognition of moving radar emitters. It considers near-zero slices of AF as representative feature set of radar emitters, which not only...
Radar emitter identification has attracted increasing interests in the last decade. The class-dependent method in to optimize time-frequency kernel of ambiguity function (AF) needs to rank kernel points in the whole AF plane and is sensitive to sampling data length. In this paper, an ambiguity function zero-slice based feature optimization algorithm is proposed for radar emitter recognition. It efficiently...
Radar emitter recognition is of great importance in modern ELINT and ESM systems. The conventional methods for emitter recognition usually use one classifier. For specific emitter recognition, there are slight differences between the feature vectors from radars with the same type. So the recognition result of single classifier is unreliable and instable. In this paper we propose a new combining method...
Radar emitter recognition plays an important role in military automated command and control system. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, a wavelet packet transform is used for feature extraction of unintentional modulation on pulse (UMOP). Then specific radar emitter recognition is achieved by...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.