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On the basis of classification, rough set theory regards knowledge as partition over data using equivalence relation. Rough set theory is deeply studied in this paper and introduced into the problem of emitter recognition, based on which a new emitter signal recognition model is presented. At the same time, a new method of determining weight coefficients is proposed, which is independent of a prior knowledge. And a new classification rule is also presented in this paper. At last, application example is given, which demonstrates this new method is accurate and effective. Moreover, computer simulation of recognizing radar emitter purpose is selected, and compared with fuzzy pattern recognition and classical statistical recognition algorithm through simulation. Experiments results demonstrate the excellent performance of this new recognition method as compared to existing two pattern recognition techniques. A brand-new method is provided for researching on emitter recognition. [ABSTRACT FROM AUTHOR]

Data association, one of the key and difficult problems for multi-target tracking, is the decision process of linking the measurements or the tracks deemed to be of common origin under certain criteria. All typical data association algorithms can be deducted into special Multidimensional Assignment Problem. However, present S-D assignment algorithm only associates the synchronous measurements from different sensors, which only generate the static result. In this paper, the static assignment algorithm(S-D) has been generalized to the dynamic Multidimensional Assignment Problem by means of combining the measurements set and the tracks set. The main challenge in the assignment problem is that it is NP-hard. The solution using Hopfield neural network (HNN) is presented in this paper. The simulation results illustrate that this method can decrease the computing burden greatly. [ABSTRACT FROM AUTHOR]