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An effective approach to classify the radar emitter signals is presented, which is based on a cascade feature extractions and a hierarchical decision technique. Firstly, the instantaneous autocorrelation, improved by non-ambiguity phase expansion and moving average, is used to extract the primary instantaneous frequencies of radar signals. Then, a(More)
Radar emitter signal recognition plays an important role in electronic intelligence systems and electronic support measure systems. To heighten accurate recognition rate of radar emitter signals, this paper proposes a hierarchical classifier structure to recognize radar emitter signals. The proposed structure combines resemblance coefficient classifier,(More)
New insight into coding for avalanche photo diode (APD) based direct-detection optical channels is obtained using a simple channel model that clearly points out the difference between the signal-dependent optical noise channel model and the well-known additive white Gaussian noise (AWGN) channel model. Coding and modulation are viewed as a single entity and(More)
Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA)(More)
Feature selection is always an important and difficult issue in pattern recognition, machine learning and data mining. In this paper, a novel approach called resemblance coefficient feature selection (RCFS) is proposed. Definition, properties of resemblance coefficient (RC) and the evaluation criterion of the optimal feature subset are given firstly.(More)