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Nowadays, star pattern recognition methods used on star sensors have shortcomings of redundancy match, matching slow, low recognition rate, and so on. To overcome this, the star pattern recognition algorithm was studied, and a new algorithm was proposed. Firstly, redundancy information from traditional star catalog was extracted. The sky coverage was divided to make sure that different sky coverage has the similar navstar number. Secondly， feature information 干rom the photo was extracted to match the feature information database. After the algorithm was designed, the star sensors with the field of view 15° x 15° were selected and simulated. Simulation results show that the matching of the whole sky can be finished in 1.5 s by this algorithm, and the success rate can reach over 98%. [ABSTRACT FROM AUTHOR]

To correctly classify advanced radar emitter signals in the condition of low signal-to-noise ratio, a novel approach is proposed by using image feature of time-frequency distribution for radar emitter signal recognition, which transforms the classification of emitter signals into image processing and image recognition. Time-frequency images of radar emitter signals are obtained by using modified B distribution, and then these images are transformed into grayscale images. In addition, the time-frequency images are enhanced and denoised by image processing methods. Finally, the texture features of time-frequency image are extracted for signal recognition based on the local binary patterns, and the support vector machine is applied to identify radar emitter signals automatically. Simulation results show that the proposed approach can achieve satisfactory accurate recognition in low signal-to-noise rate ( SNR). Even for SNR = OdB, the overall correct classification rate of twelve typical radar emitter signals is 95. 35% . The proposed approach can reduce the impact of noise effectively, and also distinguish the approximate time-frequency images well. The validity of the approach is demonstrated by experimental results. [ABSTRACT FROM AUTHOR]