Time-frequency peak filtering for the recognition of communication signals

Most existing classification methods cannot work in low signal-to-noise ratio (SNR) environments. This limitation motivates the signal filtering before the classification process. In this paper, a general framework that links the time-frequency peak filtering (TFPF) and traditional feature-based sig...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Zhang, Haijian, Bi, Guoan
مؤلفون آخرون: School of Electrical and Electronic Engineering
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2013
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/96727
http://hdl.handle.net/10220/13117
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:Most existing classification methods cannot work in low signal-to-noise ratio (SNR) environments. This limitation motivates the signal filtering before the classification process. In this paper, a general framework that links the time-frequency peak filtering (TFPF) and traditional feature-based signal classification is explored. As the name suggests, TFPF is a filtering approach to encode the received signal as the instantaneous frequency (IF) of an analytic signal, and then the filtered signal is obtained by estimating the peak in the time-frequency domain of the encoded signal. The proposed framework is tested on the recognition of some communication signals. Numerical results demonstrate the effectiveness of this classification scheme for heavily noise corrupted signals. The TFPF based signal classification method exhibits a much better classification performance than the cases where the filtering process is not used.