Polynomial chirplet approach for frequency modulation signal separation and classification

Bing C. Li

doi:10.1117/12.2518042

3 May 2019 Polynomial chirplet approach for frequency modulation signal separation and classification

Bing C. Li

Proceedings Volume 11003, Radar Sensor Technology XXIII; 110031B (2019) https://doi.org/10.1117/12.2518042
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Separation, detection and classification of multiple frequency modulation radio frequency (FMRF) signals are important in modern communication and radar environments. However, these problems are very challenging when multiple FMRF signals come from same locations, occupy in same frequency bands and transmit at same time. In this paper, we propose manifold representation and polynomial chirplet approach to separate, detect and classify multiple-FMRF-mixed signals. In the proposed approach, each FMRF signal is described by a one-dimensional manifold that is imbedded in a two-dimensional time-frequency space. This one-dimensional manifold is locally characterized by a vector that represents polynomial coefficients. Multiple FMRF signals, which may be inseparable in time and frequency, can be represented by multiple manifolds separable with polynomial coefficients. Through their separable polynomial coefficients, these inseparable FMRF signals can be separated and classified from the characteristics of their manifolds. In this paper, through simulation, we verify the manifold representation ideas and demonstrate that the proposed method can effectively separate, detect and classify multiple-FMRF-mixed signals even though these noisy FMRF signals are overlapped 100% in time and frequency band.

Citation Download Citation

Bing C. Li "Polynomial chirplet approach for frequency modulation signal separation and classification", Proc. SPIE 11003, Radar Sensor Technology XXIII, 110031B (3 May 2019); https://doi.org/10.1117/12.2518042

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