基于压缩协作表示的辐射源识别算法

doi:10.7527/S1000-6893.2015.0313

Abstract

Abstract:

Aimed at performance decline of traditional emitter recognition algorithms under the low signal-to-noise ratio (SNR) conditions, an algorithm based on compressively collaborative representation is proposed, which is described from feature extraction and classifier design. The time-domain emitter signal is transformed into a 2-dimensional image through time-frequency analysis, and then a high-dimensional feature vector is extracted with image processing methods. After random compression, the features are input into the proposed classifiers to obtain recognition results. Furthermore, the non-negative constraint is posed to the collaborative representation coefficients, which makes the proposed algorithm more suitable for the practical applications. The simulation results verify the validity of the proposed algorithm and show that its robustness to noise is better when SNR is low and it can be easily realized in engineering due to the low computation burden.

Key words: compressive classifier, collaborative representation, non-negative constraint, emitter recognition, robustness

CLC Number:

ZHOU Zhiwen, HUANG Gaoming, GAO Jun. Emitter recognition algorithm based on compressively collaborative representation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(7): 2251-2258.

References

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Emitter recognition algorithm based on compressively collaborative representation

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