Wideband LFM Signal Parameter Estimation Based on Compressed Sensing Theory

Abstract:

Compressed sensing (CS) theory breaks through the limitations of the traditional Nyquist sampling theorem, and accomplishes the compressed sampling and reconstruction of signals based on sparsity or compressibility. In this paper, CS theory is used to do the parameter estimation of wideband Linear Frequency Modulated (LFM) signal in order to decrease the sampling pressure. A novel method that reconstructs the edge information of the LFM spectrum based on wavelet transform and CS theory is proposed. On the basis that the wideband LFM signal has approximate rectangular spectrum, the wavelet-based edge detection is introduced to provide sparse representation for the signal spectrum. The edges of the spectrum can be reconstructed by the CS reconstruction algorithms. Consequently, the initial frequency and final frequency of wideband LFM signal can be estimated with high estimation precision. The effectiveness of the proposed method is confirmed with numerical simulation.

• Periodical: Applied Mechanics and Materials (Volumes 548-549)
• Pages: 1160-1165
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.548-549.1160
• Online since: April 2014
• Authors: Ning Ma*, Jian Xin Wang
• Keywords: Compressed Sensing, LFM Signal, Parameter Estimation, Wavelet Transform (WT)

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