Fpga Based Realization of Aic for Applying Cs to Radar

Chen, Xiuwei; Zhang, Yunhua; Zhang, Xiangkun

doi:10.2528/pierc10120805

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…As we know, the application of CS has two preconditions that: (1) the under reconstructed signal is sparse or compressible, (2) the measurements are incoherent. Since many SAR/ISAR images are sparse or compressible in some proper bases, the CS theory has shown very good prospects in SAR/ISAR applications [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Novel Sparse Stepped Chaotic Signal and Its Compression Based on Compressive Sensing

Yang¹,

Zhang²

2013

PIER

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Abstract-We propose a novel signal model by combining the sparse stepped frequency signals with chaotic signals, i.e., the sparse stepped chaotic signal (SSCS) model, as well as the corresponding compression algorithm based on compressed sensing. In SSCS, the chaotic signals are modulated to sparse stepped frequencies to compose a transmitting burst. When receiving, the echo signals are demodulated to the baseband and then can be sampled directly at a rate much lower than the Nyquist rate determined by the bandwidth of chaotic signal of each subpulse. Compared with radars using conventional stepped frequency waveforms, the SSCS radar can transmit fewer subpulses in a burst and directly use lower speed ADC next to the receiver. Both simulated and real radar data are processed to demonstrate the effectiveness of the proposed SSCS as well as the compression algorithm by which high resolution range profiles are very well reconstructed.

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