A Real-time Reconstruction Scheme of Pulsed Radar Echoes with Quadrature Compressive Sampling

ZHANG Suling; XI Feng; CHEN Shengyao; LIU Zhong

doi:10.11999/JEIT150767

Volume 38 Issue 5

May 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(5): 1064-1071

ZHANG Suling, XI Feng, CHEN Shengyao, LIU Zhong. A Real-time Reconstruction Scheme of Pulsed Radar Echoes with Quadrature Compressive Sampling[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1064-1071. doi: 10.11999/JEIT150767

Citation:

ZHANG Suling, XI Feng, CHEN Shengyao, LIU Zhong. A Real-time Reconstruction Scheme of Pulsed Radar Echoes with Quadrature Compressive Sampling[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1064-1071. doi: 10.11999/JEIT150767

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A Real-time Reconstruction Scheme of Pulsed Radar Echoes with Quadrature Compressive Sampling

doi: 10.11999/JEIT150767

Funds:

The National Natural Science Foundation of China (61171166, 61401210, 61571228), China Postdoctoral Science Foundation (2014M551597)

Received Date: 2015-06-29
Rev Recd Date: 2016-02-22
Publish Date: 2016-05-19

Abstract

Abstract

Quadrature Compressive Sampling (QuadCS) is an efficient Analog-to-Information Conversion (AIC) system to sample band-pass analog signals at sub-Nyquist rates. The QuadCS can be widely used in radar and communication systems to acquire sub-Nyquist samples of inphase and quadrature components. However, for wideband or ultra-wideband pulsed radars, it is often impractical to reconstruct Nyquist samples of full-range echoes in real-time because of huge storage and computational loads. Based on the characteristics of QuadCS system, an approximate scheme is proposed to transform the QuadCS measurement matrix into a matrix with a special banded structure. With the banded matrix, a segment-sliding reconstruction method is adopted to perform real-time reconstruction. Simulation results show that with a reasonable approximation of the measurement matrix, the proposed reconstruction scheme achieves nearly optimal reconstruction performance with a significant reduction of data storage and computational time.
- Radar signal processing,
- Segment-sliding reconstruction,
- Quadrature compressive sampling,
- Analog-to- Information Conversion (AIC)

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References

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