Time Reversal Imaging Algorithm Based on Signal-subspaceVectors from the Spatial-frequency Decomposition

ZHONG Xuanming; LI Junye; LIAO Cheng

doi:10.11999/JEIT160272

Volume 39 Issue 2

Feb. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(2): 494-498

ZHONG Xuanming, LI Junye, LIAO Cheng. Time Reversal Imaging Algorithm Based on Signal-subspaceVectors from the Spatial-frequency Decomposition[J]. Journal of Electronics & Information Technology, 2017, 39(2): 494-498. doi: 10.11999/JEIT160272

Citation:

ZHONG Xuanming, LI Junye, LIAO Cheng. Time Reversal Imaging Algorithm Based on Signal-subspaceVectors from the Spatial-frequency Decomposition[J]. Journal of Electronics & Information Technology, 2017, 39(2): 494-498. doi: 10.11999/JEIT160272

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Time Reversal Imaging Algorithm Based on Signal-subspaceVectors from the Spatial-frequency Decomposition

doi: 10.11999/JEIT160272

Funds:

The United Fund of National Natural Science Foundation of China and China Academy of Engineering Physics (U1330109)

Received Date: 2016-03-21
Rev Recd Date: 2016-08-17
Publish Date: 2017-02-19

Abstract

Abstract

Basing on the signal-subspace vectors from the spatial-frequency decomposition, a novel time-reversal imaging algorithm is proposed. Using the backscattered data recorded by the antenna array, a spatial-frequency multistatic matrix is set up. Singular value decomposition is applied to the matrix to obtain the signal-subspace vectors, which are employed to focus the targets imaging selectively. The imaging pseudo-spectrum based on the full backscattered data includes the contributions of multiple sub-vectors and can be viewed as the superposition of multiple images. The algorithm is statistically stable. The random phases, generated by the conventional time-reversal imaging method based on the spatial-spatial decomposition, do not arise in the algorithm. It has excellent capability to resist the noise interference and can accurately focus the multi-targets even when noise with 10 dB SNR is added to the measured data.
- Time-reversal imaging,
- Spatial-frequency imaging,
- Spatial-frequency decomposition,
- Spatial-frequency multistatic matrix

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References(16)

References

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