Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity

YU Hongyi; CHENG Biao; HU Yunpeng; SHEN Zhixiang

doi:10.11999/JEIT151030

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1703-1709

YU Hongyi, CHENG Biao, HU Yunpeng, SHEN Zhixiang. Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030

Citation:

YU Hongyi, CHENG Biao, HU Yunpeng, SHEN Zhixiang. Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030

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Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity

doi: 10.11999/JEIT151030

Funds:

The National Natural Science Foundation of China (61501517)

Received Date: 2015-09-10
Rev Recd Date: 2016-01-22
Publish Date: 2016-07-19

Abstract

Abstract

The existing spectrum detection method can not take full advantage of angle dimension. To sense the spectrum more comprehensively, the signal model is established based on the sparsity of angle dimension. The reconstruction result can be derived by Sparse Bayesian Learning (SBL) algorithm. By integrating the binary probability hypothesis into iterative procedure of SBL, a decision test combined with adaptive threshold is derived. The proposed pruning step can accept the active components of the model, and transform the sparse recovery into a detection problem for signals from different angles. Therefore, the algorithm can sense the spectrum blindly with constant false-alarm rate as well as estimate the accurate angle of each incident signal. Numerical simulation results verify that adaptive threshold can improve reconstruction accuracy with low computing cost. Moreover, the proposed algorithm can achieve better estimation and detection performance than previous algorithms.
- Spectrum detection,
- Sparse Bayesian Learning (SBL),
- Constant false-alarm rate,
- Adaptive threshold

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References(26)

References

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