基于空域稀疏性的自适应频谱检测算法

于宏毅; 程标; 胡赟鹏; 沈智翔

doi:10.11999/JEIT151030

基于空域稀疏性的自适应频谱检测算法

doi: 10.11999/JEIT151030

基金项目:

国家自然科学基金(61501517)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-09-10
- 修回日期: 2016-01-22
- 刊出日期: 2016-07-19

Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity

Funds:

The National Natural Science Foundation of China (61501517)

摘要

摘要: 现有的频谱检测算法没有充分利用信号在角度维的稀疏性质。该文根据角度维的稀疏特性建立信号模型，通过稀疏贝叶斯学习(Sparse Bayesian Learning, SBL)算法解决稀疏信号的重构问题，并在迭代过程中引入二元假设检验思想，推导出一种自适应门限的选取策略，把传统的重构算法转化为一个针对不同来波方向的信号检测问题。该算法能够在恒虚警概率下对多信号进行全盲检测，同时实现信号来波方向的精确估计。实验结果证明，自适应判决方法能够有效地提高稀疏重构算法的重构精度，降低运算复杂度，参数估计精度和信号检测性能相比于现有算法得到明显的提升。
- 频谱检测 /
- 稀疏贝叶斯学习 /
- 恒虚警概率 /
- 自适应门限
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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参考文献(26)

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