基于不确定性模型和多字典的稀疏贝叶斯学习
Sparse Bayesian learning with uncertainty models and multiple dictionaries
摘要：
稀疏贝叶斯学习（SBL）已经成为一种快速和有竞争力的方法来执行稀疏处理。SBL算法，这是使用贝叶斯框架开发的，近似地解决了非凸优化问题，使用固定点更新。它提供了相当的性能，并且比稀疏处理中使用的凸优化技术快得多。我们提出了一个信号模型，占字典不匹配和存在的错误，在低信噪比的权重向量。推导了一个包含失配和加权误差统计量的定点更新方程。我们还处理来自多个字典的观察结果。使用随机极大似然估计噪声方差。推导出的更新方程进行了定量研究，使用波束成形模拟应用于到达方向（DoA）。使用单频和多频观测，并在混叠的存在下，SBL的性能进行评估。SwellEx-96实验数据定性地证明了SBL的优点。
英文摘要：
Sparse Bayesian learning (SBL) has emerged as a fast and competitive method to perform sparse processing. The SBL algorithm, which is developed using a Bayesian framework, approximately solves a non-convex optimization problem using fixed point updates. It provides comparable performance and is significantly faster than convex optimization techniques used in sparse processing. We propose a signal model which accounts for dictionary mismatch and the presence of errors in the weight vector at low signal-to-noise ratios. A fixed point update equation is derived which incorporates the statistics of mismatch and weight errors. We also process observations from multiple dictionaries. Noise variances are estimated using stochastic maximum likelihood. The derived update equations are studied quantitatively using beamforming simulations applied to direction-of-arrival (DoA). Performance of SBL using single- and multi-frequency observations, and in the presence of aliasing, is evaluated. SwellEx-96 experimental data demonstrates qualitatively the advantages of SBL.
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