Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
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发表时间：
2018-03
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通讯作者：
Jiang Zhu;Qi Zhang-;P. Gerstoft;Mihai-Alin Badiu;Zhiwei Xu
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作者：
Jiang Zhu;Qi Zhang-;P. Gerstoft;Mihai-Alin Badiu;Zhiwei Xu
本文利用贝叶斯方法研究了具有多个测量矢量的线谱估计(LSE)问题。受最近提出的变分线谱估计(VALSE)方法的启发，我们发展了适用于多快照场景的多快照值(MVALSE)，它在阵列信号处理中特别重要。MVALSE具有Valse方法的优点，如自动估计模型阶数、噪声方差、权方差，并提供频率估计的不确定程度。结果表明，MVALSE可以看作是对每个快照应用具有单一测量向量(SMV)的值，并适当地组合中间数据。此外，还开发了用于进行序贯估计的序列-MVALSE。最后，数值结果验证了MVALSE方法的有效性，并与MMVS环境下的最新方法进行了比较。
In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) method, we develop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which is especially important in array signal processing. The MVALSE shares the advantages of the VALSE method, such as automatically estimating the model order, noise variance, weight variance, and providing the uncertain degrees of the frequency estimates. It is shown that the MVALSE can be viewed as applying the VALSE with single measurement vector (SMV) to each snapshot, and combining the intermediate data appropriately. Furthermore, the Seq-MVALSE is developed to perform sequential estimation. Finally, numerical results are conducted to demonstrate the effectiveness of the MVALSE method, compared to the state-of-the-art methods in the MMVs setting.