Deterministic and Bayesian Sparse Signal Processing Algorithms for Coherent Multipath Directions-of-Arrival (DOAs) Estimation
Deterministic and Bayesian Sparse Signal Processing Algorithms for Coherent Multipath Directions-of-Arrival (DOAs) Estimation
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DOI：
10.1109/joe.2018.2851119
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发表时间：
2019-10
影响因子：
4.1
通讯作者：
Anup Das
中科院分区：
文献类型：
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作者：
Anup Das
在本文中，我们提出了新的确定性和贝叶斯方法来识别相干多径信号的到达方向（DOA），假设有非常少的多径信号存在和多个快照可用。对于这两种类型的方法，我们利用稀疏性和相干信号传播的基本结构。对于确定性的情况下，我们制定的相干多径信号的DOA估计问题作为一个双凸优化问题，然后解决它的交替凸搜索方法。这与广泛使用的稀疏信号恢复凸优化问题相反，该问题仅利用所考虑的信号的稀疏性。对于贝叶斯的情况下，我们提出了两个新的算法的基础上平均场变分贝叶斯期望最大化方法。第一种算法假设多径信号的真实场景DOA与角网格精确对齐。接下来，我们扩展的网格模型来处理离网问题，这是本文提出的第二个贝叶斯算法。这两个贝叶斯算法是在广泛使用的稀疏贝叶斯学习相关向量机算法，它只利用空间域的稀疏性。仿真结果表明，不同算法的DOA估计均方误差有较大差异。最后，我们展示了所提出的离网贝叶斯算法的应用程序，通过分析数据从浅水HF$\mathbf {97}$海洋声学实验。
In this paper, we propose novel deterministic and Bayesian methods to identify the directions-of-arrival (DOAs) of coherent multipath signals assuming that a very few number of multipath signals are present and multiple snapshots are available. For both types of methods, we exploit both sparsity and the underlying structure of coherent signal propagation. For the deterministic case, we formulate the problem of estimating the DOAs of coherent multipath signals as a biconvex optimization problem and then solve it by an alternating convex search approach. This is in contrast to the widely used $\ell _1$ sparse signal recovery convex optimization problem, which only exploits sparsity of the signal under consideration. For the Bayesian case, we propose two novel algorithms based on the mean field variational Bayesian expectation maximization approach. The first algorithm assumes that the true scenario DOAs of the multipath signals are exactly aligned with the angular grids. We next extend the on-grid model to deal with the off-grid problem, which is the second proposed Bayesian algorithm of this paper. These two Bayesian algorithms are in contrast to the widely used sparse Bayesian learning relevance vector machine algorithm, which only exploits sparsity in spatial domain. A simulation study is carried out in terms of root-mean-squared error in the DOA estimates to compare the performances of different algorithms. Finally, we demonstrate the application of the proposed off-grid Bayesian algorithm by analyzing data from the shallow water HF$\mathbf {97}$ ocean acoustic experiment.