PhaST: Model-free Phaseless Subspace Tracking
PhaST: Model-free Phaseless Subspace Tracking
复制标题
PhaST：无模型无相子空间跟踪
DOI：
10.1109/icassp.2019.8683458
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Namrata Vaswani
中科院分区：
文献类型：
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作者：
Seyedehsara Nayer;Namrata Vaswani
无相子空间跟踪是当真实信号位于可随时间变化的低维子空间中时，从离散信号的线性投影的仅幅度测量中恢复离散信号的时间序列的问题。许多工作中使用的典型假设是子空间随着时间的推移逐渐变化。我们将其定义为（i）新旧子空间之间的最大主角不太大（小于90度）或变化的数方向很少或两者兼而有之； (ii)子空间变化时间之间的延迟足够大。本文提出了一种新颖的算法，我们称之为 PhaST，用于无模型、小批量和快速无相子空间跟踪。我们通过实验表明，与现有的低秩相位检索算法（可以解释为无相位子空间跟踪的批处理版本，不假设任何子空间变化）相比，PhaST 明显更快，并且内存效率更高。当可用测量较少时，当其结构假设有效时，它的恢复性能也明显优于 LRPR 和单信号相位检索方法。
Phaseless subspace tracking is the problem of recovering a time sequence of discrete signals from magnitude-only measurements of their linear projections, when the true signal lies in a low-dimensional subspace that can change with time. A typical assumption used in a lot of work is that the subspace changes gradually over time. We define this as (i) the maximum principal angle between the old and new subspaces is not too large (less than 90 degrees) or the number directions that changes is few or both; and (ii) the delay between subspace change times is large enough. This paper presents a novel algorithm, that we call PhaST, for model-free, mini- batch and fast Phaseless Subspace Tracking. We show via experiments that PhaST is significantly faster, and significantly more memory-efficient, than an existing algorithm for low- rank phase retrieval (which can be interpreted as a batch version of phaseless subspace tracking that does not assume anything about subspace changes). When fewer measurements are available, it also has significantly better recovery performance than both LRPR and single signal phase retrieval methods when its structural assumptions are valid.