Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals
Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals
复制标题
用于重建动态稀疏信号的稀疏融合卡尔曼滤波
DOI：
10.1109/icc.2015.7249389
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
I. Wassell
中科院分区：
文献类型：
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作者：
Xin Ding;Wei Chen;I. Wassell
本文重点研究了利用卡尔曼滤波器（KF）从一系列噪声压缩感知测量中重建动态稀疏信号的问题。这个问题出现在许多应用中，例如，磁共振成像（MRI）、无线传感器网络（WSN）和视频重建。传统的卡尔曼滤波算法没有考虑实际信号中存在的稀疏结构，应用于稀疏信号恢复时存在一定的误差。为了处理这个问题，我们推导出一个新的KF过程，其中考虑到稀疏模型。在此基础上，提出了一种稀疏融合卡尔曼滤波算法，并利用迭代软阈值法对稀疏模型进行了改进。合成数据和实际数据的无线传感器网络的收集证明了我们的方法的优越性。
This article focuses on the problem of reconstructing dynamic sparse signals from a series of noisy compressive sensing measurements using a Kalman Filter (KF). This problem arises in many applications, e.g., Magnetic Resonance Imaging (MRI), Wireless Sensor Networks (WSN) and video reconstruction. The conventional KF does not consider the sparsity structure presented in most practical signals and it is therefore inaccurate when being applied to sparse signal recovery. To deal with this issue, we derive a novel KF procedure which takes the sparsity model into consideration. Furthermore, an algorithm, namely Sparsity-fused KF, is proposed based upon it. The method of iterative soft thresholding is utilized to refine our sparsity model. The superiority of our method is demonstrated by synthetic data and the practical data gathered by a WSN.