Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the "clean" CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.

中文翻译：

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基于WiFi的多频带拼接的信道冲激响应估计和定位

使用商用WiFi数据进行室内定位，物体识别，跟踪和信道探测等应用近来引起了广泛关注。我们从商品WiFi信道状态信息（CSI）研究信道冲激响应（CIR）估计问题。在此设置中，CIR估算方法的准确性受到可用信道带宽以及底层硬件引起的各种CSI失真的限制。我们提出了一种多频带拼接方法，该方法通过组合多个频带上的CSI数据来增加信道带宽。为了补偿CSI失真，我们开发了一种每频带处理算法，该算法能够估计失真参数并将其删除以产生“干净的” CSI。该算法结合了原子范数去噪稀疏恢复方法来利用信道稀疏性。在M频带上拼接干净的CSI，我们使用正交匹配追踪（OMP）作为估计方法来恢复具有高分辨率（M倍）的稀疏CIR。与文献中先前的工作不同，我们的方法不适用于CIR的任何局限性假设（除了广为接受的稀疏性假设）或任何用于去除失真的临时处理。我们从经验上证明，所提出的方法在定位精度方面优于现有技术。我们的方法不适用于CIR的任何限制性假设（除了广为接受的稀疏性假设之外）或任何用于消除失真的临时处理。我们从经验上表明，所提出的方法在定位精度方面优于现有技术。我们的方法不适用于CIR的任何限制性假设（除了广为接受的稀疏性假设之外）或任何用于消除失真的临时处理。我们从经验上证明，所提出的方法在定位精度方面优于现有技术。