As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal’s moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves $2\sim 4\times $ improvement in the tracking accuracy, compared with the state-of-the-art approach.

中文翻译：

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并行反向散射：信道估计及其他

随着基于反向散射的物联网应用的激增，如何利用反向散射信号进行高效传感成为一个重要问题。基于反向散射的传感需要准确估计反向散射通道（相位和幅度），当多个信号相互碰撞时，反向散射通道会失真。结果，现有技术仅限于冲突信号的并行解码或具有干净信号的信道估计。出于对高感知能力的需求，我们在本文中介绍了 Fireworks，这是第一种并行反向散射信号信道估计的方法。Fireworks 的见解是，尽管通道因碰撞而失真，但 ON-OFF Keying 调制信号的移动仍保留了各个标签的通道属性。通过在 IQ 域中对通道与信号移动轨迹之间的关系进行建模，可以直接从碰撞中准确估计通道。针对Fireworks计算复杂度高、兼容商用MAC协议等实际问题，实现Fireworks。结果表明，Fireworks 能够并行估计多达五个标签的通道。当应用于跟踪应用程序时，Fireworks 实现 结果表明，Fireworks 能够并行估计多达五个标签的通道。当应用于跟踪应用程序时，Fireworks 实现 结果表明，Fireworks 能够并行估计多达五个标签的通道。当应用于跟踪应用程序时，Fireworks 实现 $2\sim 4\times $ 与最先进的方法相比，跟踪精度有所提高。