CIF: Small: Task-Cognizant Sparse Sensing for Inference

随着大数据应用在规模和数量上持续增长，为了限制所需的传感成本以及存储或通信数据的相关成本，仅处理对特定推理任务具有信息性的测量至关重要。为此，该项目开发了一种新的随机过程压缩感知范例，其中信号压缩和重建策略被设计为具有任务意识，取决于有用的信号统计而不是原始随机信号。这项研究带来了重大的传感能源节约，并可以惠及大量节能的传感应用，如位置感知服务、天气监测、频谱监测和射电天文。该项目的主要目标是通过提供针对感兴趣的推理任务定制的创新传感方法，显著降低传感成本以及相关的存储和通信需求。与现有的压缩采样不同，该研究利用了信号统计信息的内在结构，以便能够从稀疏采样数据中进行可靠的推断，而不是仅仅依靠信号的稀疏性来实现压缩。因此，针对随机过程引入了一种新的稀疏检测方法，即使在没有信号稀疏的情况下，该方法也揭示了相对于基本统计结构固有的自由度的压缩的基本限制。提出了高效的推理技术和确定性的压缩采样器设计，在获得期望的推理质量的同时，大大节省了传感成本。这些基本结果为有效处理数据密集型传感应用提供了机会，其中来自随机过程的推断是最重要的。

As big data applications continue to grow in size and number, it is of crucial importance to deal only with measurements that are informative for a specific inference task in order to limit the required sensing cost, as well as the related costs of storing or communicating the data. To this end, this project develops a new paradigm of compressive sensing for random processes, where the signal compression and reconstruction strategies are designed to be task-cognizant, hinging on useful signal statistics rather than the original random signals. This research leads to major sensing energy savings, and can benefit a plethora of energy-efficient sensing applications such as location-aware services, weather monitoring, spectrum monitoring, and radio astronomy.The main goal of this project is to significantly reduce the cost of sensing as well as the related storage and communication requirements by offering innovative sensing approaches tailored to the inference task of interest. Different from existing compressive sampling, the proposed research leverages the inherent structures of signal statistical information in order to enable reliable inference from sparsely sampled data, rather than solely relying on signal sparsity to enable compression. As such, a new approach to sparse sensing is introduced for random processes, which reveals the fundamental limits of compression in relation to the degrees of freedom inherent to the underlying statistical structure, even in the absence of signal sparsity. Efficient inference techniques and deterministic compressive sampler designs are put forth to affect major savings in the sensing costs while achieving the desired inference quality. These basic results open up opportunities for efficient handling of data-intensive sensing applications in which inference from random processes is of foremost importance.