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Spatio-temporal sensor management for environmental field estimation

机译：时空传感器管理，用于环境领域估算

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摘要

We develop sparsity-enforcing spatio-temporal sensor management methods for environmental field monitoring applications. Leveraging the space-time stationarity, an environmental field can be estimated with a desired spatio-temporal resolution based on recorded measurements. If the field is non-stationary, it can be monitored dynamically based on the collected measurements and predictions made through a state model, if known a priori. We develop algorithms to implement sparse sensing, i.e., sensing only the most informative locations in space and time for both spatio-temporally stationary and non-stationary field monitoring applications. The selected sensing locations form an underdetermined measurement model which can be used to estimate the field based on the prior knowledge regarding the space-time variability of the field. The task of locating the most informative sensing locations can be performed for both multiple snapshots and a single snapshot based on the availability of prior knowledge (space-time correlation and dynamics) regarding the field, available computing power and the application. Centralized sensor placement problems for the estimation of both stationary and non-stationary fields are formulated as relaxed convex optimization problems, constrained by static or dynamic performance criteria. Finally, an iterative sparsity-enhancing saddle point method is formulated to solve both of these sensor placement problems.
机译：我们为环境现场监控应用开发了稀疏增强时空传感器管理方法。利用时空平稳性，可以基于记录的测量值以所需的时空分辨率估算环境场。如果该字段是非平稳的，则可以根据收集的测量值和通过状态模型做出的预测（如果已知）来动态地对其进行监视。我们开发了算法来实现稀疏感测，即对于时空固定和非固定的现场监视应用，仅感测空间和时间中信息量最大的位置。所选择的感测位置形成欠定的测量模型，该测量模型可用于基于关于场的时空变化的先验知识来估计场。可以基于有关该领域的现有知识（时空相关性和动力学），可用的计算能力和应用，为多个快照和单个快照执行定位信息最多的感测位置的任务。用于估计固定和非固定场的集中式传感器放置问题被表述为松弛凸优化问题，受静态或动态性能标准约束。最后，提出了一种迭代的稀疏性增强鞍点方法来解决这两个传感器放置问题。

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