稀疏感知传感器协作线性相干估计
Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation
摘要：
在分布式估计的背景下，我们考虑的问题，传感器协作，这是指与相邻传感器共享测量传输到融合中心之前的行为。同时结合传感器协作的成本，我们的目标是找到最佳的稀疏协作方案受到一定的信息或能量约束。研究了两类传感器协作问题：信息约束下的最小能量问题和能量约束下的最大信息问题。为了解决由此产生的传感器协作问题，我们提出了易于处理的优化配方，并提出了有效的方法，使近最佳的解决方案，在数值实验。我们还探讨的情况下，有一个成本与参与每个传感器的估计方案。在这种情况下，必须明智地选择参与的传感器。我们引入了一个统一的框架，共同设计最佳的传感器选择和协作方案。对于一个给定的估计性能，我们的经验表明，传感器选择和传感器协作之间存在一个权衡。
英文摘要：
In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods which render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we show empirically that there exists a trade-off between sensor selection and sensor collaboration.
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