This paper presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss-Markov process and encoders that transmit the measurements to a data fusion center through the resource restricted communication network. The proposed approach heuristically minimizes a weighted sum of communication costs subject to a constraint on the state estimation error at the fusion center. The communication costs are quantified as the expected bitrates from the sensors to the fusion center. We show that the problem as formulated is a difference-of-convex program and apply the convex-concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and 2D target tracking by a drone swarm model for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity promoting, and an effective sensor selection heuristic.

中文翻译：

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资源受限通信网络上动态传感器融合的优化数据速率分配

本文提出了一种解决动态传感器融合问题的新方法。我们考虑了大量测量常见高斯马尔可夫过程的远程传感器和通过资源受限通信网络将测量结果传输到数据融合中心的编码器。所提出的方法在融合中心的状态估计误差的约束下，启发式地最小化通信成本的加权总和。通信成本被量化为从传感器到融合中心的预期比特率。我们表明，所制定的问题是凸差程序，并应用凸凹过程（CCP）来获得启发式解决方案。我们考虑通过无人机群模型进行一维传热模型和二维目标跟踪以进行数值研究。通过这些模拟，