Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks
Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks
复制标题
DOI：
10.1002/rnc.6076
复制
发表时间：
2021-06
影响因子：
3.9
通讯作者：
H. Jung;A. Pedram;Travis C. Cuvelier;Takashi Tanaka
中科院分区：
文献类型：
--
作者：
H. Jung;A. Pedram;Travis C. Cuvelier;Takashi Tanaka
本文提出了一种新的方法来解决动态传感器融合问题。我们考虑了大量的遥感器测量一个共同的高斯-马尔可夫过程。每个传感器编码并通过资源受限的通信网络将其测量传输到数据融合中心。给定传感器产生的通信成本被量化为从传感器到融合中心的期望比特率。我们提出了一种方法，试图最大限度地减少这些通信成本的加权和约束的状态估计误差在融合中心。我们将问题表述为凸规划的差异，并应用凸凹过程（CCP）来获得启发式解决方案。我们考虑一个一维热传递模型和一个模型的二维目标跟踪的无人机群的数值研究。通过这些模拟，我们观察到我们提出的方法倾向于将零数据速率分配给不必要的传感器，这表明我们的方法是稀疏促进的，并且是有效的传感器选择启发式。
This article 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. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as 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 a model for 2D target tracking by a drone swarm 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.