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Spatial Deployment of Heterogeneous Sensors in Complex Environments

机译:复杂环境中异质传感器的空间部署

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摘要

Studies on the deployment of sensors mostly involve a 2D plane or 3D volume. However, the optimal sensor deployment in field environments is actually the resource distribution on 3D surfaces. Compared with the traditional deployment environments, field environments are more complicated, owing to some interferences on the detection capability of sensors and limitations on the maneuverability of platforms. In this paper, an optimal sensor deployment algorithm in 3D complex environments is discussed. First, considering the characteristics of field environments, the maneuverability matrix of heterogeneous platforms was introduced as a constraint. Then, a non-isomorphic environment value distribution map was constructed to mark the differences among mission areas. Furthermore, the sensor detection range model was improved to better deal with the occlusion issue. Finally, based on the multi-objective particle swarm optimization (MOPSO) algorithm, a sensor deployment strategy was deployed for complex environments. Experiments demonstrated that the proposed algorithm can better deal with the sensor deployment problem in field environments, while improving the detection accuracy of the objects in mission areas.
机译:关于传感器部署的研究主要涉及2D平面或3D体积。但是,现场环境中的最佳传感器部署实际上是3D表面上的资源分布。与传统的部署环境相比,由于传感器检测能力的干扰和平台可操纵性的限制,现场环境更加复杂。在本文中,讨论了3D复杂环境中的最佳传感器部署算法。首先,考虑到现场环境的特征,引入了异构平台的机动性矩阵作为约束。然后,构建非同组环境值分布图以标记任务区域之间的差异。此外,改善了传感器检测范围模型以更好地处理遮挡问题。最后,基于多目标粒子群优化(MOPSO)算法,部署了传感器部署策略以进行复杂环境。实验表明,所提出的算法可以更好地处理现场环境中的传感器部署问题,同时提高任务区域中对象的检测准确性。

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