高钱豪, 赵翼, 朱秋明, 郭兰图, 林志鹏, 林云, 王洁. 面向频谱环境地图重构的传感器布局优化[J]. 电波科学学报.
      引用本文: 高钱豪, 赵翼, 朱秋明, 郭兰图, 林志鹏, 林云, 王洁. 面向频谱环境地图重构的传感器布局优化[J]. 电波科学学报.
      Sensor Layout Optimization for Radio Environment Map Reconstruction[J]. CHINESE JOURNAL OF RADIO SCIENCE.
      Citation: Sensor Layout Optimization for Radio Environment Map Reconstruction[J]. CHINESE JOURNAL OF RADIO SCIENCE.

      面向频谱环境地图重构的传感器布局优化

      Sensor Layout Optimization for Radio Environment Map Reconstruction

      • 摘要: 复杂电磁环境下分布式传感器的数目和数据采集的时间非常有限,严重影响电磁频谱环境地图的重构精度。本文提出了一种面向频谱地图重构的传感器布局和优化方法,该方法基于压缩感知理论,利用感知矩阵相关性越小,重构精度越高的原则,通过梯度下降法进行优化,获得最佳测量矩阵的表达式。此外,设计了一种基于贪婪匹配的位置优化算法进行传感器位置选择,以解决最佳测量矩阵无法直接映射到传感器位置的问题。最后,针对校园场景的频谱环境地图数据进行仿真验证和性能评估。结果表明,在5%-60%稀疏采样条件下,本文所提传感器布局方案的重构性能优于其他布局方案,平均绝对误差提升了约20%-50%,可以用于辅助真实环境下频谱数据的高效采集和频谱地图的精确构建。

         

        Abstract: The number and acquisition time of distributed sensors in complex electromagnetic environments are very limited, which seriously affects the accuracy of reconstructing electromagnetic radio environment maps (REM). This article proposes a sensor layout and optimization method for REM reconstruction. This method is based on compressed sensing theory and utilizes the principle that the smaller the correlation of the sensing matrix, the higher the reconstruction accuracy. The gradient descent method is used to optimize and obtain the optimal expression of the measurement matrix. In addition, a greedy matching based position optimization algorithm is designed for sensor position selection to address the issue of the optimal measurement matrix not being directly mapped to the sensor position. Finally, simulation verification and performance evaluation are conducted on the REM data of campus scenes. The results show that under sparse sampling conditions of 5%-60%, the reconstruction performance of the sensor layout scheme proposed in this paper is superior to other layout schemes, with an average absolute error improvement of about 20%-50%. It can be used to assist in efficient collection of spectrum data and accurate construction of REM s in real environments.

         

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