结合分裂Bregman的压缩感知电磁地图重构算法

受限于非协作和采集数据残缺等因素，分布式感知网络难以实现对目标区域电磁态势的全覆盖感知，因此，有必要研究一种依据残缺感知数据重构出目标区域完整电磁态势的技术，进而掌控电磁态势。论文由此提出了一种结合分裂Bregman的压缩感知电磁地图重构算法。该算法基于压缩感知提出了一种滤波式分区正交匹配追踪算法，并用其重构出目标区域内的参考信号接收功率数据，然后再利用分裂Bregman对该数据进行精度提升，最终得到更高精度的数据并绘制出完整的电磁地图。仿真实验表明，重构出的电磁地图和实际情况更接近，且在可用感知节点数量稀少情况下能保证重构数据和实际数据之间的均方根误差低于2.5，算法具有重要的理论意义和应用价值。

An Electromagnetic Map Reconstruction Algorithm Based on Compressed Sensing and the Split Bregman

Being limited by factors such as nonC-cooperation and incomplete collected data, distributed sensing networks are difficult to achieve full coverage of the electromagnetic situation in the target area. Therefore, it is necessary to study a technology to reconstruct the complete electromagnetic situation in the target area according to the incomplete sensing data, and then master the electromagnetic situation. This paper proposes an electromagnetic map reconstruction algorithm based on compressed sensing and the split Bregman as a consequence. According to the compressed sensing, the algorithm proposes a filtered subdistrict orthogonal matching pursuit algorithm, and uses it to reconstruct reference signal receiving power data of the target area, and then uses the split Bregman to improve the accuracy of the data, eventually gets more accurate data. After that, the algorithm uses the data to draw a complete electromagnetic map. The simulation experiments show that the reconstructed electromagnetic map is more close to the actual situation. Moreover, with the numbers of the sensing nodes being rare, the root mean square error between the reconstructed data and the actual one is less than 2.5. The algorithm has important theoretical significance and application value.