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.