基于 DSGD 的分布式电磁目标识别

Distributed electromagnetic target identification aims to realize traditional centralized electromagnetic target identification by using technologies such as distributed optimization and distributed computing. The distributed optimization method combines the distributed computing architecture to realize the distributed solution to the optimization problem, and realizes the mapping from the problem information and data to the optimal target identification model in a distributed manner. This paper uses the decentralized stochastic gradient descent, which is a classical distributed optimization method, to establish a distributed computing architecture and a distributed electromagnetic target identification method for electromagnetic target identification. Based on the actual electromagnetic signal data, the effectiveness of the proposed algorithm is verified. When the performance of the distributed electromagnetic target identification algorithm and the centralized identification algorithm remains above 90%, the single node training time decreases by more than 50%, which significantly improves the training efficiency.