Federated Target Recognition for Multiradar Sensor Data Security

Abstract

Single sensor radar can no longer satisfy the increasingly complex electromagnetic environment. More attention is paid to radar sensor networks, which can obtain more information from different nodes and points of view. Moreover, better detection and tracking performance can be achieved through resource sharing and complementary advantages (joint learning). How to improve the utilization efficiency of multiple radar sensors with limited resources is an open problem, which can be transformed into joint learning in scenarios such as limited training data or imbalanced samples. This paper presents a distributed learning model to solve these problems. It has three phases. Self-reweighting loss is developed to dynamically rebalance the gradients of positive and negative samples for each category, after which the imbalance of samples can be alleviated. An image generation technique via target reimaging addresses the problem of limited samples. Self-reweighting loss and image generation are then unified in a federated learning framework. The classifier is adjusted using virtual representations to further improve learning efficiency. Comparative studies on the MSTAR dataset demonstrate the advantages of the proposed method.