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Federated Target Recognition for Multiradar Sensor Data Security
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AI
DOI：10.1109/TGRS.2023.3271759.png)
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Abstract
En 中文
A 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 article presents a distributed learning model to solve these problems. It has three phases. Self-reweighting (SR) 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. SR loss and image generation are then unified in a federated learning (FL) framework. The classifier is adjusted using virtual representations to further improve learning efficiency. Comparative studies on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the advantages of the proposed method.
Keywords:
Radar
Radar imaging
Training
Servers
Image reconstruction
Federated learning
Target recognition
Deep learning
federated algorithm
finite samples
label imbalance
radar networking
Journal
IF:
8.6
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2.1W
Citations:
10.7W