Mode Recognition of Multifunction Radars for Few-Shot Learning Based on Compound Alignments

The multifunctional radars can switch among a variety of fine-grained working modes, which often have flexible modulation types and programmable parameters. In an electromagnetic reconnaissance system, the process of identifying different working modes in pulse sequences guarantees the subsequent intention analysis and assists in devising a jamming strategy. Most of the existing working mode recognition methods attempt to establish a machine learning mechanism by training a model using a large number of annotated samples. However, this is hardly applicable in the real-world scenarios where only a few samples can be intercepted in advance. As the labeled signal samples are expensive, one direction is to augment the dataset by generating either samples or signal features in an embedding space. In this article, inspired by the fact that different modalities of the same working modes are generated from a set of fixed parameters, a few-shot learning framework based on compound alignments is proposed. Three branches, which take observations of long windows, observations of short windows, and coded semantic attributes as inputs, are aligned in both the latent variable space and the reconstruction space to learn a shared embedded space. A simple soft-max classifier is trained by sampling sufficient instances in the learned space to realize the final identification process. The experimental results and analysis show that the proposed method achieves excellent performance for fine-grained working mode recognition even for a small number of observed pulses. In addition, the proposed method is robust under different nonideal conditions, such as noise contamination, incomplete sequences, and spurious pulses.