Multi-component signal separation based on ALSAE

Abstract

Most research in the field of radar signal processing focuses on the use of time-frequency images (TFIs) to distinguish between different signal types. However, most studies have only examined the TFIs of a single signal, making it challenging to analyze and process the simultaneous reception of multiple signal components. This study proposes the use of adversarial latent separation auto encoder to separate and recognize multi-component signals, and innovatively propose a multi-network structure of feature extraction sub-network and signal separation sub-network. Thus, the problem of multi-component signal recognition is solved. Following separation, each component retains its time-frequency data while removing the influence of other components, and the separated TFIs are then subjected to parameter estimation and structural similarity (SSIM) measurements. The experimental findings demonstrate that the parameters retrieved from the separated signal have a low error with respect to the original signal, especially at low signal-to-noise ratios. The excellent SSIM and parameter estimation metrics between the separation results and the time-frequency image of the target tag imply that the separated single-component signal can be successfully reconstructed.