Unsupervised Radar Target Detection under Complex Clutter Background Based on Mixture Variational Autoencoder

Abstract

The clutter background in modern radar target detection is complex and changeable. The performance of classical detectors based on parametric statistical modeling methods is often degraded due to model mismatch. Existing data-driven deep learning methods require cumbersome and expensive annotations. Furthermore, the performance of the detection network is severely degraded when the detection scene changes, since the trained network with the data from one scene is not suitable for another scene with different data distribution. To this end, it is crucial to develop an unsupervised detection method that can finely model complex and changing clutter scenes. This problem is challenging yet rewarding because it completely eliminates the cost of obtaining cumbersome annotations. In this paper, we introduce GM-CVAE, a novel unsupervised Gaussian Mixture Variational Autoencoder with a one-dimensional Convolutional neural network approach to finely model complex and changing clutter. Furthermore, we develop an unsupervised narrow-band radar target detection strategy based on reconstructed likelihood. Comprehensive experiments are carried out to show that the proposed method realizes the refined modeling of clutter and guarantees superior detection performance in the simulated complex clutter environment. Compared with baselines, the proposed method shows better performance.