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.

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

“…Since the normalized Doppler frequency of Clutter II is about 0.22, all methods suffer detection performance degradation for targets whose normalized Doppler frequency is close to the normalized Doppler frequency of Clutter II. Additionally, MTI-MTD also experiences a loss in detection performance for targets with Doppler close to zero, attributed to the presence of the extremely deep notch near zero frequency [27]. To further explore the detection performance of the proposed method for targets with velocities close to the clutter, we show the PD curves of all the methods for targets with normalized Doppler frequencies of 0.22 and 0.25 in Figure 8b and Figure 8c, respectively.…”

“…Specifically, supervised approaches cast RTD as a classification problem and train a neural network using the labeled data to distinguish the target class from the clutter class [24][25][26]. Differently, unsupervised methods draw inspiration from anomaly detection and build a network to define a region in the feature space to represent the behavior of clutter (target-absent) samples [27,28]. As a result, target-present samples can be detected as outliers.…”

PCBA-Net: Pyramidal Convolutional Block Attention Network for Synthetic Aperture Radar Image Change Detection

“…Remote sensing image change detection (CD) focuses on identifying changes in phenomena or objects that have occurred in the same geographical area at different times [1]. Because synthetic aperture radar (SAR) images are independent of weather and atmospheric conditions [2][3][4], SAR image CD has attracted increasing attention. Recently, SAR image CD has been widely used in a diversity of studies, such as disaster assessment [5], urban research [6], environmental monitoring [7], and forest resource monitoring [8].…”