מאיר קלך

The detection of small maritime targets by radar demands particular attention due to the challenge of distinguishing them within noisy environments. With advancements in machine learning (ML), several studies have explored integrating ML models to replace or enhance traditional radar target detection methods. In this research, we explore the integration of ML with airborne radar for maritime target detection, a domain that, to the best of our knowledge, has not been addressed in existing studies. We introduce an approach that uniquely applies ML as a secondary stage to enhance detection in scenarios where traditional methods prove insufficient. In this paper, we propose two deep-learning models aimed at improving real-time detection of small marine targets using airborne radar systems. The first model is a Convolutional Neural Network (CNN) designed to handle low-height, highly asymmetrical input dimensions (AsymCNN) adapted to classify small targets and spikes from Range-Doppler ( R / D[jls-end-space/]) maps. The second, a novel Rings-based Convolutional Neural Network (RbCNN) developed for this research, extends the AsymCNN architecture, optimizing it for cylindrical input data such as R / D maps. To evaluate our method, we used raw radar signal data collected from airborne radar systems operating over the sea. Each signal is represented using two components, in-phase and quadrature ( I / Q[jls-end-space/]) samples, which together capture both the amplitude and phase characteristics of the signal. We demonstrate that our architectures, AsymCNN and RbCNN, outperform traditional detection methods, Resnet and Densenet, in distinguishing small marine targets from spikes.