RETRACTED ARTICLE: Radar optical communication for analysing aerial targets with frequency bandwidth and clutter suppression by boundary element mmwave signal model

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

“…Deep learning, a branch of machine learning encompassing convolutional neural networks (CNNs) [10] and recurrent neural networks (RNNs) [11], has demonstrated remarkable capabilities in automatic feature learning and extraction, enabling tasks such as intelligent recognition of speech information and image segmentation [12]. Deep learning has also been successfully applied in radar, particularly for the intelligent detection and processing of high-resolution synthetic aperture radar (SAR) images [13]. However, these techniques often struggle when applied to environments that differ from those they were trained in, and they can also be computationally demanding.…”

“…While effective in many scenarios, traditional time-domain processing techniques, such as constant false-alarm rate (CFAR) detection [14] and coherent or non-coherent accumulation, face difficulties in dynamic and changing environments with non-Gaussian, non-stationary, and non-linear features. Frequency domain techniques, such as moving target indicator (MTI) and moving target detection (MTD), utilize Doppler information extracted through the Fourier transform [13]. Moving target detection (MTD) is a crucial task in various applications, ranging from conventional surveillance to autonomous driving.…”