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Millimeter-wave (mmWave) radar, with its high resolution, sensitivity to micro-vibrations, and adaptability to various environmental conditions, holds immense potential across multi-modal fusion sensing. Although there exist review papers on mmWave radar, there is a noticeable lack of comprehensive reviews focusing on its multi-modal fusion sensing capabilities. Addressing this gap, our review offers an extensive exploration of mmWave radar multi-modal fusion sensing, emphasizing its integration with other modalities. This review discusses the complex realm of millimeter-wave radar multi-modal fusion sensing, detailing its importance, hardware and software aspects, principles, and current potential and applications. It delves into data characteristics and datasets associated with mmWave radar, focusing on Doppler, point cloud, and multi-modal data formats. The review highlights how these data types enhance multi-modal fusion sensing and discusses methodologies, including signal processing and learning algorithms. Three categories of multi-modal fusion methodologies are proposed to optimally manage and interpret fused data. Various practical applications of mmWave radar multi-modal fusion sensing are illustrated, underlining the unique capabilities it provides when integrated with other sensors. The review concludes by identifying potential future research avenues, underscoring the immense potential of this field for further exploration and advancement.
Wang et al. (Wed,) studied this question.