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The capturing of hands, including their poses, shapes and motions, has numerous potential applications, such as human-machine interfaces and medical use cases. However, in the radar context, most existing methods only allow for the recognition of dynamic hand gestures based on Doppler evaluations due to the respective systems&#x2019; limited lateral resolution. Radar-based high-resolution three-dimensional (3D) imaging using multiple-input multiple-output (MIMO) radars is currently the state-of-the-art in personnel security scanning. However, the associated imaging techniques suffer from computationally burdensome reconstruction algorithms that sample the entire 3D space of interest, thereby making them less suitable for real-time applications. Moreover, their application in hand motion tracking scenarios is limited by low frame rates that result from a high number of transmit frequencies. Hence, we present an efficient and powerful approach for the radar-based 3D reconstruction of hand poses. The method extends the frequency shift keying continuous wave radar principle and reconstructs the hand surface using only two carrier frequencies. Instead of reconstructing an entire 3D volume, only two single-tone radar images are computed. Depth information is derived from phase differences between corresponding pixels in the images. The approach significantly reduces computational load by three orders of magnitude compared with the state-of-the-art and enables higher frame rates. Within this paper, this novel reconstruction principle is analyzed and compared to a state-of-the-art radar imaging approach using a MIMO radar system with 94 transmitting and 94 receiving antennas. Detailed simulations of point targets and comprehensive measurements demonstrate the excellent imaging performance of our approach.

An Ultra-Efficient Approach for High-Resolution MIMO Radar Imaging of Human Hand Poses