Feature Reconstruction For Multi-Hand Gesture Signals Separation Based on Enhanced Music Using Millimeter-Wave Radar

Millimeter-wave radar has found widespread applications in perception domains such as gesture recognition. In comparison to single-hand gestures, radar echo signals from multi-hand gestures involve the superposition of multiple signals, presenting a more challenging recognition task. This paper introduces a multi-hand gesture signal separation method based on the enhanced Multiple Signal Classification (MUSIC) algorithm, capable of simultaneously distinguishing the motion states of the left and right hands. Specifically, this paper utilizes Minimum Variance Distortionless Response (MVDR) to extract joint angle-range information for gesture features. The enhanced MUSIC algorithm is then applied to reconstruct single-hand gesture Range-Angle Map (RAM) by constructing a new noise subspace. The effectiveness of the proposed algorithm is validated through simulations and radar signal experiments, demonstrating its capability to separate multi-hand gesture signals from the foundation of multi-hand gesture RAM to obtain single hand RAM.