Joint Position Estimation for Hand Motion Using MIMO FMCW mmWave Radar

In the scenario of ultrashort range (USR) gesture interaction, the joint range-angle parameter estimation encounters numerous challenges when using multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar. In signal modeling, the traditional far-field plane-wave assumption breaks down due to the hand motion spanning the near-field and far-field regions. Moreover, the hand target exhibits a significant angular extent in USR, making a distributed source model more realistic than a point target model. For joint parameter estimation, while the wideband FMCW signal improves range resolution, it may introduce direction-of-arrival (DOA) estimation bias and ambiguities. To address these issues, this article establishes a spherical wavefront distributed source signal model applicable to hybrid-field scenarios, which accurately reflects real-world signal propagation in USR. We analyze the errors induced by traditional signal processing algorithms, and proposes a joint range-DOA estimation algorithm for spatial localization. This method initially performs meticulous peak alignment within the range domain to correct for phase residuals from wideband signals. It then iteratively processes each range-bin data using a 2-D Capon angle estimation algorithm, which is based on the spherical wavefront MIMO radar array model, for joint estimation. Ultimately, the target's Cartesian coordinates are computed utilizing an elliptic geometric model. Our simulations and real-data results both validate the effectiveness of the proposed model and method.