Future sixth-generation (6G) networks are envisioned to provide both sensing and communications functionalities by using densely deployed base stations (BSs) with massive antennas operating in millimeter wave (mmWave) and terahertz (THz). Due to the large number of antennas and the high frequency band, the sensing and communications will operate within the near-field region, thus making the conventional designs based on the far-field channel models inapplicable. This paper studies a near-field multiple-input-multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space. In particular, we adopt a general wavefront propagation model by considering the exact spherical wavefront with both channel phase and amplitude variations over different antennas. Besides, we consider the general transmit signal waveforms and also consider the unknown cluttered environments. Under this setup, the unknown parameters to estimate include the 3D coordinates and the complex reflection coefficients of the multiple targets, as well as the noise and interference covariance matrix. Accordingly, we derive the Cram\'er-Rao bound (CRB) for estimating the target coordinates and reflection coefficients. Next, to facilitate practical localization, we propose an efficient estimator based on the 3D approximate cyclic optimization (3D-ACO), which is obtained following the maximum likelihood (ML) criterion. Finally, numerical results show that considering the exact antenna-varying channel amplitudes achieves more accurate CRB as compared to prior works based on constant channel amplitudes across antennas, especially when the targets are close to the transceivers. It is also shown that the proposed estimator achieves localization performance close to the derived CRB, thus validating its superior performance.

翻译：未来第六代（6G）网络拟通过密集部署配备大规模天线且工作于毫米波（mmWave）与太赫兹（THz）频段的基站（BS），同时实现感知与通信功能。由于大规模天线与高频段特性，感知与通信将工作在近场区域，这使得传统基于远场信道模型的设计不再适用。本文研究近场多输入多输出（MIMO）雷达感知系统，其中配备大规模天线的收发信机旨在三维（3D）空间中对多个近场目标进行定位。具体而言，我们采用广义波前传播模型，精确考虑球面波前在不同天线上的信道相位与幅度变化。此外，我们考虑一般化发射信号波形，并处理未知杂波环境。在此框架下，待估未知参数包括多个目标的三维坐标、复反射系数，以及噪声与干扰协方差矩阵。据此，我们推导了目标坐标与反射系数估计的克拉美-罗界（CRB）。为促进实际定位，我们基于三维近似循环优化（3D-ACO）提出高效估计算法，该算法遵循最大似然（ML）准则。数值结果表明，相较于先前基于天线间恒定信道幅度的研究，考虑精确的天线变化信道幅度可获得更准确的CRB，尤其当目标靠近收发信机时。同时，所提估计算法实现了接近推导CRB的定位性能，验证了其优越性。