Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echos of its transmitted cellular OFDM signals to sense multiple targets. The Cramer-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.

翻译：集成感知与通信（ISAC）是第六代移动通信系统的主要应用场景。由于蜂窝系统中天线数和子载波数的快速增长，ISAC系统中联合方位-距离-速度估计（JARVE）的计算复杂度极高。本文研究基于正交频分复用（OFDM）波形的单站ISAC系统的JARVE问题，其中基站接收自身发射的蜂窝OFDM信号回波以感知多个目标。首先推导了JARVE的克拉美罗界。进一步设计了一种低复杂度的超分辨率JARVE算法，该算法利用所提出的迭代子空间更新方案和Levenberg-Marquardt优化方法，替代多信号分类（MUSIC）算法中空间谱的穷举搜索。最后，基于5G新空口的实际参数进行仿真验证，结果表明：与现有的三维MUSIC算法和基于旋转不变技术的信号参数估计（ESPRIT）算法相比，所提算法可分别将计算复杂度降低三个数量级和两个数量级，同时提升了估计性能。