Integrated sensing and communication (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC in pervasive mobile networks, it is crucial to have widely deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) hardware architectures and waveforms is pivotal for realizing seamless integration of effective communication and sensing functionalities. In this paper, we introduce a novel joint angle-range-velocity estimation algorithm for MIMO-OFDM ISAC systems. This approach exclusively depends on the format of conventional MIMO-OFDM waveforms that are widely adopted in wireless communications. Specifically, the angle-range-velocity information of potential targets is jointly extracted by utilizing all the received echo signals within a coherent processing interval (CPI). The proposed joint estimation algorithm can achieve larger signal-to-noise-ratio (SNR) processing gains and higher resolution by fully exploiting the echo signals and jointly estimating the angle-range-velocity information. A theoretical analysis for maximum unambiguous range, resolution, and SNR processing gains is provided to verify the advantages of the proposed joint estimation algorithm. Finally, the results of extensive numerical experiments are presented to demonstrate that the proposed joint estimation approach can achieve significantly lower root-mean-square-error (RMSE) performance for angle/range/velocity estimation for both single- and multi-target scenarios.

中文翻译：

---

一种新型的MIMO-OFDM ISAC系统联合角度-范围-速度估计方法


集成传感与通信 (ISAC) 正在成为下一代无线系统的关键技术。为了加快 ISAC 在普及移动网络中的实际实施，广泛部署具有雷达传感功能的基站至关重要。因此，标准化多输入多输出（MIMO）正交频分复用（OFDM）硬件架构和波形的利用对于实现有效通信和传感功能的无缝集成至关重要。在本文中，我们介绍了一种用于 MIMO-OFDM ISAC 系统的新型联合角度-距离-速度估计算法。这种方法完全依赖于无线通信中广泛采用的传统 MIMO-OFDM 波形的格式。具体地，利用相干处理间隔(CPI)内所有接收到的回波信号来联合提取潜在目标的角度-距离-速度信息。所提出的联合估计算法通过充分利用回波信号并联合估计角度-距离-速度信息，可以获得更大的信噪比（SNR）处理增益和更高的分辨率。提供了最大无模糊范围、分辨率和信噪比处理增益的理论分析，以验证所提出的联合估计算法的优点。最后，大量数值实验的结果表明，所提出的联合估计方法可以在单目标和多目标场景的角度/距离/速度估计方面实现显着较低的均方根误差（RMSE）性能。