With the application of high-frequency communication and extremely large MIMO (XL-MIMO), the near-field effect has become increasingly apparent. The near-field channel estimation and position estimation problems both rely on the Angle of Arrival (AoA) and the Curvature of Arrival (CoA) estimation. However, in the near-field channel model, the coupling of AoA and CoA information poses a challenge to the estimation of the near-field channel. This paper proposes a Joint Autocorrelation and Cross-correlation (JAC) scheme to decouple AoA and CoA estimation. Based on the JAC scheme, we propose two specific near-field estimation algorithms, namely Inverse Sinc Function (JAC-ISF) and Gradient Descent (JAC-GD) algorithms. Finally, we analyzed the time complexity of the JAC scheme and the cramer-rao lower bound (CRLB) for near-field position estimation. The simulation experiment results show that the algorithm designed based on JAC scheme can solve the problem of coupled CoA and AoA information in near-field estimation, thereby improving the algorithm performance. The JAC-GD algorithm shows significant performance in channel estimation and position estimation at different SNRs, snapshot points, and communication distances compared to other algorithms. This indicates that the JAC-GD algorithm can achieve more accurate channel and position estimation results while saving time overhead.

翻译：随着高频通信与极大规模MIMO（XL-MIMO）的应用，近场效应日益显著。近场信道估计与位置估计问题均依赖于到达角（AoA）与到达曲率（CoA）的估计。然而，在近场信道模型中，AoA与CoA信息的耦合对近场信道估计提出了挑战。本文提出一种联合自相关与互相关（JAC）方案以解耦AoA与CoA估计。基于JAC方案，我们提出了两种具体的近场估计算法，即逆正弦函数（JAC-ISF）算法与梯度下降（JAC-GD）算法。最后，我们分析了JAC方案的时间复杂度以及近场位置估计的克拉美-罗下界（CRLB）。仿真实验结果表明，基于JAC方案设计的算法能够解决近场估计中CoA与AoA信息耦合的问题，从而提升算法性能。在不同信噪比、快拍点数与通信距离下，JAC-GD算法在信道估计与位置估计方面均表现出优于其他算法的性能。这表明JAC-GD算法能够在节省时间开销的同时，实现更精确的信道与位置估计结果。