###### Abstract
The IRS is a programmable meta-surface consisting of a number of low-cost passive reflective elements, which is considered one of the key technologies for 5G and for the realization of wireless smart communication environments towards 6G. Facing the NLOS environment, the signals transmitted by the user to be located can bypass the obstacles by means of the intelligent reflection of the IRS [34]. The phase and amplitude of the signal transmitted by the user to be located can be changed by presetting the reflection coefficient of the smart reflection element through a digital controller. This ensures that the signal propagates along the direction of the base station and is eventually received by the base station [35]. The IRS improves the wireless communication environment by realizing intelligent control of the wireless communication environment in the above way. Many studies have analyzed enhancements in IRS for the communication environment. In [36], the authors established a wireless channel model based on distributed IRS and proposed a mathematical framework based on the method of moments, analyzed the communication performance of the model such as spectral efficiency and energy efficiency, and demonstrated the advantages and disadvantages of the scheme. In [37], the authors proposed a dual-distributed IRS-assisted communication system that utilizes passive beamforming to optimize the deployment locations of IRSs, thereby maximizing the worst-case Signal-to-Noise Ratio (SNR) at all locations in the target area. In [38], the authors discussed the practical role of IRS for 6G-oriented massive MIMO technology, which is expected to realize all aspects of massive MIMO technology in the upcoming 6G era by exploiting the low-cost maneuverable performance of IRSs. In addition to this, with the continuous development of AI technology, research on AI-assisted IRS communication [39] is ongoing. In [40], a model for IRS communication using deep neural networks was introduced, and the authors verified that a low bit error rate is still obtained with a low signal-to-noise ratio. In [41], the authors used federated learning augmented with deep deterministic policy gradients to optimize IRS deployment and power allocation strategies, and simulationsshowed that the algorithm has better convergence rates as well as optimization performance. Therefore, it can be seen that IRSs can enhance the performance of the communication system and improve the communication environment, which plays an important role in the future development of intelligent communication and has great potential to be applied in research on positioning technology [42]. Recently, a number of localization techniques have emerged with regard to the use of IRSs. The authors of [43] investigated the challenges of setting up a fully passive IRS in a wireless network for positioning and showed that with a sufficient number of IRSs, conventional positioning protocols can still function with satisfactory accuracy. In [44], the authors employed a near-field positioning algorithm using large linear IRSs in an NLOS environment to improve signal coverage, and the results show that the method provides good positioning accuracy, even in the presence of high blocking rates. In [45], virtual line-of-sight paths were established using IRSs to maximize the signal reception strength and achieve complete localization in the near field. In [46], the authors used an IRS for RSS-based multiuser positioning and improved the positioning accuracy by amplifying the RSS distribution difference. Although the above literature uses IRSs to improve the wireless signal communication environment and increase the positioning accuracy, it only considers the positioning problem in single-antenna mode and fails to take advantage of MIMO technology.