## 1 Introduction  
With the rising demand for indoor location-based services, indoor positioning techniques have been proposed, including geomagnetic [1], ultra-wideband [2], Bluetooth [3], Wi-Fi [4], audio [5], visible light [6], and computer vision [7]. Wi-Fi Received Signal Strength (RSS) is highly susceptible to transient power from the transmitter and indoor environments, causing substantial fluctuations and compromising stable detection performance [8, 9]. Wi-Fi, supporting the IEEE 802.11n standard, now incorporates Multiple-Input Multiple-Output (MIMO) technology. Using multiple transmitting and receiving antennas to form wireless channels for simultaneous data transmission enhances the system's transmission rate. In MIMO systems, each transmitting and receiving antenna has an independent channel response, usually represented as CSI, illustrating the signal variations from transmitter to receiver [10]. Using this detailed and varied information as signal feature indicators instead of RSS enables a more accurate depiction of characteristics at different locations [11, 12]. Furthermore, CSI technology allows for effective passive localization by monitoring wireless signal features in the target's indoor environment, even without a receiving device on the target. Thus, CSI technology holds vast potential in specialized areas like location awareness [13], human monitoring [14], and motion recognition [15]. Based on the positioning methods, Wi-Fi indoor positioning includes geometric ranging localization and fingerprint localization. Geometric ranging localization primarily calculates the distance between the target and the base station by relying on the characteristic of wireless signal attenuation with increased transmission distance. Faced with the complex multipath environment indoors, conventional channel attenuation models struggle to accurately describe the relationship between signal characteristics and distance [15]. Additionally, while ranging models based on Time of Flight (TOF), Time Difference of Arrival (TDOA), and Round Trip Time (RTT) have their advantages, they often face challenges with clock synchronization and clock drift [4; 9]. Despite the introduction of some countermeasures, they often have higher computational complexity compared to fingerprint localization [16; 17; 18]. Fingerprint-based localization methods have received widespread attention due to their simple deployment and effective positioning results [4]. FIFS [19] is the first fingerprint localization system to adopt CSI, obtaining superior positioning performance to RSS by averaging the CSI amplitudes of multiple antennas. [PERSON] and colleagues [20] applied deep learning to CSI fingerprint localization, proposing the DeepFi system. Subsequently, a fingerprint localization system specifically for the CSI phase, named PhaseFi, was introduced [21]. Then, due to hardware defects of the devices, CSI phase information experiences substantial fluctuations, affecting the accuracy of localization [19]. Therefore, we choose CSI amplitude as the location fingerprint information. In recent years, machine learning has gained popularity in indoor localization, with support vector machines standing out for their strong generalization on small samples and high-dimensional nonlinear data [22]. Numerous researchers have estimated target locations by constructing Support Vector Regression (SVR) models [23; 24; 25]. Given the performance of SVR is influenced by hyperparameters, many studies have focused on the selection of these hyperparameters. [PERSON]. [26] employed the Particle Swarm Optimization Algorithm (PSO) to finetune SVR parameters, yielding discernible enhancements in a navigation system's positioning accuracy. In a parallel approach, ref. [27] harnessed an Adaptive Genetic Algorithm (GA) for the adaptive selection of SVR hyperparameters within a ZigBee fingerprint localization framework, achieving results that outstripped those of PSO-SVR. Delving further, ref.