## 1 Introduction  
Visible light communication (VLC) relates to wireless optical communication, which is a technology that uses light emitted by light-emitting diodes (LEDs) to transmit data [1; 2; 3]. Compared to indoor positioning technologies such as RFID [4], WLAN [5], Bluetooth [6], ZigBee [7], and ultra-wideband [8], VLC has many advantages, such as high security, fast transmission rate, no electro-magnetic radiation, etc. In addition, VLC has the ability to simultaneously illuminate and communicate. With the continuous development of VLC, visible light positioning (VLP), as an emerging technology, has attracted extensive attention and in-depth research from scholars [9; 10]. VLP has wide applicability and low cost in various application scenarios due to its strong resistance to electromagnetic signal interference and freedom from electromagnetic radiation, and it is easy to realize large-scale deployment.  
In recent years, researchers have conducted extensive studies on enhancing the performance of VLP systems. Depending on the type of sensor used at the receiver side, these methods are mainly categorized into two groups: imaging-based and non-imaging-based. Among the imaging methods, traditional approaches cover proximity [11], trilateral localization [12; 13; 14], and fingerprint localization. [PERSON] et al. [15] proposed and validated an angle-of-arrival (AOA)-based VLP system, the average positioning error was reduced by 55.6% and 57.2%, respectively. [PERSON] [16] proposed a hybrid APTI algorithm that significantly reduces the target localization error at the center of the room and overcomes the limitation when the number of LEDs is small. [PERSON] et al. [17] proposed an RSS VLP system that utilizes data preprocessing and convolutional neural networks (CNNs) to reduce the under-illuminated areas of the system and achieve improved positioning accuracy.  
In order to further improve the positioning accuracy and reduce the time cost, this paper proposes an indoor visible light positioning method based on marine predator algorithm-optimized least squares support vector machine (MPA-LSSVM). Using four LEDs as transmitters and a light meter as receiver, light intensity signals are collected from preset points in the localization area to create a localization structure. The specific localization process is divided into two stages, in the offline stage, the value of the received light intensity and the coordinate point of the location of the illuminance meter are used as the features to construct the fingerprint database, and then the marine predator algorithm (MPA) is introduced to iteratively search for the optimization of the hyperparameters of the least squares support vector machines (LSSVM) to establish the localization model with the optimal hyper-parameters; in the online stage, the light intensity signal collected at the preset test point is inputted into the localization model as the fingerprint data to calculate the coordinates of the test point to finally realize the localization. The collected light intensity signal is input into the localization model as fingerprint data to calculate the coordinates of the test point and finally realize the localization. The experimental results show that the use of this localization method can improve the positioning accuracy and enhance the stability of the system, with wide applicability.