# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 2 Related Work
### Wi-Fi-Based Indoor Localization  
Wi-Fi-based indoor-localization technology offers the advantages of wide signal coverage, relatively mature equipment, easy deployment (it needs no additional sensor equipment), low cost, strong applicability, and expansibility [23]. Therefore, indoor-localizationtechnology based on Wi-Fi has high research value and broad application prospects. Wi-Fi-based indoor-localization technology includes the following two types:  
* **Triangulation method. ** This method relies on the measurement of distance. Thereafter, the location estimation is obtained through geometric calculation. Classical triangulation localization methods [24] include time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA). TOA is a measurement method to calculate the distance between the terminal device and the Wi-Fi access point (AP) by recording the unidirectional or bidirectional arrival time of Wi-Fi signals between the their terminals. However, it requires the precise synchronization of the time stamps at the transmitter and receiver. TDOA uses the characteristic that two focal distances on a hyperbola remain fixed. Based on the arrival time difference between the terminal device and different APs, using the hyperbolic equation, the location of the localization point can be solved. In contrast to TOA, TDOA reduces the time synchronization requirement, but accurate time measurement is a limiting factor. Moreover, it is susceptible to non-line-of-sight (NLOS) problems. AOA involves obtaining positions through azimuth angle measurements. It eliminates the need for accurate time synchronization between devices and requires a small number of base stations. However, it measures the signal transmission angle, which requires localization equipment carrying an antenna array device, thereby increasing the difficulty of its popularization. * **Wi-Fi fingerprint-based method. ** This method utilizes the mapping correlation between Wi-Fi signal characteristics and physical locations. In the ideal localization environment, each physical location should have a unique and distinguishable fingerprint [23]. Generally, in this method, an indoor-location area is divided into a series of discrete grid spaces in advance to obtain the radio fingerprint map. Further, in the Offline phase, the Wi-Fi received signal strength (RSS) from different access points (APs) are collected on each reference point (RPs) of discrete grid points. Consequently, combining the physical coordinates of RPs, the fingerprint database is constructed. Thus, a received Wi-Fi fingerprint can determine the most similar fingerprint of database in the Online phase, and, subsequently, the corresponding coordinate can be estimated. By contrast, Wi-Fi fingerprint-based indoor-localization technology is not affected by NLOS and does not require the location information of APs. It has been extensively studied since it was proposed by [PERSON] [25]. Moreover, many methods have been proposed, which can be divided into deterministic and probabilistic methods. * **Deterministic methods. ** These algorithms directly use the one-to-one mapping relationship between Wi-Fi fingerprint and physical location and estimate the unknown position based on the closest fingerprint location in signal space. K-nearest neighbor (KNN) [25] is one such example. The method is to determine the K most similar Wi-Fi fingerprints from a database using the Euclidean distance, and then calculate the average of the K corresponding physical locations. [PERSON] et al. [26] improved the KNN algorithm and proposed the WKNN algorithm, which used a weighted average for location estimation. Neural network (NN) algorithms [27], such as the multi-layer neural network [28], have also been applied to Wi-Fi fingerprint-based localization but with high computational cost. NN obtains the mapping relationship between a fingerprint and physical position after considerable training, then uses it to predict the unknown position.