# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 2 Related Work  
There have been three types of models suggested regarding indoor user location positioning. First, in [15], a method of identifying the user's location using a particle filter in a complex indoor environment was proposed. The particle filter is a method of moving each particle in the same direction and speed according to the user's movement. After moving the particle, if the particle is in an invalid position, the resampling process relocates the particle. As mentioned above, the movement and resampling of the particles are repeated to position the user. However, for resampling to be performed, many obstacles and walls must exist indoors. The second uses fingerprinting. The fingerprinting scheme has been adopted by many existing indoor positioning systems [16; 17]. The fingerprinting scheme collects RSS samples from SPs of the indoor environment and constructs a database. After that, the measured value in the online step is matched with the database to determine the user's location. In [18], an F-score-weighted indoor positioning algorithm that combines RSSI and magnetic field (MF) fingerprints in a Wi-Fi communication environment was proposed. The proposed scheme creates a learning database for indoor positioning based on the RSSIvalue and MF fingerprint value from each AP at the location of each SP (SP) in the offline step. Next, in the online step, the F-score-weighted algorithm is used to estimate the real user's location. However, the experimental results of the authors could achieve 91% of the average positioning error less than 3 m. Despite this relatively high positioning accuracy, it requires a lot of time to calculate the user's location in the online step. The third method locates the user's location based on the PSO. In [19], the maximum likelihood estimation (MLE) method and PSO are used together. In the proposed method, the approximate location of the user is determined using MLE. Thereafter, the initial search region of the PSO is limited by setting a certain radius around the estimated position. The PSO distributes particles within a limited region to derive the user's final location. However, there may be a problem that the user does not exist within a limited radius due to the RSSI error according to the distance. In [20], the authors proposed a hybrid PSO-artificial neural network (ANN). A feed-forward neural network was chosen for this algorithm. The algorithm used Levenberg-Marquardt to estimate the distance between the AP and the user. Although the algorithm's positioning accuracy has improved, it requires a large data set to train a feedforward neural network. If there are not enough data sets for training, it cannot converge to the best local minimum or global minimum. In [21], the authors propose an improved algorithm for hybrid annealing particle swarm optimization (HAPSO). The proposed method improved the convergence speed and accuracy of PSO based on the annealing mechanism. However, the benefits of the proposed algorithm diminish as the number of access points (APs) increases. In [22], the authors performed a comparison of the improved PSO of four methods. Although the hierarchical PSO with time acceleration coefficients in the literature achieved the highest positioning accuracy, the total number of iterations used in the simulation is 100, so the PSO processing time is very long. Therefore, in this work we try to use a fingerprinting scheme [23], weighted fuzzy matching (WFM) algorithm [24; 25], and PSO algorithm to improve the positioning accuracy. Compared with the existing studies, the main improvements of this paper are as follows:  
* In [15], each particle acts as a filter that moves in the same way as the user's movement. However, when there are no obstacles in the indoor environment, the algorithm processing time is slowed down. The proposed method in this paper can shorten the convergence time by using intelligent particles. * In [19], a merger of MLE and PSO was proposed.