# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Machine Learning Localization Approach
Although the localization performance of DZ-ELM is better than ELM, the computing time of DZ-ELM is more required. In IPS, the main problems are normally time consumption and manpower usage in offline site survey. In this case, online sequential extreme learning machine (OS-ELM) derives from the obvious benefit of ELM, and it can learn with the fast speed and is more feasible for labour-intensive and computational costs [218; 219]. OS-ELM could enable solving the timely manner problem by achieving the performance on the environmental dynamic modes [219]. The experiment was concerned with human behaviour and the status of (opening/closing). In terms of localization accuracy, OS-ELM has a good impact compared to the conventional batch ELM in describing two states. To predict the position under nonlinear RSS measurement, ELM was combined with kernel principle component analysis (KPCA) [216]. Generally, RSS loss and attenuation effect due to multipath propagation are disturbances to estimate the desired accuracy. However, KPCA is utilized to reconstruct RSS new values by extracting RSS features and reducing dimensionality corresponding to a nonlinear signal relationship. These new values were trained through the ELM technique to search the higher localization accuracy. * Classification algorithm: most of the classification algorithms are based on supervised learning. There are two phases in supervised learning--the training phase and testing phase. In the training phase, received signal strengths need to know their labels to set up the dataset. Then, in the testing phase, the assigned label data need to predict the discrete output values. The classification method under supervised learning, such as NN, KNN, WKNN, SVM, sequential minimal optimization (SMO), Naive Bayes Classifier (NBC), Bayesian network, random forest (RF) classifier, decision tree (DT), boost and bagged were used as a classifier to outperform the indoor positioning methods [213]. Among them, KNN initially emerged as a nearest location estimation in RADAR, which is effective with simplicity. However, it cannot work well for a computational metric due to multiple environmental changes and often has low positioning accuracy. Therefore, the authors in [221] introduced a way to improve the performance of KNN in the field of the GSM network. Popular for indoor positioning, KNN is used for the weighted centroid of relative position for fingerprinting estimation. In addition, the weighted KNN of FP localization and weighted values of RPs certainly depend on their Euclidean distance [222]. The authors in [223] found that fingerprinting localization by using beacon technology (a small radio transmitter) can be combined with a weighted centroid localization method (WCL) and WKNN, in order to reduce the number of RPs over the localization space. NN, KNN and WKNN have been used for the estimation of distance measurements related to the Euclidean distance of a nearest neighbour which has features based on the class of their nearest neighbour in the dataset. WKNN is an extension of KNN, and in that case, the weighted K values are the largest. If all weighted values of WKNN are equal to one, it reduces to the KNN method. In [224], rank-based fingerprinting (RBF) are compared with NN and WKNN in order to investigate the problem of the RSS variant by addressing the drawback without the calibration process. SVM and NBC can also give the desired accuracies for the Wi-Fi fingerprinting system [225]. DT is a tree-like model, in combination with root (nodes), branches (non-terminal nodes) and leaf (terminal nodes). In [226], the authors show the comparison of DT, NN and a neural network based on the WLAN of an indoor environment, in which the location of the user is determined from the DT. Moreover, DT, Adaboost, Bagged, and RF are also used not only as classifiers but also as regression algorithms. * Clustering algorithm: most clustering problems are solved with unsupervised learning, which can identify hidden patterns from the data analysis and can predict future values.