# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Machine Learning Localization Approach  
Machine learning approaches have been combined with the basic FP deterministic and probabilistic method, for the execution of the classification and clustering purposes of signal measurements in offline and online phases, as presented in Figure 13. The signal measurement method and positioning algorithms are significantly integrated with machine learning [212; 213; 214; 215] to predict and estimate the location in indoor wireless localization method, as summarized in Table 6. The current literature considers reducing the location estimation error accumulation, finding the positioning accuracy and efficiency on multipath fading. However, conventional measurement methods are not satisfied with determining these aspects, because indoor localization requires RPs' density from different APs. A machine learning algorithm of supervised learning, regression and unsupervised learning can be used in the classification algorithm, clustering algorithm and matching algorithm, based on useful signals to eliminate the attenuation of the error caused by signal interference from indoor objects and humans. In addition, one of the learning algorithms of neural network type is used in IPS. Extreme learning machine (ELM) is based on the learning algorithm of a single-hidden layer feedforward neural network (SLFN) architecture. Typically, ELM can occupy the fast learning speed as the robust learning technique. ELM can determine the output weighted values from randomly hidden nodes by using the neural networks type too many repetitive times. Regression and classification learning have been conducted with ELM for some existing IPS cause of the good evidence performance in theory. Thus, ELM \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Evaluation** & **Data Type** & **Infrastructure** & **Performance** \\ \hline
[201] & Wi-FiSLAM & Wi-Fi & University building, floor level 250 m to a half of km & Mean localization error 3.97 \(\pm\) 0.95 m \\  & & constraint and unlabelled map & & \\  & information) & & & \\ \hline
[202] & Wi-Fi GraphSLAM & Wi-Fi/pedometry and & University building, cover 600 m\({}^{2}\), 1.2 km & Localization accuracy range 1.75 m to 2.8 m, \\  & & constraint and labelled & & radius & mean error 2.23 \(\pm\) 1.25 \\  & & data, addressing & & m \\  & runtime complexity & & & \\ \hline
[203] & FootSLAM & Foot mounted IMU & Building/ constraint & Pedestrian’s relative location accuracy, \\  & approach to track & sensors & area & location accuracy, \\  & user’s step and location & & & 1 to 2 m at two reference points \\  & based on & & & reference points \\  & odometry(track & & & \\  & motion) & & & \\ \hline
[205] & PlaceSLAM & Proximity information & Two office building & Tracking error 2–10 m from pedestrian walking \\  & & Particle Filtering & & \\ \hline
[204] & WiSLAM, & Wi-Fi/IMU data & building & Upgrade FootSLAM convergence, accuracy is up to 2 m \\  & & Bayesian statics, & & \\  & concerted FootSLAM & & & is up to 2 m \\  & and PlaceSLAM & & & \\ \hline
[148] & SignalSLAM & Wi-Fi and Bluetooth & Walking naturally & Medium tracking \\  & Modification of & RSS/4G LTE, & around the building & accuracy 11 to 16.5 m \\  & GraphSLAM and & RSRP/magnetic/GPS & & \\  & generate the & reference points NFC at & & \\  & multi-modal signal & specific landmarks and & & \\  & maps from available & PDR from IMU sensor & & \\  & multiple sources & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of the location accuracy based on simultaneous localization and mapping (SLAM). is extensively considered for inevitable IPS problems [216; 217; 218; 219; 220]. In [217], ELM with dead zone (DZ-ELM) focused on the uncertain data problem creating a dead zone approach to raise the positioning performance inference from the original ELM technique. The several disturbances indoors can be addressed with this approach, that of RSS attenuation and the various changing environments.