# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Filtering Approach  
Indoor localization algorithms have been amended with the filtering algorithms to increase the performance of positioning and tracking methods. The filtering algorithms were able to construct the real-time database and compensate for the cumulative positioning error and then it can also remove noise measurements. Bayesian filter [242; 243; 244], Kalman \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Scheme** & **Moving to** & **Performance/Limitations** \\  & & **Evaluation/Appraisal** & **/Remarks** \\ \hline \multirow{8}{*}{[212]} & & Utilized Principle Component & \multirow{2}{*}{RF 70\% in static and KNN} \\  & & Analysis (PCA) for extracting & 33\% in dynamic \\  & & data feature from the radio & \multirow{2}{*}{corresponding to reducing the time, outperforming} \\  & & map, time and manpower of & \\  & & computation costs covered & \\  & & with KNN, DT, RF, SVM. & \\ \hline \multirow{8}{*}{[213]} & & Comparison of the & \multirow{2}{*}{Building, floor and region} \\  & & performance of each & \\ \cline{1-1}  & & classification algorithm with & \\ \cline{1-1}  & & the confusion matrix (NN, & \\ \cline{1-1}  & & SMO, DT J48, KNN, & \\ \cline{1-1}  & & AdaBoost, Bagging, Naive & \\ \cline{1-1}  & & Bayes, Bayesian Network), & \\ \cline{1-1}  & & using UJIndoorLoc database. & \\ \hline \multirow{8}{*}{[214]} & & Evaluation of the six location & \multirow{2}{*}{Positioning accuracy} \\  & & classification algorithms, & \\ \cline{1-1}  & & ANN, KNN, DT, NB, ELM & \\ \cline{1-1}  & & and SVM and then the & \\ \cline{1-1}  & & normalization was performed & \\ \cline{1-1}  & & on the data with the standard & \\ \cline{1-1}  & & score (z-scores) and feature & \\ \cline{1-1}  & & scaling, using the UCI library. & \\ \hline \multirow{8}{*}{[215]} & & Effective for non-linear & \multirow{2}{*}{Positioning errors are 1.5 and} \\  & & localization feature extraction & \\ \cline{1-1}  & & leading to mitigate the & \\ \cline{1-1}  & & positioning error from original & 2 m based on the accuracy of \\ \cline{1-1}  & & RSS information with KDDA & \\ \cline{1-1}  & & transform and RVR supports a & \\ \cline{1-1}  & & better regression effect in the & \\ \hline \multirow{8}{*}{[217]} & & Evaluates higher accuracy & \multirow{2}{*}{Localization accuracy of 2.19} \\  & & based on the uncertainty data & \\ \cline{1-1}  & & with DZ-ELM to increase the & \\ \cline{1-1}  & & original ELM performance, & \\ \cline{1-1}  & & Introduces a dead zone & \\ \cline{1-1}  & & approach for the solutions. & \\ \hline \multirow{8}{*}{Extreme learning machine} & & \multirow{8}{*}{[218]} & \multirow{8}{*}{Uses OS-ELM, ability to reduce computational workload costs in offline calibration survey.} & \multirow{8}{*}{Considers training time, testing time and the average localization performance, localization accuracy under} \\ \cline{1-1}  & & & human interference and \\ \cline{1-1}  & & & circumstances of \\ \cline{1-1}  & & & opening/closing doors. \\ \hline \multirow{8}{*}{[216]} & & Uses KPCA-ELM leading to a & \multirow{2}{*}{Provides a non-linear attenuation effect influence on RSS correlation.} \\ \cline{1-1}  & & fast learning ability and & \\ \cline{1-1}  & & effective accuracy positioning, & \\ \cline{1-1}  & & reduces large data dimension. & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance based on machine learning algorithms and extreme learning machine (ELM). filter (KF) and extended Kalman filter (EKF) [245, 246], and particle filter (PF) [247, 248, 249, 250] have been implemented by integrating WLAN-based indoor localization determination techniques. The filtering process may aid in obtaining a continuous trajectory and decrease the estimation error. In the state-space model, the tracking problem can be solved by using Bayesian tracking which is provided by the Bayesian filter. Indeed, a KF, a particle filter and a grid-based Bayesian filter are diverse methods in the overview of the Bayesian filtering process [251].