# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Radio Map Construction Aiding the Offline Workload
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Usage Information** & **Measurement** & **Pros and Cons** \\ \hline Proximity (range-free information) & Cell origin results & Limited coverage, connectivity-based & Inexacture and unsatisfactory in positioning. Coarse-grained results. \\ \hline Trilateration (range-based information) & Geometric properties & Timing information, distance-based & Ineffective for nonlinear model. \\ \hline Multilateration (range-based information) & Geometric properties & Timing information, distance-based & Ineffective for nonlinear model. \\ \hline Triangulation (range-based information) & Geometric properties & Incident angle, direction-based & Ineffective for nonlinear model. \\ \hline Triangulation (range-based information) & Geometric properties & Incident angle, direction-based & Ineffective for nonlinear model. \\ \hline  & & & & \\ \hline  & & & & \\ \hline  & & & & \\ \hline Fingerprinting (range-free information) & Statistical and empirical analysis & Signal strength intensity, signal-based & Effective linear and nonlinear models. \\ \hline  & & & Easy upgrade information to amend. Challenges for dynamically environmental changes. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The summary of the indoor positioning algorithm. Another option to consider for radio map construction is SLAM. SLAM derives from the mapping of mobile robot trajectory in which the robot explores the map in free space autonomously. By implementing it in IPS, SLAM incorporates smartphones sensors types. Especially the Wi-Fi signal, Bluetooth RSS, IMU data, odometry data, magnetic data, and compass data could be used to exploit the location estimation, as summarized in Table 5. Wi-Fi SLAM uses only Wi-Fi RSS with Gaussian distribution, Gaussian process latent variable model (GP-LVM) [201]. During the training phase, the training data can collect without pre-defined coordinates in Wi-Fi SLAM. GP-LVM could convert these high-dimensional RSS measurements from different APs to a two-latent dimensional space (x-y coordinates of the user's device). In an affluent signal environment, RSS constraints generally occur as a similar signal strength information cause of nearby locations in the environment. The location of these signal strengths was observed in the reading collected by a person freedom walk at the whole building. In fact, Wi-Fi-SLAM could be satisfied with the corresponding localization accuracy of unlabelled training data, a mean localization error with 3.97 \(\pm\) 0.95 m. Wi-Fi GraphSLAM uses Wi-Fi RSS from 536 APs, pedometry and gyroscope data [202]. Unlike Wi-Fi-SLAM using GP-LVM, the specific predefined maps are not essential in GraphSLAM problem. Obviously, GraphSLAM can reduce the complexity and limitations. The localization accuracy is between 1.75 and 2.18 m. However, it uses the smartphone IMU sensors to track a walking user in which pedometry data could not reliably obtain the accurate step length of a person. FootSLAM uses only the odometric data from a foot-mounted inertial sensor based on the Bayesian framework estimation [203,204]. FootSLAM is constructed as the probabilistic map with a hexagon grid in the 2D area where the data are recorded by a person walking through the building. It is implemented by using a Rao-Blackwellized particle filter (RBPF) to follow the user's trajectory and relative map. Alternatively, PlaceSLAM uses proximity information, and can improve FootSLAM accuracy [204,205]. WiSLAM uses odometric data from the foot-mounted sensor and Wi-Fi RSS, which comes from the idea of using of FootSLAM and PlaceSLAM. The WiSLAM solution improves the FootSLAM convergence based on the probabilistic Bayesian network [204]. The SignalSLAM uses Wi-Fi and Bluetooth RSS, 4G LTE RSRP, magnetic data, GPS reference points and NFC tag for constructing an automatic generated radio map [148]. Although SignalSLAM is extended, so is the adaptation of the Wi-Fi GraphSLAM Technique [202].