# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 2 Related Work
Similarly, in [34], it is used the probabilistic KNN (pkNN) algorithm that uses the probability of RSS in the radio-map as a weighting to calculate the Euclidean distance. This solution also filters the RSS values whose probability is less than 3%. In [35], it was proposed an iBeacon-based method by integrating a trilateration algorithm and a fingerprinting method that uses weight matrix to differentiate the contribution of beacons in various distances and adopt cosine similarity to reveal the consistent trend between two RSSI vectors to reduce effects of RSS fluctuations. In the second phase, the pedestrian trajectories are segmented and applied a Kalman filter (KF) to improve the locations derived by the pedestrian dead reckoning (PDR). The last step uses another KF to derive locations in each segmented path by fusing the BLE-based positioning and the PDR. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. Ref. [36] has evaluated BLE localization performances based on RSS fingerprinting. They presented a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in an indoor office environment. An appropriate measurement workplace and its setup are proposed to measure RSS values and are used to create an RSS-fingerprinting map. Accuracy of position determination is computed and evaluated. The results show that the performance of the BLE technology, for indoor localization purposes, highly depends on both the number of considered BLE nodes and the applied evaluation method (e.g., number of considered sectors). In [37], it is used BLE with multiple anchors and multiple radio channels to improve the reliability of power measurements. The accuracy-complexity trade-off affects the four most popular supervised learning techniques (k-Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network). They were evaluated in one and two-dimensional environments with four static anchors. The best result is obtained by the Random Forest with a classification accuracy of over 99%. Another pedestrian dead reckoning (PDR) localization technique is proposed in [38]. The authors used the smartphone sensors to estimate a pedestrian's location with a lower installation cost. Nowadays, smartphones have multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) that can be used for mobility and localization sensing [39]. In [40], using a magnetometer, a magnetic field strength fingerprinting approach is used to localize a pedestrian indoor. Accelerometer and gyroscope sensors are also used to find the pedestrian's traveled distance and heading estimation combined with the patterns of magnetic field strength to formulate a fingerprint database. Experiments were conducted with many models of smartphones and they were able to localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared to the K-Nearest Neighbor (KNN). The geomagnetic field localization has a limit because smartphones have embedded magnetic sensors from different manufacturers. In [41], the authors propose the use of geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. The evaluation using four different smartphones demonstrates that using a database with multiple smartphones has the same performance when compared to using a single smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved. In all of these works, there is no evaluation of the quality of the returned positions. Thus, even a result which the system is not quite sure is correct is still returned by the system.