# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 2 Related Work  
In RADAR [15; 16], one of the first localization systems using RSS and fingerprinting, RSS readings are taken at different points in the place where the localization is to be carried out. Each reading contains the RSS values received from the WiFi routers. Such information is recorded in a fingerprint database. After this training, when a user requests your location, the RSS values of the request are compared with those observed in the training phase using the kNN classifier, which will return the most likely position from which the request was sent. In the Horus System [23; 24], a joint grouping technique is proposed for the location estimation using a probabilistic method. In this system, each possible coordinate that the candidate may be in is considered a class or category. To minimize the distance error, the location is chosen where its probability is the highest. Increasing the number of samples at each sampling site improves accuracy, as this improves the estimation of the mean and standard deviation of the Gaussian distribution used. Similar probabilistic systems have also been proposed in other works [25; 26; 27]. A training-based method for determining the localization using classifiers based on neural networks is proposed in [28]. In a more broad approach, the authors in [29] compared the various classifiers mentioned: neural networks, kNN, and probabilistic methods. Their results show that all techniques performed well in different scenarios. In [30], several experiments were carried out to show that localization techniques for indoor environments based on training (fingerprinting) perform better than the traditional ones based on trilateration/multilateration. The main reason given was the multipath phenomenon, in which the signal behavior is affected by the various obstacles encountered in such scenarios. However, an observation raised in this work is that the training-based localization techniques suffer degradations in the location accuracy in dynamic environments, where the properties of the place can change with time (e.g., rearranging cabinets, tables, partitions). Due to multipath, shadow fading, and some hardware restrictions, outliers in RSS measurements are not uncommon. If the measurement is a outlier, either in the offline or online phase, the resulting position estimate is likely to be the wrong [31]. In [19], an outlier detection technique called iF_Ensemble was developed. The proposed solution is able to analyze the quality of the RSS using a combination of supervised, unsupervised, and ensemble machine learning methods. In this solution, the isolation forest (iForest) is used as an unsupervised learning method. The supervised learning method includes support vector machine (SVM), kNN, and random forest (RF) classifiers. In [17], the authors propose a novel multiple-bandwidth generalized regression neural network (GRNN) with the outlier filter indoor positioning approach, named GROF, to improve the positioning accuracy and robustness. It uses a new type of multiple bandwidth kernel architecture to achieve more flexible regression performance than the traditional GRNN. In addition, an outlier filtering scheme is used to improve the system's robustness against RSS fluctuation caused by the dynamic environment. A Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN) is proposed in [32]. This solution returns sets of k results with probabilities higher than some threshold T. The result is refined using filters to remove results with the aid of a spatial index and probabilistic candidate selection. In [33], a probabilistic threshold kNN (PTkNN) query is used. It uses a combination of three techniques for the efficient processing of PTkNN queries. Minimal indoor walking distance (MIWD) metric to prune objects that are too far away, efficient probability estimation to prune unqualified candidates, and an efficient probability evaluation for the remaining candidate and k-subsets.