You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Indoor Fingerprint Positioning Based on Wi-Fi: An Overview 
Chapter Title:  Deterministic Positioning Algorithm 
1. Abstract of Cited Paper (BIB001):  In this paper, techniques and algorithms developed in the framework of Statistical Learning Theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no special-purpose hardware is required.The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques. 
2. Abstract of Cited Paper (BIB002):  Ultrasonic location systems are a popular solution for the provision of fine-grained indoor positioning data. Applications include enhanced routing for wireless networks, computer-aided navigation, and location-sensitive device behavior. However, current ultrasonic location systems suffer from limitations due to their use of narrowband transducers, This paper investigates the use of broadband ultrasound for indoor positioning systems. Broadband ultrasonic transmitter and receiver units have been developed and characterized. The utilization of these units to construct two positioning systems with different architectures serves to highlight and affirm the concrete, practical benefits of broadband ultrasound for locating people and devices indoors. 
3. Abstract of Cited Paper (BIB003):  Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm. 
4. Abstract of Cited Paper (BIB004):  Location-based systems for indoor positioning have been studied widely owing to their application in various fields. The fingerprinting approach is often used in Wi-Fi positioning systems. The K-nearest-neighbor fingerprinting algorithm uses a fixed number of neighbors, which reduces positioning accuracy. Here, we propose a novel fingerprinting algorithm, the enhanced weighted K-nearest neighbor (EWKNN) algorithm, which improves accuracy by changing the number of considered neighbors. Experimental results show that the proposed algorithm gives higher accuracy. 
5. Abstract of Cited Paper (BIB005):  Indoor localization using fingerprinting techniques became more attracting to researchers in recent years because of their high accuracy. However, unpredictable Received Signal Strength (RSS) is one of the challenges. In our proposed system, it can be reduced by using strong Access Points (APs) selection method to select a subset of reliable APs and decrease the input of feature dimension. In addition, the computational cost due to a large fingerprint database was addressed by using Affinity Propagation clustering algorithm. The efficient indoor localization system relies on Support Vector Regression (SVR) due to its advantage of high generalization ability. The detailed results of real experiments indicate that the proposed system using SVR achieved a high accuracy compared to the conventional indoor algorithm. 
6. Abstract of Cited Paper (BIB006):  In received signal strength (RSS)-based indoor wireless localisation system, radio pathloss model or radio map must be readily obtainable. However, the unpredictability of wireless channel makes it difficult to achieve high accuracy localisation in practice. In this study, the authors employed a multi-layer neural network (MLNN) for RSS-based indoor localisation without using the radio pathloss model or comparing the radio map. The proposed MLNN localisation system integrate the RSS signals transforming section, the raw data denoising section and the node locating section into a deep architecture. Furthermore, a boosting method is designed to promote location accuracy of the MLNN effectively. Experiment results demonstrate the feasibility and suitability of the proposed algorithm.

Literature Review:
The deterministic positioning algorithm utilizes real-time matching of fingerprint data by means like machine learning, data mining, artificial neural network, etc. This method is based on the measurement of signal strength; therefore, it is affected greatly by the precision of the fingerprint data acquired offline. The signal intensity vector of the user's location is V a = (V 1 , V 2 , . . . , V n ) and is used to calculate the similarity between the signal intensity vector of the user's location and the fingerprint data in the fingerprint database. The computing method is shown as Equation BIB003 . If it is calculated by the Manhattan distance, then q = 1. If it is calculated by Euclidean distance, then q = 2. After calculating the similarity, this method chooses the k fingerprint data based on the similarity. The k fingerprint data which contains the coordinates of the k position is the most likely position of users and calculating the mean values of the coordinates of the k fingerprint data increases accuracy. The average value is the location of the user. The radar BIB001 system is the earliest indoor positioning system that applies the nearest neighbor methods. Bahl et al. presented a new version called the Nearest Neighbor in Signal Space by improving the nearest neighbor methods. The core ideal of the NNSS was to match the nearest neighbor by calculating the distance between the signal strength received by users and the signal strength in the fingerprint database. Bahl BIB001 compared this method with the perceived similarity and channel model methods and found that the positioning accuracy of this method could reach 2.94 m. It still has some issues in its practical applications, but has provided new thoughts in researching indoor positioning. Ma et al.  presented a new method called the Clustering Filtered KNN (CFK) which combined clustering with the KNN method. CFK can divide the entire fingerprint database into several non-adjacent parts by using hierarchical clustering as per the physical location of each neighbor point. It selects the nearest neighbor points in one cluster to match the user's location. Ma  also found that the average positioning error and positioning median error were both less than the KNN, as was the calculating cost. Guowei et al. BIB005 used the Jeffries Matusita distance instead of the Euclidean distance. The distance formula was established based on Euclidean distance, but expanded the error function elements (which were small), and could stably achieve the Euclidean distance. First, their method obtained the nearest neighbor point datasets by KNN before extracting the reference points from these sets by the K-n algorithm. Finally, they used similarity between the user and reference points, and set the frequency of the reference points (showing as weight) to participate in matching. Compared to KNN, the method put forward by Guowei BIB005 has carried out deeper digging on the correlation of signal strength value and has some advantages. Shin et al. BIB002 presented the EWKNN (Enhanced Weighted K-nearest Neighbor algorithm) algorithm. First, it generated a list of signal strength by comparing fingerprint data in the fingerprint database with signal strength measured while positioning for the first selection. Next, it compared the rest of the data in the list for the second selection. The k in Equation (11) represents the number of RPs reserved and utilizes the differences in signal strength as weight to participate in the second selection. To acquire the final positioning result, it requires some iterations. The EWKNN reduces the error in comparison to the KNN algorithm, and has better adaptability for k which is variable. (2) Support Vector Machine Statistical learning theory is an important theoretical basis for the machine learning method, the theory of statistical estimation, and the prediction of small sample theory. The support vector machine (SVM) is one of the youngest, most practical, and highest potential methods in statistical learning theory as it translates the input space into a higher dimensional space by nonlinear transform defined by inner product function, and calculates the optimal classification plane in this space. The expression formula is shown by Equation BIB004 . Here, T i is the Lagrange multiplier corresponding with each sample. b is the classification threshold and K(x i , x) is the optimal classification plane of the inner product function. This function can achieve linear classification after a nonlinear transformation and is the kernel function of support vector machines (SVM) which are classified by its kernel function. So far, the SVM used in indoor positioning generally divides the fingerprint matching problem into two kinds: the SVC problems and the Support Vector Regression (SVR) problems. Brunato et al. BIB006 systematically illustrated the application background and principle of statistical learning theory and SVM applied in indoor fingerprint positioning, and established the theoretical foundation for the application of SVM in the field of indoor positioning. Brunato BIB006 used SVM to match fingerprint data and compared them with WKNN, Bayesian regression analysis and artificial neural network. They found that the error of positioning results by SVC was very low, and the results by SVR were roughly the same as the WKNN algorithm. Abdou et al.  presented regression analysis positioning technology based on the similarity of support vector machines by combining SVM and cluster. The method clustered the fingerprints in the fingerprint database via the similarity propagation model in an offline collection phase, and studied each type of fingerprint data by SVM for correlation information based on the RPs and signal strength values. In the online positioning stage, the user's mobile device uploaded the collected signal strength information to the server. The server determined the user's signal strength information membership category through pattern recognition and used the strongest AP to choose the corresponding AP information. Next, the information would be served as the input of the support vector machine regression analysis to conduct positioning precisely. The positioning results were good, and positioning errors were far less than other methods such as KNN, and Bayesian. Wu et al. BIB007 combined SVM and particle filter. They classified the data in the fingerprint database as per the known position by SVM and used Pedestrian Dead Reckoning to achieve a positioning match, and optimized the data by particle filter. The positioning accuracy was 1.2 m.