Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> (4) Other Deterministic Positioning Algorithms <s> In this survey-style paper we demonstrate the usefulness of the probabilistic modelling framework in solving not only the actual positioning problem, but also many related problems involving issues like calibration, active learning, error estimation and tracking with history. We also point out some interesting links between positioning research done in the area of robotics and in the area of wireless radio networks. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> (4) Other Deterministic Positioning Algorithms <s> Positioning a user is an essential ingredient of a location-based system. For the outdoor positioning, GPS is practically used. For the indoor positioning, Active Badge, BAT, Cricket, and so on have been introduced. These methods are very accurate but require special equipments dedicated for positioning. Instead of using special equipments, using existing equipments is more economical. For this reason, positioning methods of using existing wireless LAN access points have recently been introduced. Among the methods employed by them, the fingerprint methods are the most promising. Probabilistic method, K-NN (Nearest Neighbor), and Neural networks are the techniques used by the most location fingerprinting. We are proposing a new technique which is more efficient than these three. Our technique builds a decision tree during the off-line phase and determines a user's location referring to the tree. Time complexity analysis and experimental accuracy analysis of the proposed technique are presented in this paper. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> (4) Other Deterministic Positioning Algorithms <s> This paper discusses low-cost 3D indoor positioning with Bluetooth smart device and least square methods. 3D indoor location has become more and more attractive and it hasn't been well resolved. Almost each smart phone has a Bluetooth component and it can be used for indoor positioning and navigation in the nature of things. Least square algorithms are the powerful tools for linear and nonlinear parameters estimation. Various linear and nonlinear least square methods and their theoretical basics and application performance for indoor positioning have been studied. Simulation and hardware experiments results prove that nonlinear least square method is suitable for parameters estimation of Bluetooth signal propagation, and generalized least square method has better performance than total least square methods. Simulation and hardware experiments results also show that proposed method has the advantages of low cost, lost power consumption, perfect availability and high location accuracy. <s> BIB003
In addition to the above-mentioned mainstream positioning algorithm, there are several algorithms used by researchers that have achieved good results. Kushki BIB002 used the distance calculation method of kernel function and AP selection strategy. The average positioning error could be kept at approximately 2m. Feng Chen [54] used the combination of compression perception technology and clustering technology for indoor positioning. Yim BIB001 used a decision tree for indoor positioning, which reduced not only the complexity of the algorithm, but also the average positioning error when the sample space was larger (about 80 samples), in comparison with other algorithms. Li BIB003 used the Least Square and its related variants, such as the Weighted Least Square (WLS), and the Nonlinear Least Square (NLS) to achieve positioning match. The average error could be up to 1 m and positioning efficiency was high.