Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work. <s> BIB003 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation. <s> BIB004 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach. <s> BIB005 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> Considering that indoor positioning applications based on wireless local area network location fingerprinting would be mainly used on the mobile devices. This paper investigates the differences of received signal strength indication (RSSI) between different smart phones and the distributions of RSSI are also analyzed. The statistical analysis of experimental results shows the differences of RSSI between different smart phones are not trivial. Nearly 65% of the RSSI histograms are significantly peaked relative to Gaussian distribution and 65% of them are left-skewed distribution. Therefore, taking skewness and kurtosis coefficients into account, Gaussian distribution is not sufficient to ensure an accurate modeling of the RSSI. The impacts of human behavior on RSSI distribution are explored and two types of human behavior are revealed to be the cause of bi-modal distribution. The statistical data analysis could enable smart phone indoor positioning systems designers to improve positioning performance and to model location fingerprinting based indoor positioning systems. <s> BIB006 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Probabilistic Positioning Algorithm <s> Different positioning schemes are based on the probability p ( o | l ) to have an observation vector o at a Reference Point (RP) l , based on Gaussian probabilities. This paper presents an approach to speed-up the p ( o | l ) computation without any approximation. The consequent positioning scheme is called Smart P-FP. The comparison between Traditional (without any p ( o | l ) acceleration) and Smart P-FP is performed over different smartphones. The saved energy is about 90% for a large number of Access Points (APs) but is significant even with few APs: more than 86% with 3 APs. The proposed p ( o | l ) computation is beneficial to any p ( o | l ) -based positioning scheme. <s> BIB007
The most significant difference between the probabilistic positioning algorithm and the deterministic positioning algorithm is that the deterministic positioning algorithm matches the signal strength of corresponding fingerprint data by using the positioning algorithm to obtain the final position of the user, while the probabilistic positioning algorithm stores the probability distribution of the signal strength during a certain time in the fingerprint database  . Next, the probability position of the user's location is calculated by the Bayesian theory system based on signal strength. Kontkanen et al. BIB006 conducted a more comprehensive analysis and study of the application of probability and statistical methods in location services and concluded that the probability and statistical method not only had broad prospects in indoor positioning, but also had great potential in personnel tracking, distance calibration, and other related position services. The main task of the probabilistic positioning algorithm in the offline phase is to build the probability distribution model of signal strength collected through the sample data by using corresponding methods such as the histogram method and kernel function. In the online phase, the model calculates the probability that a user is at a certain location, and then uses the model to estimate the location of the user. (1) Probability Distribution of Signal Strength An important step is to obtain the probability distribution of the signal strength for the probabilistic positioning algorithm. The quality of the probability distribution model directly affects the efficiency and accuracy of positioning. The probability distribution model of signal intensity is established using the histogram method or kernel function based on the signal intensity measurements of different APs received at different RPs in the offline phase. Many researchers have used standard Gaussian distribution to model the probability distribution of signal strength. For example, Kaemarungsi et al. BIB002 analyzed the change and influence of signal intensity in the indoor environment and concluded that standard Gaussian distribution was more suitable for the probability distribution of the signal strength in the indoor environment. The Horus system, designed by Youssef et al. BIB003 , was the first system to locate indoors through the probabilistic positioning method that also used a standard Gaussian distribution in the distribution model. Luo et al.  suggested that the standard Gaussian distribution did not fully describe the signal strength in the indoor environment of the distribution. Luo  demonstrated that the signal intensity distribution in indoor environments tended to bimodal distribution rather than the traditional sense of the standard Gaussian distribution. They found that the signal strength values will be left, and its peak was much higher than the standard Gaussian distribution, so proposed the addition of appropriate parameters to perform a more suitable fit in the probability distribution of signal strength modeling. Chen Miao BIB005 proposed a multi-Gaussian mixture model to store the signal strength; and Ferris et al. BIB007 used a Gaussian process to model the signal strength. Although the Gaussian process was used to reduce the number of samples collected previously, the probability distribution model established at each reference point was the standard Gaussian distribution, which could not describe the signal strength in the indoor environment. (2) Probabilistic Positioning After establishing the probability distribution model of the signal strength, we needed the Bayesian system, combined with other methods (the most commonly used is clustering), on the posterior probability of the user's location calculated to obtain an estimate of the user's location. Naive Bayesian, Hidden-Bayesian, Bayesian networks, and maximum likelihood estimation are also widely used methods. The Horus system uses joint clustering, probability distribution, and deterministic positioning, first combining the clustering and probability distribution in the indoor positioning field simultaneously. The Bayesian theorem and the maximum certainty positioning algorithm were used to solve the positioning problem in different environments. The positioning error of the system was less than 0.6 m. Bisio et al. BIB004 proposed an intelligent probabilistic fingerprinting algorithm that improved the computational speed of prior probabilities and reduced the cost of positioning and the time-consuming costs associated with traditional probabilistic fingerprinting algorithms. Madigan et al. BIB001 used a Bayesian hierarchical model for positioning. Patwari et al.  selected the maximum likelihood function for positioning.