SAP dissimilarity based high performance Wi-Fi indoor localization

Compared with the Wi-Fi Received Signal Strength (RSS) commonly-used for the indoor localization, the Channel State Information (CSI) can be used for precise ranging to achieve the high Wi-Fi indoor localization accuracy since it includes the fine-grained physical-layer information such as the amplitude and phase of each subcarrier during the signal transmission. Due to the lack of the theoretical analysis of the localization error bound in the existing CSI ranging-based localization methods, it is not easy to compare the ideal performance of different localization methods. Therefore, this paper proposes a CSI ranging-based Wi-Fi indoor localization error bound analysis method, which is based on the indoor signal propagation model to derive out the CSI ranging-based localization error bound by considering the relationship between the localization accuracy and the path loss, shadow fading, multipath effect and asynchronous effect. Besides, through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, as well as discusses the impact of different experimental parameters on the localization performance.

In indoor positioning system, the user’s location is estimated based on user fingerprints database collected. The received signal from a user at a certain location is compared with all database entries and the best match is selected as the correct estimation. For a given number of access points (APs), optimizing the locations of APs can significantly improve the performance of user location estimation. In this work, the probability of correct estimation is analyzed based on APs location. Both analytical expressions and numerical results are presented and discussed. It has been shown that the probability of correct estimation is highly affected by the APs locations. Furthermore, a new technique is proposed to remarkably reduce the search-cost when comparing a received user signal to the data-base. The performance of the system is analyzed considering different APs locations, different user locations, and different noise variances. It is shown that user’s location estimation is significantly improved when compared to existing techniques.

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

The global positioning system (GPS) is a popular choice for accurate location sensing and is often used in navigation systems. However, GPS fails to operate inside buildings because satellite signals are blocked. Thus, GPS cannot be used for localization indoors. Moreover, it is difficult to distinguish between the different floors. Research to overcome this problem using the ultrawideband (UWB), radio frequency identification (RFID), and infrared ray (IR) has been conducted. These methods, however, have drawbacks, such as the need for an additional communication module and database. Wireless local area network (WLAN) access point (AP) is widely installed at various locations, and using these WLAN APs can be a viable alternative. The proposed system in this paper indicates the location information in the service set identifier (SSID) of virtual AP. Therefore, merely by scanning the WLAN signals, mobile users can detect their location indoors.

A novel indoor localization method for Wi-Fi devices such as smart phones is presented. With an indoor path loss model, the distance between a mobile device (MD) and an access point (AP) can be estimated form the received signal strength (RSS). Then, least-squared-error (LSE) triangulation yields the position of the MD. Though indoor channel characteristics are highly fluctuating with perturbations, a reference point (RP) can be used to compensate for channel instability. We present two contributions: cross monitoring AP and weighted triangulation. Cross monitoring AP removes the need of deploying RPs since it can play the dual role of a regular AP and a RP. Weighted triangulation yields improvement in the accuracy of LSE estimation of the MD position. Experimental results show weighted triangulation with cross monitoring AP provides indoor localization with an error less than 2 meters.

In order to realize the high precision indoor positioning system, an embedded gateway is designed for data transmission and forwarding, and the server is developed on Windows platform for storing data, obtaining and showing location information. The indoor positioning system architecture is given, focusing on the design and implementation of gateway and server. ARM11 processor is used to design a gateway. Gateway architecture and software development are introduced. A server solution is described. The results show that the designed gateway and server meet the requirements of data transmission and good positioning accuracy.

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

Positioning services are increasingly used for applications such as navigation, advertising and social media. While outdoor navigation based on GPS and/or cellular systems works well, indoor navigation is a much tougher challenge. This paper proposes an indoor localization using Wi-Fi received signal strength indicator (RSSI) measurements from existing access points. Using RSSI data from multiple access points, additional access points will be added in the system thru linear regression statistical model to create virtual access points (VAPs). This paper fuses the low-cost and flexible system of fingerprint localization with VAP creation and the improvement in accuracy by adapting Kalman Filter (KF) and Particle Filter (Pf). The effectiveness of using KF with VAP has demonstrated positive results in previous research. This paper aims to integrate the use of PF in the system. To improve the performance of indoor localization, the proposed model will be an improved virtual access point localization, proposed filtering (VAP+KF+PF).

Selective AP probing for indoor positioning in a large and AP-dense environment

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

The increasing demand of indoor Location Based Services (LBS) has made Indoor Positioning System (IPS) a hot research topic. Recently, various machine learning techniques have been developed to tackle the problems of IPS. The WLAN fingerprinting technique is one of the most cost effective choices for IPS due to the use of existing Wi-Fi networks in commercial and public buildings. However, the variation of Received Signal Strength (RSS) at any specific location due to the indoor fading issue leads to serious challenges in the accuracy of the distance estimates. In this paper, we propose a new technique by using Probabilistic Neural Network (PNN) multi-classifier based on Multiple Service Set Identifiers (MSSIDs) that are configured on the same access point. A spatial voting scenario for three PNN-classifiers is proposed as a tool to determine the location of the user. Using a spatial voting based technique is specifically designed to tackle the negative impact of the multi-path propagation on the performance of IPS. The proposed system is performed inside the College of Engineering and Applied Sciences (CEAS) at Western Michigan University. The performance of the proposed system compared with some of the common methods such as K-Nearest Neighbors (K-NN) and multi-class support vector machine (SVM). According to the experimental results, the spatial voting of the three PNN-dassifiers can significantly mitigate the adverse effects of RSS variation. The performance of the multi-classifier exhibits superior performance to that of the other single classifiers in all Area of Interest (AOI). Over 3000 m2 AOI, the proposed system achieves localization accuracy < 0.73m and with precision 90 % of the distance error less than 2m.

The time-of-flight of a signal captures the time it takes to propagate from a transmitter to a receiver. Time-of-flight is perhaps the most intuitive method for localization using wireless signals. If one can accurately measure the time-of-flight from a transmitter, one can compute the transmitter's distance simply by multiplying the time-of-flight by the speed of light. Today, GPS, the most widely used outdoor localization system, localizes a device using the time-of-flight of radio signals from satellites. However, applying the same concept to indoor localization has proven difficult. Systems for localization in indoor spaces are expected to deliver high accuracy (e.g., a meter or less) using consumer-oriented technologies (e.g., Wi-Fi on one's cellphone). Unfortunately, past work could not measure time-of-flight at such an accuracy on Wi-Fi devices. As a result, over the years, research on accurate indoor positioning has moved towards more complex alternatives such as employing large multi-antenna arrays to compute the angle-of-arrival of the signal. These new techniques have delivered highly accurate indoor localization systems. Despite these advances, time-of-flight based localization has some of the basic desirable features that state-of-the-art indoor localization systems lack. In particular, measuring time-of-flight does not require more than a single antenna on the receiver. In fact, by measuring time-of-flight of a signal to just two antennas, a receiver can intersect the corresponding distances to locate its source. Thus, a receiver can locate a wireless transmitter with no support from the surrounding infrastructure. This is quite unlike current indoor localization systems, which require multiple access points at known locations, to find the distance between a pair of mobile devices. Furthermore, each of these access points need to have many antennas -- far beyond what is supported in commercial Wi-Fi devices.

Wi-Fi based indoor localization is gaining popularity because of the wide adoption of WiFi technologies in existing infrastructure. In order to increase the accuracy of Wi-Fi localization, we develop a novel localization method using One-to-all Regularized Logistic Regression-based Classification (ORLRC). This method is based on logistic regression. The proposed ORLRC is compared with the k-means clustering approach and achieves a location estimation accuracy of 95.8% comparing to an accuracy of 80% by the k-means clustering approach.

The location method based on Received Signal Strength Indication (RSSI) ranging has the advantages of low development cost and simple implementation mechanism, and is the mainstream indoor location method nowadays. However, at present, there is a lack of systematic research on the characteristics of Access Point (AP) received signal strength, which cannot meet the signal strength characteristics required by specific indoor positioning environment. Detailedly, in this study, the indoor distribution characteristics of 2.4 GHz RSSI were analyzed experimentally from three influencing factors including antenna orientation of the receiver, type of wireless network card of the receiver and height difference of the transmitting and receiving antenna. The experimental results show that: (1) The RSSI value measured is the strongest When the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the antenna is vertically backward to the transmitter, it is the opposite. The difference between the strongest signal and the weakest signal is 20%–25% at the same test point. (2) The network card with a large measurement range should be selected for data collection on the premise that the quantization step size is 1 and the smoothness is small. Avoiding using the network card for positioning with the maximum limit of measurement range is proposed also. (3) The path loss index of the ranging model is affected by the height difference of the antenna, resulting in a deviation of 0.1–0.2.

Recently, machine learning (ML) has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength (RSS) information and labels accurately. Existing ML-based indoor localization systems are less robust because they only adopt the output with the highest probability. This affects the final location estimate, hence compromising accuracy due to the severity of RSS fluctuations. Since different ML algorithms (MLAs) yield different performances, it is therefore intuitive to fuse predictions from multiple MLAs to improve the positioning performance in the presence of signal fluctuation. In this article, we propose SmartLoc, a smart wireless indoor localization framework to enhance indoor localization. In the offline phase, multiple MLAs are trained by utilizing an offline database. We further apply probability alignment to guarantee the predicted probabilities of each MLA at the same confidence level. In the online phase, given a testing RSS sample of a user at an unknown location, we extract the labels with probabilities greater than a certain threshold from each MLA to construct the space of candidate labels (SCL). The size of SCL can be adaptively determined by using our proposed dynamic size determination algorithm. Based on the SCL, we propose a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously. A high label credibility indicates that the frequently occurred label is more likely to be true. Experimental results in a real changing environment verify the superiority of SmartLoc, outperforming the best among comparative methods by 10.8% in 75th percentile accuracy.