Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

Location-based services have become of great importance to a wide range of applications in the era of mobile computing. Various wireless indoor localization techniques are emerging. Due to the wide deployment and availability of WiFi infrastructure, WiFi fingerprint-based indoor localization has become one of the most attractive localization techniques. Site survey to build WiFi fingerprint library and fingerprint matching-based real-time positioning are based on WiFi indoor localization method two basic stages. Equipment heterogeneity and environmental dynamics are two main factors affecting indoor localization accuracy. In order to solve these problems, an algorithm based on Hierarchical Edit Distance(HED) function is proposed to realize calibrations-free fingerprint comparison of heterogeneous devices. RSSI information collected by different mobile devices is transformed into AP sequence. The hierarchical energy level of each AP is calculated according to the difference of the RSSI values. Then the distance between the WiFi fingerprints is calculated by edit distance. In order to locate WiFi fingerprint RSSI information, we use HED to obtain K neighbors, and use the WKNN algorithm to predict position. In order to verify robustness and effectiveness of the algorithm, five different mobile devices were used to collect WiFi RSSI information in three different types of indoor environments. The average localization accuracy was 1.5m.

In this paper, a real WiFi fingerprint based indoor localization system is considered for experiments, including three primary components: the APP in the smart phone, the server system and the embedded localization algorithm. As we all know, one of the main drawbacks in fingerprint based localization is the labor intensity and time consumption of data collection. This paper proposes an improved graph-based semi-supervised learning (I-GSSL) to better overcome this problem. Apart from taking advantage of the indoor propagation model, the I-GSSL algorithm is proposed to handle the existing out-of-sample problem where an elastic regularization is considered as an extra constraint. Meanwhile, due to unequal amount of location information in the received signal strength (RSS) from different access points (APs) and the redundancy of RSS at APs, a double weighted K nearest neighbor (DWKNN) algorithm is proposed for localization. Experimental results show the proposed scheme achieves a better label propagation and localization accuracy.

In indoor localization applications, many different methods have been proposed to increase positioning accuracy. Among these methods, fingerprint-based techniques are generally preferred because they use existing resources such as Wi-Fi, Bluetooth, FM signals, etc., and can be implemented on commonly used devices such as mobile phones. In this paper, we evaluate different Wi-Fi fingerprint-based methods on two datasets (with and without room-to-room transition features) created from the same environment, and we investigate the impact of room-to-room transition features on classification performance. To the best of our knowledge, transition time between rooms has not been used in past studies on fingerprint-based indoor localization. This information is of significant importance, due to the physical distance between rooms. Therefore, in this study source room and transition time to a target room have been included as features in addition to signal sources and signal strength values in the target room. From preliminary experimental results we observed that the transition time between rooms increases the performance of all tested positioning algorithms, with the Back-propagation classifier showing the best performance increase (13%).

Supervised and semi-supervised deep probabilistic models for indoor positioning problems

It may be very difficult to receive the signals from satellite positioning systems due to the existing obstacles in indoor environment. Arising from the popularity of smart phones, Wi-Fi based indoor positioning technology has the advantages with convenient deployment and low hardware cost. In this study, we focused on indoor positioning using Wi-Fi fingerprint data that were collected in shopping malls. Due to the volatility of Wi-Fi signals and the high-dimensional sparseness of fingerprint data, we proposed a feature extraction algorithm, called joint multi-task stacked denoising auto-encoder (JMT-SDAE), aiming at reducing the dimensionality of the original fingerprint data and improving the indoor positioning performance in shopping malls. Furthermore, the features extracted by JMT-SDAE and gradient boosting decision tree (GBDT) were merged to construct a hybrid model, named as JMT-SDAE+GBDT. The experimental results based on 13 location datasets showed that the proposed feature fusion model had better positioning accuracy when compared with other existing positioners, and thus confirmed the effectiveness of our proposed feature extraction algorithm through multi-task learning.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

This paper presents a high-secure fingerprint authentication system using ring learning with errors (ring-LWE) cryptography to protect users’ fingerprint data more securely. A delay-optimized high-accuracy scheme for a fingerprint-features extraction approach is proposed to collect necessary features’ information from fingerprint images. In addition, a ring-LWE cryptography scheme using low-latency number theoretic transform (NTT) polynomial multiplications is deployed to speed up the ring-LWE encryption and decryption times. As a result, the processing time of the fingerprint authentication system is significantly reduced, and the fingerprint data are effectively protected. The simulation results show that the proposed NTT multiplication-based ring-LWE cryptography scheme for fingerprint features outperforms the existing works up to 46% and 44% in terms of encryption time and decryption time, respectively. The latency of the whole fingerprint authentication system is less than 160 ms, which makes it suitable for practical applications. Furthermore, performance analysis on entropy and similarity of the encrypted fingerprint features proves the domination of the proposed system compared with the previous systems in terms of confidentiality.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Achieving short range accuracy is an ongoing challenge in Indoor Positioning methods for providing location based services. Wireless positioning is a key enabler for location based services and many research papers have been published for indoor positioning using Bluetooth, Wi-Fi, ZIGBEE, IR etc. The published research suggests that position of wireless beacon can be determined based on the time of arrival and the measured signal strength. But, the margins of error in the calculated value using the published formulae, negatively impacts the efficacy of most indoor positioning applications. This paper recommends methods to improve the accuracy of the calculated value when using Bluetooth LE technology. Methods used for Indoor positioning as compared to Outdoor positioning need to handle attenuation factors such as Multipath fading, diffraction, reflection etc. These become especially critical for short ranges of ∼3m which is the general range of influence for a display unit. Hence, modelling a very short range Path Loss Prediction becomes imperative. This paper proposes a new Path Loss model for very short range between 10–12 ft with better accuracy and miniscule error rate.

Map information and sensor information is considered to be an effective way to assist indoor wireless location. Wireless location combined with map information and Sensor information can solve many problem such as positioning point cross the wall, falling into the wrong room. In this paper, a new indoor map matching algorithm was proposed based on particle filter. The indoor positioning system could be considered as non-linear system, so Non-parametric Monte Carlo (Monte Carlo) simulation method could achieve recursive Bayesian filtering, at the same time, the indoor GIS data, and sensor information, wireless location information was incorporated into the weight value of the particles to revise the observations constantly. Many experiments show that the indoor positioning accuracy is improved.

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.

China will establish the Xihe System based on Beidou/GNSS, Mobile Communication, Internet and Satellite Communication system to realize the Seamless Outdoor and Indoor Positioning in Wide Area (SOIPWA) on personal mobile terminals. Indoor Positioning System (IPS) is one of the main components and effective supplement for indoor positioning signal resource to resolve the positioning signal coverage of blind areas in complicated indoor environment based on current 2G/3G communication network and existing equipment. According to the requirement, the IPS adopted the unified time-frequency reference which affects the accuracy of indoor positioning directly. This paper provides an acquisition method of high accuracy frequency source used in the indoor positioning signal supplement. Positioning signals from outdoor base stations are acquired by RF front end and tracked by the carrier loop and code loop adjusting. After that, positioning signals are locked and synchronized to extract the high accuracy frequency source used for IPS, which can be built with low cost and precisely integrated with an outdoor positioning system. In practical test, the method satisfies the need of the accuracy and stability in time-frequency reference for IPS with high frequency accuracy, quick start and low drift rate through adjusting high accuracy Voltage Controlled Oscillator (VCO) by Digital to Analog Converter (DAC) with the frequency difference result from the adaptive loop.

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.

Space-time radio interferometric positioning system (STRIPS) is a indoor localization system with low complexity that is robust to multipath effects. Its general idea follows a radio interferometric positioning system (RIPS), where two transmitters transmit sinusoidal signals at slightly different frequencies to create a slowly-varying envelope at a receiver. The receiver employs a cost-efficient envelope detector to extract the low-frequency differential signal, of which its phase contains a range information. Unlike the original RIPS, however, the STRIPS operates in the millimeter-wave (MMW) band, where the signal interference and multipath are less prominent than a typical radio-frequency (RF) band. In this paper, we implement the STRIPS on hardware and experimentally validate its performance. The experimental results show that the prototype we have built successfully achieves a submeter accuracy on average.

Novel weighted ensemble classifier for smartphone based indoor localization