Transition Model–driven Unsupervised Localization Framework Based on Crowd-sensed Trajectory Data

Nowadays, it becomes very convenient to collect synchronized WiFi received signal strength and inertial measurement (RSS+IMU) sequences by mobile devices, which enables the promising solution to conduct unsupervised indoor localization without the pain of radio-map calibration. To relax the needs of floor-map information or trajectory knowledge, this paper proposes to learn a transitional model (TM), which segments the massive unlabeled sequences to train a model that captures the expected relationship between {zt--1, zt } and ut--1, where zt--1, zt are two consecutive signal states at t and t -- 1, and ut--1 is the one step motion calculated from inertial data. We present both a transitional model in signal space (TMS) and a transitional model to predict motion from signal change (TMM) to represent the relationship in different ways. In particular, from the massive sequences, both the signal states and the one step motion are smoothed from the nearest neighbours, so that the transition model learns the expected relative signal state change triggered by the smoothed one step motion. Its distinctive features are that (1) no external floor-map or trajectory knowledge is needed; (2) it can be continuously on-line refined as unlabeled sequences are incrementally collected. KALMAN filter based on-line mobile user location tracking methods are given for both models. Experiments show that the transition model based localization method provides comparable accuracy with the manually fingerprint calibration methods.

This paper proposes an enhancement for a well know method of mobile positioning Based on Signal Strength Measurements and Database Correlation Method (DCM). Signal Strength Measurements is based on the view that the variation in the received signal strength is in the main, position dependent. Many Works have been done previously on this method regarding the techniques used for analyzing the Empirical Database used to store the Signal Strength Measurements. The Proposed Mobile positioning Systems consists of a Mobile Position determination server (MPDS) installed in the operator site and a Grid of Mobile listeners installed in a selected locations between the Mobile network radio sites, these Radio Listeners periodically send Signal Strength & Network Measurement Reports to the Mobile Position determination server through any Data Bearer (SMS/GPRS), in order to provide an assisted data to the mobile positioning applications allowing calculations to be done without the need of conducting new Drive Tests when new sites are installed or new configuration parameters are applied to the existing sites.

Unsupervised localization based on received signal strength and inertial measurement unit (RSS + IMU) sequences is an important branch of indoor localization community, among which Transitional Model to predict Motion from signal change (TMM) is a promising method that does not require much prior knowledge, e.g., floor maps. However, there are still many challenging problems existing in TMM. Among them, the computation burden of the model is awfully heavy, and its localization error is also a painful point. In order to solve the above challenges, we propose a novel transition model, called enhanced TMM (ETMM). First, trajectory data enhancement is proposed to enrich the diversity of trajectory data, which improves the robustness of the transition model by allowing the model to learn more comprehensive and detailed information from the environment. Second, the computation burden has been significantly reduced by using effective RSS preprocessing, which reduces the data dimension and the solution domain. Finally, in order to increase the robustness and localization accuracy of the model, we propose a direction matching (DM) constraint to enhance the mapping relationship between the consecutive RSS signals and the one-step motion. Experiments show that ETMM has a better performance compared with the state-of-the-art method in terms of localization accuracy, computation cost, and robustness.

As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.

This paper presents a technique which is based on pattern recognition techniques, in order to estimate Mobile Terminal (MT) velocity. The proposed technique applies on received signal strength (RSS) measurements and more precisely on information extracted from Iub air interface, in wIDeband code-division multiple access (WCDMA) systems for transmission control purposes. Pattern recognition is performed by HIDden Markov Model (HMM), which is trained with downlink signal strength measurements for specific areas, employing Clustering LARge Applications (CLARA) like a clustering method. Accurate results from a single probe vehicle show the potential of the method, when applied to large scale of MTs.

Wireless ad hoc networks often require a method for estimating their nodes' locations. Typically this is achieved by the use of pair-wise measurements between nodes and their neighbours, where a number of nodes already accurately know their location and the remaining nodes must calculate theirs using these known locations. Typically, a minimum mean square estimate (MMSE), or a maximum likelihood estimate (MLE) is used to generate the unknown node locations, making use of range estimates derived from measurements between the nodes. In this paper we investigate the efficacy of using radio frequency, received signal strength (RSS) measurements for the accurate location of the transmitting nodes over long ranges. We show with signal strength measurements from three or more wireless probes in noisy propagation conditions, that by using a weighted MMSE approach we can obtain significant improvements in the variance of the location estimate over both the standard MMSE and MLE approaches.

As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target’s movement is continuous and that the target’s current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

ABSTRACTSignal‐strength‐based location estimation in wireless sensor networks is to locate the physical positions of unknown sensorsviathe received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly‐proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose a new hotspot ranking-based indoor mapping and mobility analysis approach based on the sporadically collected crowdsourced Wi-Fi received signal strength (RSS) data. This approach aims to construct the indoor mapping, as well as achieve the mobility analysis of the users following their daily motion patterns in target environment. First, we perform the wavelet analysis with respect to each RSS sequence to mitigate the noise interference to some extent. Second, we develop a new multidimensional scaling approach to map each RSS data into a linear one in the 2-D signal space, which is followed by the density clustering approach to merge the linear ones into different clusters based on the spatial correlation property. Finally, we construct the indoor mapping from the signal into physical spaces by the concept of hotspot ranking order, as well as the transfer relations among different RSS clusters and different physical sub-areas. The experimental results demonstrate that the proposed approach can achieve the superior performance in terms of indoor mapping and mobility analysis in an unknown indoor environment.

The paper focuses on the performance analysis of the timing acquisition scheme based on received signal strength (RSS) measurements for ultra-wideband (UWB) communication systems. To our knowledge, few paper discusses the performance of the timing acquisition scheme based on RSS measurements for UWB in details. The timing acquisition scheme based on RSS measurements has many advantages, such as low cost, high speed and robustness. However, low transmission power limitation and complicated indoor multipath propagation environments make it difficult to measure accurate RSS at the receiver and affect the accuracy of the time delay offset estimation for the local template signals. In addition, the time variation of the indoor channel state can form a multiple-to-one mapping relation between the RSS measurements and the distance, which largely limits the method application. In the paper, we analyze the accuracy of time delay offset estimation for this method by deriving the Cramer-Rao lower bound (CRLB). The number results show that the accuracy of the timing acquisition method based on RSS measurements suffers from both the UWB signal transmission speed and the varying distance between the transmitter and receiver. It is noted that though the accuracy of the method can hardly fulfil the fine timing acquisition requirement for UWB systems, it will largely reduce the coarse timing acquisition time and improve the performance for UWB timing acquisition systems if combined with other timing acquisition ones

Recent developments in the fields of Smart Phones and Wireless Communication Technologies such as Wi-Fi, cellular networks, Bluetooth and VLC have made possible to realize Indoor Positioning System with a suitable accuracy. Fingerprinting based on Received Signal Strength (RSS) measurements is commonly the most popular method of localization because of its high accuracy compared to other methods. It does not require line-of-sight measurements of transmitters TXs, RSS-based Fingerprinting localization usually consists of two main phases: offline (training) and online (estimation). The database sizes grow rapidly as the coverage areas and the number of LEDs increases. In this paper, an improvement over traditional RSS-based fingerprinting localization is proposed by reducing the database sizes in both training and estimation phases. The reduction is based on a concept of compressed RSS images, which allows through an astute 2-D frequency analysis, only a fraction of the transform-domain components need to be stored and transferred to the receivers. The proposed localization method reduces the total number of fingerprint reference points RPs over the localization space; thus, minimizing both the time required for reading visible light signals and the number of reference points needed during the fingerprinting training process, which eventually makes the process less time-consuming, hence less energy-consuming. Moreover, the proposed system is able to provide results close to that given by the traditional RSS-based fingerprinting approach, with a similar localization estimation error and an important reduction in the database sizes.

In order to reduce the overheads of constructing a dense radio map as well as to prevent the accuracy degradation, various crowdsourcing-based methods have been developed to automatically collect WiFi RSS measurements to build the radio map. Unlike existing studies focusing on crowdsourcing techniques, this paper deals with how to efficiently and accurately produce the radio map based on crowdsourcing RSS measurements which suffer from noisy location labels. In the literature, gaussian process regression (GPR) is commonly adopted to construct radio maps by sufficiently making use of the spatial correlation among received signal strength (RSS) measurements at nearby locations. However, the standard GPR does not take into account the uncertainties in the location labels attached to the crowdsourcing RSS measurements, which consequently deteriorates the performance of localization systems relying on the corresponding radio maps. Hence, the standard GPR is extended to mitigate the influences of noisy location labels. Experiments are carried out based on practical RSS measurements, and confirm the feasibility and superiority of the proposed method.

Mobile phone equipped with WiFi, Bluetooth, MEMS-IMU, and other sensors is a perfect platform to provide location based service for personal users. For positioning using wireless signals, the fingerprints of received signal strength (RSS) measurements from access points (APs) are typically used, which are called fingerprinting methods. The main characteristic of the fingerprint positioning technologies is to calculate the similarities between the observed RSS and the known RSS fingerprints. Compared with WiFi, the iBeacon APs are with lower cost, smaller size, and more flexible location, so it is much more suitable and economic for applications where enough WiFi APs are not available. In this paper, the RSS noise characteristics of iBeacon and WiFi signals are compared. The fingerprinting positioning performance by using iBeacon and WiFi RSS measurements are further presented and evaluated.

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced Indoor Positioning System (IPS) has to handle diverse devices and the inherent heterogeneity in Received Signal Strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this paper, we focus on Mean Differential Fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all APs from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér-Rao Lower Bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS datasets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

Device-free localization (DFL) is the estimation of the position of a person or object that does not carry any electronic device or tag. Existing model-based methods for DFL from RSS measurements are unable to locate stationary people in heavily obstructed environments. This paper introduces measurement-based statistical models that can be used to estimate the locations of both moving and stationary people using received signal strength (RSS) measurements in wireless networks. A key observation is that the statistics of RSS during human motion are strongly dependent on the RSS level” during no motion. We define fade level and demonstrate, using extensive experimental data, that changes in signal strength measurements due to human motion can be modeled by the skew-Laplace distribution, with parameters dependent on the position of person and the fade level. Using the fade-level skew-Laplace model, we apply a particle filter to experimentally estimate the location of moving and stationary people in very different environments without changing the model parameters. We also show the ability to track more than one person with the model.