The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
A common approach to localization within existing radio frequency (RF)-based beacon networks is to use the received signal strength (RSS) as a fingerprint that is matched against a database of RSS-location pairs to determine the receiver's location @cite_23 . This database is typically generated manually via a site-survey,
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The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints. Radio signal strength (RSS) is a reasonable proxy for link quality, but its accurate estimation requires frequency and spatial diversity due to fluctuation caused by fading. We consider a Rayleigh Rician fading model, and gather RSS measurements during motion in a complex environment to enable gradient estimation. Using the RSS gradient, we develop control laws to track active sources. These may be used to establish and preserve connectivity among collaborative autonomous agents, to locate and approach radio sources, as well as deploying agents to assist mobile ad hoc networks (MANETs). Among the various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is apt to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile in run-time. In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and reform it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is 1 meter. Compared with the traditional RSS-based approaches in dynamic environment, the accuracy improves up to 10 times. In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization. With the technical advances in ubiquitous comput- ing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoreti- cal base and develop a localization algorithm for building a zero- configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters). The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an 1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods. Locating smartphone users will enable numerous potential applications such as monitoring customers in shopping malls. However, conventional received signal strength (RSS)-based room-level localization methods are not likely to distinguish neighboring zones accurately due to similar RSS fingerprints. We solve this problem by proposing a system called feature-based room-level localization (FRL). FRL is based on an observation that different rooms vary in internal structures and human activities which can be reflected by RSS fluctuation ranges and user dwell time respectively. These two features combing with RSS can be exploited to improve the localization accuracy. To enable localization of unmodified smartphones, FRL utilizes probe requests, which are periodically broadcast by smartphones to discover nearby access points (APs). Experiments indicate that FRL can reliably locate users in neighboring zones and achieve a 10 accuracy gain, compared with conventional methods like the histogram method. In traditional radio-based localization methods, the target object has to carry a transmitter (e.g., active RFID), a receiver (e.g., 802.11x detector), or a transceiver (e.g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow tracking of transceiver-free objects. Based on radio signal strength indicator (RSSI), which is readily available in wireless communication, three tracking algorithms are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has potential to track multiple objects but requires calibration. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the best side length between sensor nodes in the grid is 2 meters and the best-cover algorithm can reach localization accuracy to 0.99 m Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g. triangulation). In this paper, we propose a novel approach based on multiple neural networks. We demonstrate with experimental results that by training and then activating different neural networks, tailored on the user orientation, high definition accuracy is achievable, allowing indoor navigation with a cost effective Bluetooth (BT) architecture. Wireless Indoor localization systems based on RSSI-values typically consist of an offline training phase and online position determination phase. During the offline phase, geo-referenced RSSI measurements, called fingerprints, are recorded to build a radio-map of the building. This radiomap is then searched during the position determination phase to estimate another nodes' location. Usually the radiomap is build manually, either by users pin-pointing their location on a ready-made floorplan or by moving in pre-specified patterns while scanning the network for RSSI values. This cumbersome process leads to inaccuracies in the radiomap. Here, we propose a system to build the floorplan and radio-map simultaneously by employing a handheld laser mapping system in an IEEE802.15.4-compatible network. This makes indoor- and radio-mapping for wireless localization less cumbersome, faster, more reliable and delivers a new way to evaluate wireless localization systems. This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,…) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm. A radio transceiver normally provides received signal strength (RSS) quantized with 1 dB or higher step size. Currently, we know of no application which has demonstrated a need for sub-dB RSS estimates. In this paper, we demonstrate the need for, and benefits of, greater resolution in RSS for breathing rate monitoring and gesture recognition. Measuring RSS requires orders of magnitude less bandwidth than measuring OFDM channel state information (CSI) or frequency modulated carrier wave (FMCW) channel delay. We have designed a prototype with an off-the-shelf low-power transceiver and a processor to achieve an RSS estimate with a median error of 0.013 dB. We experimentally verify its performance in non-contact breathing monitoring and gesture recognition. We demonstrate that simply decreasing the step size of RSS lower than 1 dB can enable significant benefits, enabling extremely low bandwidth RF sensing systems. Results indicate that RFIC designers could enable significant gains for RF sensing applications with four more bits of RSS quantization. This paper presents and evaluates a method for simultaneously tracking a target while localizing the sensor nodes of a passive device-free tracking system. The system uses received signal strength (RSS) measurements taken on the links connecting many nodes in a wireless sensor network, with nodes deployed such that the links overlap across the region. A target moving through the region attenuates links intersecting or nearby its path. At the same time, RSS measurements provide information about the relative locations of sensor nodes. We utilize the Sequential Monte Carlo (particle filtering) framework for tracking, and we use an online EM algorithm to simultaneously estimate static parameters (including the sensor locations, as well as model parameters including noise variance and attenuation strength of the target). Simultaneous tracking, online calibration and parameter estimation enable rapid deployment of a RSS-based device free localization system, e.g., in emergency response scenarios. Simulation results and experiments with a wireless sensor network testbed illustrate that the proposed tracking method performs well in a variety of settings. In wireless networks, a client's locations can be estimated using the signals received from various signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when the signal-strength values change with time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal- strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the variations of signal strength over different time periods without the need to rebuild the radio maps repeatedly. We demonstrate the effectiveness of our proposed technique on realistic data sets collected from an 802.11b wireless network and a RFID-based network. Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings. WLAN RSS-based localization has been a hot research topic for the last years. To obtain high accuracy in the noisy wireless channel, WLAN location determination systems usually use a calibration phase, where a radio map, capturing the signal strength signatures at different locations in the area of interest, is built. The radio map construction process takes a lot of time and effort, reducing the value of WLAN localization systems. In this paper, we propose 3D ray tracing as a way for automatically generating a highly accurate radiomap. We compare this method to previously used propagation modeling-based methods like the Wall Attenuation Factor and 2D ray tracing models. We evaluate the performance of each method and its computational cost in a typical residential environment. We also examine the sensitivity of the localization accuracy to inaccurate material parameters. Our results quantify the accuracy- complexity trade-off of the different proposed techniques with 3D ray tracing giving the best localization accuracy compared to measurements with acceptable computational requirements on a typical PC. Being applicable for almost every scenario, mobile localization based on cellular network has gained increasing interest in recent years. Since received signal strength indication (RSSI) information is available in all mobile phones, RSSI-based techniques have become the preferred method for GSM localization. Although the GSM standard allows for a mobile phone to receive signal strength information from up to seven base stations (BSs), most of mobile phones only use the information of the associated cell as its estimated position. Therefore, the accuracy of GSM localization is seriously limited. In this paper, an algorithm for GSM localization is proposed with RSSI and Pearson's correlation coefficient (PCC). The information of seven cells, including the serving cell and six neighboring cells, is used to accurately estimate the mobile location. With redundant information, the proposed algorithm restrains the error of Cell-ID and shows good robustness against environmental change. Without any additional device or prior statistical knowledge, the proposed algorithm is implementable on common mobile devices. Furthermore, in the practical test, its maximum error is below 550 m, which is 100 m better than that of Cell-ID, and the mean error is below 150 m, which is 250 m better than Cell-ID. In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques. During the last decades, location based services have become very popular and the developed indoor positioning systems have achieved an impressive accuracy. The problem though is that even if the only requirement is room-level localization, those systems are most of the times not cost-efficient and not easy to set-up, since they often require time-consuming calibration procedures. This paper presents a low-cost, threshold-based approach and introduces an algorithm that takes into account both the Received Signal Strength Indication (RSSI) of the Bluetooth Low Energy (BLE) beacons and the geometry of the rooms the beacons are placed in. Performance evaluation was done via measurements in an office environment composed of three rooms and in a house environment composed of six rooms. The experimental results show an improved accuracy in room detection when using the proposed algorithm, compared to when only considering the RSSI readings. This method was developed to provide context awareness to the international research project named SmartHeat. The projects aims to provide a system that efficiently heats a house, room by room, based on the habitants' habits and preferences. In this paper, we consider the indoor localization problem, i.e., identifying the Cartesian coordinates of an object or a person under the roof. To solve this problem we consider an RF-based localization method called profiling , a two-step process, where a radio map of the monitored area is first constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose a K nearest neighbor (KNN) profiling-based localization method dubbed LEMON (location estimation by mining oversampled neighborhoods). It is based on a low-cost, low-power wireless devices and ensures good accuracy compared to the state-of-the-art. We then propose a variant of LEMON called combinatorial localization which exhaustively searches for the best possible set of nearest neighbors. We further define a Bayesian network model for the same localization problem. The performance of these methods is evaluated through extensive experiments in various indoor areas. We found an interesting outcome that the simple KNN-based approach can offer better localization accuracy compared to other complex localization methods. Thus we further enhance the performance of the KNN-based approach using multiple RF channels.