A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide worldwide robust and accurate location based services (LBSs). The proposed Lighthouse landmarks have the nice properties of being abundant in space, hardware-and carry position-independent, can be computed efficiently from a single scan, are confined to a small area of space, do not restrict the user movement path, and do not require any calibration. We show that the positioning error of the Lighthouse landmarks is bounded and present the different extensions that allow it to handle practical situations including the noisy wireless channel, different AP transmit powers, obstacles in the environment, among others. We further present efficient algorithms for extracting them from WiFi scans. We have implemented and evaluated Lighthouse using thousands of surveys collected from different cities worldwide over a six months period. Our results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is less than 3.6 meters. This accuracy is robust over time, different phones hardware, phone carrying positions, and parameters configurations; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs on a worldwide scale.

A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide robust and accurate location based services (LBSs). Our experimental results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is 3.4 meters which is robust under different conditions; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs.

Pedestrian navigation is an important ingredient for efficient multimodal transportation, such as guidance within large transportation infrastructures. A requirement is accurate positioning of people in indoor multistory environments. To achieve this, maps of the environment play a very important role. FootSLAM is an algorithm based on the simultaneous localization and mapping (SLAM) principle that relies on human odometry, i.e., measurements of a pedestrian's steps, to build probabilistic maps of human motion for such environments and can be applied using crowdsourcing. In this paper, we extend FootSLAM to multistory buildings following a Bayesian derivation. Our approach employs a particle filter and partitions the map space into a grid of adjacent hexagonal prisms with eight faces. We model the vertical component of the odometry errors using an autoregressive integrated moving average (ARIMA) model and extend the geographic tree-based data structure that efficiently stores the probabilistic map, allowing real-time processing. We present the multistory FootSLAM maps that were created from three data sets collected in different buildings (one large office building and two university buildings). Hereby, the user was only carrying a single foot-mounted inertial measurement unit (IMU). We believe the resulting maps to be strong evidence of the robustness of FootSLAM. This paper raises the future possibility of crowdsourced indoor mapping and accurate navigation using other forms of human odometry, e.g., obtained with the low-cost and nonintrusive sensors of a handheld smartphone.

The avoidance of train collisions is vital for human safety in railway transportation. Technical approaches are general train control or collision avoidance systems as well as semi-automated or fully autonomous trains. These systems rely on robust and exact train localization as well as an accurate map of the track network.We present Simultaneous Localization and Mapping relying exclusively on train-side sensors. RailSLAM, implemented as a probabilistic filter, uses measurements from multiple sensors and computes a track map. We rely heavily on sensors that are not affected by the harsh environmental conditions often experienced in this application, in particular a low-cost MEMS Inertial Measurement Unit (IMU). Rail vehicle localization methods based on these sensors require a dedicated map with detailed geometric track features in combination with the topological track connections. If this feature map does not exist apriori, it needs to be created. If it does, it may suffer from incompleteness, insufficient accuracy or outdated information. RailSLAM addresses the creation and maintenance of this special track map by a simultaneous estimation of the probabilistic geometric-topological feature-rich track map and the train state. A first proof of concept implementation of mapping is given based on the use of an Extended Kalman Filter with measurements from Global Navigation Satellite System (GNSS) and an IMU.

Received signal strengths have been widely exploited in indoor positioning due to the massive presence of wireless local networks in buildings. Theoretical propagation models such as the path-loss model can be used in order to avoid long training phases as in fingerprinting approaches. The main issue concerning the employiment of the path-loss model is that the values of some parameters, i.e., the transmit power and the decay exponent, depend on many factors, such as the device, building structure and other environmental features. In this paper, we propose a Bayesian positioning algorithm based on the Rao-Blackwellized particle filter, where the parameters of the path-loss model are estimated independently for each AP in addition to localizing the user. Both parameters are described by discrete random variables with uniform priors. We validate ou proposal by means of simulations and two different experiments; finally, some remarks on complexity are also given.

Indoor positioning by means of received signal strengths has been gathering much interest since the massive presence of wireless local area networks (WLANs) in buildings. Theoretical approaches rely on the perfect knowledge of the APs' positions and propagation conditions; since this is unrealistic in real world, we estimate such knowledge as well as the building map from data by applying Simultaneous Localization and Mapping (SLAM). In this paper we address the joint estimation of the path loss parameters, namely the transmitted power and the path loss exponent, this latter being usually approximated in the literature by the free space value. We provide examples that show the relevance of estimating both parameters and analyze observability issues from the point of view of estimation theory. The integration of the parameter estimation in a WLAN based SLAM algorithm - WiSLAM - has been carried out and the results are discussed.

The use of foot-mounted inertial measurement units (IMUs) has shown promising results in providing accurate human odometry as a component of accurate indoor pedestrian navigation. The specifications of these sensors, such as the sampling frequency have to meet requirements related to human motion.

Heading information is a critical input to pedestrian dead reckoning. Unlike in most outdoor environments, the magnetic field inside of buildings is often strongly perturbed and inhomogeneous. Hence, straightforward approaches to use measurements of two-axis and three-axis magnetometers perform poorly. In recent measurements we have observed statistical properties of the magnetic field indicating that knowledge of the measured horizontal magnetic intensity is informative about the expected deviation of the measured magnetic heading. This statistical dependence has been quantified based on indoor measurements that have been collected in several offices and corridors in 3 buildings having different building orientations. A decrease in the spread of the horizonal angle is exhibited for larger horizontal intensities suggesting that measurements with large horizontal intensities are more reliable. We provide an approach to determine a likelihood function for the measured magnetic heading as a function of the local magnetic intensity in indoor environments. We show how the Expectation Maximization (EM) algorithm is used to construct a parametric two dimensional distribution of heading and planar-intensity, which can serve as a heading likelihood function in Bayesian positioning estimators, based upon which, greater weight is given to the less disturbed (strong-intensity) heading measurements and lower weight to the more erroneous ones (low-intensity). Drawing on our empirical data we show the performance improvement achieved with this new likelihood function.

We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.

The magnetic field in indoor environments is rich in features and exceptionally easy to sense. In conjunction with a suitable form of odometry, such as signals produced from inertial sensors or wheel encoders, a map of this field can be used to precisely localize a human or robot in an indoor environment. We show how the use of this field yields significant improvements in terms of localization accuracy for both legged and non-legged locomotion. We suggest various likelihood functions for sequential Monte Carlo localization and evaluate their performance based on magnetic maps of different resolutions. Specifically, we investigate the influence that measurement representation (e.g., intensity-based, vector-based) and map resolution have on localization accuracy, robustness, and complexity. Compared to other localization approaches (e.g., camera-based, LIDAR-based), there exist far fever privacy concerns when sensing the indoor environment's magnetic field. Furthermore, the required sensors are less costly, compact, and have a lower raw data rate and power consumption. The combination of technical and privacy-related advantages makes the use of the magnetic field a very viable solution to indoor navigation for both humans and robots.

To improve our understanding of the indoor properties of the perturbed Earth's magnetic field, we have developed a methodology to obtain dense and spatially referenced samples of the magnetic vector field on the ground's surface and in the free space above. This methodology draws on the use of various tracking techniques (photometric, odometric, and motion capture) to accurately determine the pose of the magnetic sensor, which can be positioned manually by humans or autonomously by robots to acquire densely gridded sample datasets. We show that the indoor magnetic field exhibits a fine-grained and persistent micro-structure of perturbations in terms of its direction and intensity. Instead of being a hindrance to indoor navigation, we believe that the variations of the three vector components are sufficiently expressive to form re-recognizable features based on which accurate localization is possible. We provide experimental results using our methodology to map the magnetic field on the ground's surface in our indoor research facilities. With the use of a magnetometer and very little computation, these resulting maps can serve to compensate the perturbations and subsequently determine pose of a human or robot in dead reckoning applications.

FootSLAM or simultaneous localization and mapping (SLAM) for pedestrians is a technique that addresses the indoor positioning and mapping problem based on human odometry (aka pedestrian dead reckoning), e.g. with a foot-mounted inertial sensor. FootSLAM follows the FastSLAM factorization, using a Rao-Blackwellized particle filter to simultaneously estimate the building layout and the pedestrian's pose - his position and orientation. To that end, FootSLAM divides the 2D space into a grid of uniform and adjacent hexagons and counts the number of times each particle crosses the edges of the hexagons it visits. As we shall show, the complexity of FootSLAM grows quadratically with time, preventing the mapping of large areas.

The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.

We address Simultaneous Localization and Mapping (SLAM) for pedestrians by means of WiFi signal strength measurements. In our system odometric data from foot mounted Inertial Measurements Units are fused with received signal strength (RSS) measurements of IEEE 802.11. To do this, we assign a probabilistic model to RSS measurements, and adopt the Bayesian framework on which FootSLAM and PlaceSLAM are based. Computational aspects are also accounted in order to provide a practical implementation of the algorithm. Simulative and experimental examples of WiSLAM are shown to underline the effectiveness of our proposal.

By incorporating known floor-plans in sequential Bayesian positioning estimators such as Particle Filters (PF), long term positioning accuracy can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians' motion. Instead of using binary decisions to eliminate particles when crossing a wall as several authors do, a maps-based angular probability density function (PDF) is used in this paper that is capable of weighting the possible headings of the pedestrian according to local infrastructure. We will show that the angular PDF will help to obtain better performance in critical multi-modal navigation scenarios.

The success of cooperative Intelligent Transportation Systems (ITS) applications such as collision avoidance or adaptive cruise control stands or falls with the exchange of information between distributed and usually moving nodes. The extensive transmission of information contrasts with a limited channel bandwidth which has to be shared between all nodes. Thus, a tradeoff is required which cooperatively selects pieces of information for dissemination according to their worth for the receivers under consideration of the communication channel conditions. The tradeoff is achieved by an entropy-based evaluation of evidence in a dynamic probabilistic filter system. With this approach the dissemination priority is based on the uncertainty reduction which can be achieved by the reception of a piece of evidence in contrast to a pure prediction. The novelty of the approach as presented in this paper is exceptional due to its information-centric evaluation of the worth of evidence which has not been performed so far for cooperative ITS applications. It outperforms current state of the art by its generic and theoretically grounded approach for diverse applications, its inclusion of measurement uncertainty, its context-adaptability and its optimized cooperative radio bandwidth utilization.

In this paper we present a reference data set that we are making publicly available to the indoor navigation community. This reference data is intended for the analysis and verification of algorithms based on foot mounted inertial sensors. Furthermore, we describe our data collection methodology that is applicable to the analysis of a broad range of indoor navigation approaches. We employ a high precision optical reference system that is traditionally being used in the film industry for human motion capturing and in applications such as analysis of human motion in sports and medical rehabilitation. The data set provides measurements from a six degrees of freedom foot mounted inertial MEMS sensor array, as well as synchronous high resolution data from the optical tracking system providing ground truth for location and orientation. We show the use of this reference data set by comparing the performance of algorithms for an essential part of pedestrian dead reckoning systems for positioning, namely identification of the rest phase during the human gait cycle.

In this paper we describe a new Bayesian estimation approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. When somebody walks within a constrained area such as a building, then even noisy and drift-prone odometry measurements can give us information about features like turns, doors, and walls, which we can use to build a form of a map of the explored area, especially when these features are revisited over time. Our initial results for our novel scheme which we call "FootSLAM" are very surprising in that true SLAM with stable relative positioning accuracy of 1-2 meters for pedestrians is indeed possible based on inertial sensors alone without any prior known building indoor layout. Furthermore, the 2D maps obtained even for just 10 minutes of walking converge to a good approximation of the true layout forming the basis for future automated collaborative mapping of buildings.

A sequential Bayesian estimation algorithm for joint positioning and multipath mitigation within satellite navigation receivers is presented. The underlying process model is especially designed for dynamic user scenarios and dynamic channel conditions. To demonstrate its capabilities simulation results are presented.

Current driver assistance systems merely use a minimum set of information. By using additional information of the environment hazardous situations can be detected earlier, more reliably and with a higher precision. This situational information has a significant impact not only on the hazard detection, but also on other modules such as the human-machine-interface or knowledge distribution between vehicles over suitable networks. For a number of reasons situational information is inherently subject to uncertainty. This may be handled by the use of dynamic Bayesian networks, which we suggest can be used to represent these dynamic complex systems. The objective of this paper is to provide an architecture for driver assistance systems that is aware of uncertain situational information in order to prevent accidents and reduce the number of traffic fatalities. It introduces the concepts of utility-based knowledge exchange based on the consideration of other partners' knowledge, the amount of inference that has taken place to identify a situation and current network conditions. Furthermore, we suggest a representation of hazard descriptions using Bayesian network fragments.

A historical perspective of turbo coding and turbo transceivers inspired by the generic turbo principles is provided, as it evolved from Shannon's visionary predictions. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannon's capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated space-time and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted.

We present a simple system to help people navigate inside of buildings or even in outside areas close to buildings. It is based on the "RSSI" and "transmit power" data of an established Bluetooth link. The system is in principle sufficient for the intended application (pedestrian, indoor), but it is certainly not a high resolution indoor location system. The achievable accuracy is dependent on the setup (number of access points and their constellation and available Bluetooth devices) but will not be better than in the order of several meters. A main feature of this fingerprinting-like system is the representation of the estimated position in the form of probability density functions.

In this contribution we present an exhaustive treatment of various coding and decoding techniques for use in fast frequency-hopping/multiple frequency shift keying multiple-access systems. One of the main goals is to show how reliability information on each received bit can be derived to enable soft-decision decoding. Convolutional codes as well as turbo codes are considered applying soft-decision, erasure, and hard-decision decoding. Their performance is compared to that of previously proposed Reed-Solomon with either errors-only or errors-and-erasures decoding. A mobile radio environment yielding a frequency-selective fading channel is assumed. It is shown that the application of turbo codes and convolutional codes with soft decision decoding can allow for a comparable number of simultaneously transmitting users to Reed-Solomon codes with errors-and-erasures decoding. Furthermore, the advantage of soft decisions is shown, which can be applied to a widely and growing range of channel codes. The pertinent technique of calculating soft decisions is described in the paper.

We present the architecture for a four chip integrated receiver decoder compliant with the European Digital Terrestrial Video Transmission standard (DVB-T); both 8 K and 2 K FFT size are supported. The paper focuses on the algorithms used for I/Q signal generation, OFDM demultiplexing, channel estimation, synchronization and channel decoding. The algorithms chosen are robust against the channel and interference conditions to be expected, including Rayleigh fading, co-channel interference from other (e.g. existing analogue) services and tuner imperfections. The channel SNR degradation arising in the digital part of the receiver-with imperfect channel estimation, synchronization and quantization-with respect to the BER at the output of the Viterbi decoder is not more than 2 dB from theory.

We present a bandwidth-efficient channel coding scheme that has an overall structure similar to binary turbo codes, but employs trellis-coded modulation (TCM) codes (including multidimensional codes) as component codes. The combination of turbo codes with powerful bandwidth-efficient component codes leads to a straightforward encoder structure, and allows iterative decoding in analogy to the binary turbo decoder. However, certain special conditions may need to be met at the encoder, and the iterative decoder needs to be adapted to the decoding of the component TCM codes. The scheme has been investigated for 8-PSK, 16-QAM, and 64-QAM modulation schemes with varying overall bandwidth efficiencies. A simple code choice based on the minimal distance of the punctured component code has also been performed. The interset distances of the partitioning tree can be used to fix the number of coded and uncoded bits. We derive the symbol-by-symbol MAP component decoder operating in the log domain, and apply methods of reducing decoder complexity. Simulation results are presented and compare the scheme with traditional TCM as well as turbo codes with Gray mapping. The results show that the novel scheme is very powerful, yet of modest complexity since simple component codes are used.

The potential of pilot-symbol-aided channel estimation in two dimensions are explored. In order to procure this goal, the discrete shift-variant 2-D Wiener filter is derived and analyzed given an arbitrary sampling grid, an arbitrary (but possibly optimized) selection of observations, and the possibility of model mismatch. Filtering in two dimensions is revealed to outperform filtering in just one dimension with respect to overhead and mean-square error performance. However, two cascaded orthogonal 1-D filters are simpler to implement and shown to be virtually as good as true 2-D filters.

We extend turbo trellis coded modulation (TTCM) with punctured component codes to allow higher bandwidth efficiency. Two techniques, that can also be combined, were investigated that achieve this: the inclusion of uncoded bits in TTCM in conjunction with high order modulation (e.g. 64-QAM), and the replacement of two dimensional component codes (e.g. Ungerboeck codes) with multi-dimensional codes. In the first approach, the inter-set distance of the partitioning tree can be used to fix the number of coded and uncoded bits, which will strongly determine the complexity of the decoder. Simulation results are presented for 8-PSK with 2.5 bits per symbol and 64-QAM with 5 bits per symbol; they indicate that the scheme can achieve good bit error rates close to Shannon's limit.