Mohammed Khider

German Aerospace Center (DLR), Köln, North Rhine-Westphalia, Germany

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Publications (34)3.01 Total impact

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    ABSTRACT: Maps representing aspects of an environment that affect pedestrian motion can be very informative sources of data in indoor localization. Their proper representation and usage is mandatory to fully leverage their potential. In this chapter, we show how probabilistic representations facilitate accuracy and availability of position estimates even in the absence of usable satellite navigation signals or similar forms of localization signals. We will show that maps may effectively substitute infrastructure, such as active or passive (RFID-type) radio beacons when their information is properly used in combination with dynamic models of movement and some form of motion estimate such as pedestrian dead reckoning. This chapter aims at illuminating the details of how to generate, represent and use probabilistic maps for indoor localization. While this discussion applies to a wide range of sensors, we will focus on showing how maps are essential in achieving long-term stability in combination with inertial sensors. We begin by motivating why the use of a probabilistic map of human motion is a natural way of incorporating building information into a sequential Bayesian filtering framework. This stands in contrast to the often used ad-hoc solutions which is to use a floor plan as a “kill or live” weighting function in a particle filter (PF), driven by some form of pedestrian dead reckoning (PDR) such as foot mounted inertial sensors based PDR. We show how the latter method can fail catastrophically and how a probabilistic map formulation addresses these problems. We present a number of ways of how to obtain such maps for real world applications. The first is based on knowledge of the building layout and applies a diffusion algorithm to compute an estimate of the probability distribution of the motion direction of a pedestrian at each point in the building. Secondly, we compare these maps with those obtained using Simultaneous Localization and Mapping (SLAM) by applying FootSLAM that requires no sensors other than a source of dead reckoning. The map concept can be further extended in order to include features that are relevant to radio-based localization techniques, like transmitter positions and a model for radio propagation or, eventually, a database of fingerprints. The influence of the different kind of maps on positioning accuracy is discussed in detail and the maps are compared to each other by means of metrics derived from information theory.
    Indoor Wayfinding and Navigation, 02/2015;
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    Luigi Bruno, Mohammed Khider, Patrick Robertson
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    ABSTRACT: Received signal strengths have been widely used in indoor positioning due to the massive presence of wireless local networks in buildings. To avoid long training phases theoretical propagation models such as the path-loss model can be used. The main issue is that the path-loss parameters, namely the transmitted power and the decay exponent, can assume a wide range of values, depending on devices, building structure and other environmental features. In this paper, we propose a Bayesian positioning algorithm based on the Rao-Blackwellized particle filter, where the system estimates the parameters of the path-loss model independently for each AP in addition to localizing the user. Both parameters are described by discrete variables and their probability distribution is estimated starting from a uniform prior. We validate the algorithm with simulations and two different experiments; finally, some remarks on complexity are also given.
    International Conference on Indoor Positioning and Indoor Navigation - IPIN 2013; 10/2013
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    ABSTRACT: 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. We investigate the lowest usable sampling frequency: To do so, we evaluate the frequency distribution of different human motion like crawling, jumping or walking in different scenarios such as escalators, lifts, on carpet or grass, and with different footwear. These measurements indicate that certain movement patterns, as for instance going downstairs, upstairs, running or jumping contain more high frequency components. When using only a low sampling rate this high frequency information is lost. Hence, it is important to identify the lowest usable sampling frequency and sample with it if possible. We have made a set of walks to illustrate the resulting odometries at different frequencies, after applying an Unscented Kalman Filter (UKF) using Zero Velocity Updates. The odometry error is highly dependent on the drift of the individual accelerometers and gyroscopes. In order to obtain better odometry it is necessary to perform a detailed analysis of the sensor noise processes. We resorted to computing the Allan variance for three different IMU chipsets of various quality specification. From this we have derived a bias model for the UKF and evaluated the benefit of applying this model to a set of real data from walk.
    10/2013;
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    ABSTRACT: 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.
    IPIN 2013; 10/2013
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    ABSTRACT: 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.
    Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013); 10/2013
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    ABSTRACT: Pedestrian navigation in challenging areas where Global Navigation Satellite Systems (GNSSs) lack the required accuracy and availability such as indoor and urban canyons is becoming increasingly important. Multisensor navigation is one of the techniques that has shown promising positioning accuracy in such environments. Being able to appropriately fuse GNSS pseudoranges in a multisensor positioning system is advantageous as it allows to make use of fewer than four detected satellites signals and to incorporate more accurate estimation models. In this paper a Particle Filter based multisensor pedestrian positioning system is enhanced to use pseudorange measurements of a GNSS receiver instead of direct position solutions. Using correctly modeled pseudoranges, a single visible satellite over a certain time period might improve positioning accuracy and robustness when combined with measurements from other positioning sensors like a foot mounted inertial measurement unit. Additionally in this paper, a statistical multipath error model for pseudoranges is integrated. Promising results have been achieved in a challenging indoor environment where blockage of satellites' signals and multipath are common issues.
    IET Radar Sonar ? Navigation 09/2013; 7(2013-09-26-8):881-884. · 0.92 Impact Factor
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    ABSTRACT: Pedestrian navigation in challenging areas where global navigation satellite systems (GNSSs) lack the required accuracy and availability, such as indoor and urban canyons, is becoming increasingly important. Multisensor navigation is one of the techniques that has shown promising positioning accuracy in such environments. Being able to appropriately fuse GNSS pseudoranges in a multisensor positioning system is advantageous as it allows us to make use of fewer than four detected satellite signals and to incorporate more accurate estimation models. In this study, a particle filter-based pedestrian positioning system is enhanced to use pseudorange measurements of a GNSS receiver instead of direct position solutions. By using correctly modelled pseudoranges, a single satellite visible over a certain time period might improve positioning accuracy and robustness when combined with measurements from other positioning sensors, like a foot-mounted inertial measurement unit. Additionally in this study, a statistical multipath error model for pseudoranges is integrated. Promising results have been achieved in a challenging indoor environment where blockage of satellites' signals and multipath are common issues.
    IET Radar Sonar ? Navigation 01/2013; 7(8):881-894. · 0.92 Impact Factor
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    ABSTRACT: Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban canyons and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Often, positioning with GNSSs is impossible in these scenarios. On the other hand, cellular wireless communication systems such as the third generation partnership project (3GPP) long-term evolution (LTE) provide excellent coverage in urban and most indoor environments. Thus, this paper researches timing based positioning algorithms, in this case time difference of arrival (TDoA), using 3GPP-LTE and GPS measurements. This paper considers a particle filter for 3GPP-LTE TDoA positioning and the fusion of 3GPP-LTE signals with GPS measurements. To obtain better positioning results, a 3GPP-LTE TDoA error model is derived, which splits the TDoA errors in slow varying and fast varying errors. The slow varying error model is included in the prediction model and the fast varying error model in the likelihood function of the particle filter. The last part of this paper, evaluates the positioning performances of the developed particle filter in an indoor scenario. These evaluations show clearly the possibility of using 3GPP-LTE measurements for indoor positioning. Additionally, it shows the advantage of fusing 3GPP-LTE with GPS measurements.
    Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012); 09/2012
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    ABSTRACT: The Extended Kalman Filter (EKF) has been the state of the art in Pedestrian Dead-Reckoning for foot-mounted Inertial Measurements Units. However due to the non-linearity in the propagation of the orientation the EKF is not the optimal Bayesian filter. We propose the usage of the Unscented Kalman Filter (UKF) as the integration algorithm for the inertial measurements. The UKF improves the mean and covariance propagation needed for the Kalman filter. Although the UKF provides a better estimate of the orientation, with Zero velocity UPdaTes (ZUPT) measurements, the yaw and the bias in the gyroscope associated with it becomes unobserved and might generate errors in the positioning. We studied the changes in the magnetic field during the stance phase and their relationship with the turn rates to propose three measurements using the magnetometer signal that will be called Magnetic Angular Rate Updates (MARUs). The first measurement uses the change in the angle of the magnetic field in the horizontal plane to measure the change in the yaw and provides a simple measurement for the UKF implementation. The second measurement relates the change in the magnetic field vector to the turn rate and provides information on the bias of the gyroscope for an UKF. The last measurement uses a first order approximation to generate a linear relationship with the gyroscope bias and therefore it can be used in an EKF. Finally we proposed a metric for the reliability of the stance as a way to use the pre and post stance information but adjusting the covariance of the measurements gradually from swing to stance. These methods were tested on real and simulated signals and they have shown improvements over the original PDR algorithms.
    Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION; 04/2012
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    Mohammed Khider, Susanna Kaiser, Robertson Patrick
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    ABSTRACT: In this paper, a Three Dimensional Pedestrian Movement Model (3D-MM) capable of probabilistically representing pedestrian movement in challenging indoor and outdoor localization environments is developed, implemented and evaluated. In the scope of this paper, the model is used to generate a ‘movement’ or a transition for dynamic positioning systems that are based on sequential Bayesian filtering techniques, such as particle filtering. It can also be used to assign weights for particles' movements proposed by sensors in Likelihood Particle Filters implementations. Alternatively, the developed model can be applied to other applications domains such as infrastructure design, evacuation planning, robot-human interaction and pervasive computing. The novelty of the model is in its ability to characterize both random and goals-oriented pedestrian motions and additionally use the a priori knowledge of maps and floor plans. It will be shown that an appropriate pedestrian movement model not only improves the positioning accuracy, but is also essential for a robust positioning estimator. Additionally, this work shows that maps and floor plans can improve pedestrian movement models but do not replace them, as several authors suggest.
    Journal of Navigation 04/2012; 65(2012-03-13-2):245-264. · 0.62 Impact Factor
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    ABSTRACT: Services and applications based on accurate knowledge of the mobile terminal (MT) location play a fundamental role in current and future wireless communications systems. In addition, the United States Federal Communications Commission (FCC) has stated accuracy requirements on the location determination process of enhanced 911 (E-911) emergency callers. Global navigation satellites systems (GNSSs) based positioning provides a sufficient accuracy in rural and suburban environments, where a sufficient number of satellites are visible in line-of-sight conditions. However, in GNSS critical environments, such as dense urban, urban canyon or even indoors, view to sky is limited. In these environments, low signal power, bad satellite constellations, severe multipath and non-line-of-sight propagation causes erroneous and biased position estimates. Especially in these environments, cellular wireless communication systems provide good coverage and can be used for position determination of the MT. Mobile radio communications systems like GSM, UMTS or the currently deployed 3GPP-LTE primarily target on optimizing communication performance figures such as bandwidth efficiency or data throughput. Availability, signal strength or even signal bandwidths, however, make them interesting for positioning. However, the correlation properties of the synchronization signals of the 3GPP LTE system limit the positioning performances. Moreover, while non-line-of-sight propagation improves the communication performance, it degrades the navigation performance due to the additional distance the signal might travel. Hence, this paper shows an indoor positioning approach with the 3GPP-LTE mobile communication standard, which is currently deployed in many countries. Moreover, it shows the benefit of using the 3GPP-LTE mobile communication system for indoor positioning. Therefore, this paper describes a novel real-time mobile radio based positioning system using time-difference-of-arrival (TDOA) measurements. The paper considers an indoor scenario, where the transmitters are located outdoors and the MT is moving in an office building. The position estimation is done by a particle filter. Furthermore, to improve the positioning accuracy, this paper derives a time-variant error model for indoor positioning. Using this time-variant error model, the positioning error of the MT can be decreased significantly. The downlink of 3GPP-LTE is based on Orthogonal Frequency Division Multiplexing (OFDM), which allows a spectral efficient and flexible usage of the available frequency spectrum. The 3GPP-LTE standard specifies signal parts, dedicated to time and frequency synchronization. For our investigations we use these synchronization signals for TDOA based position estimation. The 3GPP-LTE signal structure, in particular the properties of the synchronization properties, will be discussed in detail in the final paper. For the 3GPP-LTE positioning we apply a particle filter in order to process TDOA measurements of the 3GPP-LTE base stations. The TDOA measurements are taken from DLR´s 3GPP-LTE positioning test-bed. The test-bed consists of up to four synchronous transmitters. From each transmitter site a predefined 3GPP-LTE OFDM frame can be transmitted periodically. The test-bed operates at 2.4 - 2.5 GHz, providing signal bandwidths of up to 20 MHz. At the receiver we convert the received signal down to base band and sample both the inphase and quadrature component. The sampled signal is stored on a hard disk, which allows both offline and real-time processing. These sampled data are processed by the TDOA estimation algorithm and consists of two steps. The first step estimates the TDOA roughly by correlating the narrow band synchronization signals with the received sequence and the second step performs subsample based estimation with the wideband pilot symbols. These algorithms use a first peak and maximum peak detection algorithms, for detecting the first and maximum arriving signal. However, the 20 MHz bandwidth limits the rough synchronization and allows only a sample based estimation within 15 meters, which hinders accurate positioning in indoor environments. Thus, an oversampling approach is used, to tackle this issue which results in a significant error reduction. However, due to hardware imperfections of the test-bed and channel errors, such as non-line-of-sight and multipath propagation, the TDOA measurements are noisy and biased. Thus, to obtain better positioning results, this bias has to be predicted and mitigated. Several approaches exist to model time of arrival based ranging in indoor environments. All of these statistical models depend on bandwidth, carrier frequency and are time-invariant. However, for navigation applications an evaluation of the multipath and non-line-of-sight error for a moving receiver is essential. Hence, to improve the positioning accuracy, we derive in this paper a time-variant TDOA error model based on a measurement campaign of an outdoor-to-indoor channel. The evaluation of this measurement campaign yields an autoregressive error model which allows us to predict the TDOA error for a moving MT. By using this model within the particle filter yields promising results in terms of error mitigation. Each particle itself models the multipath and non-line-of-sight error according to the obtained time-variant error model. Additionally, we compare these results to the more general approach by assuming an uncorrelated error. In the final paper, we will provide a detailed description of the 3GPP-LTE downlink signal structure, which we use for TDOA based positioning. We will discuss and describe the applied algorithms for timing (pseudo-range) estimation. Furthermore, we will describe the 3GPP-LTE test-bed and the scenario in detail. Additionally, the particle filter used for the positioning estimation will be described in detail. Especially, we will analyze the measurement results and the derived time-variant model to predict and mitigate the multipath and non-line-of-sight error in the particle filter.
    IEEE/ION PLANS 2012; 01/2012
  • Susanna Kaiser, Mohammed Khider, Patrick Robertson
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    ABSTRACT: 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 is often the case in the state-of-the art, a map-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. In addition, we will include outdoor maps by processing satellite images of the region. We will show that the angular PDF will help to obtain better performance in critical multi-modal navigation scenarios and in the outdoor area when including maps.
    Journal of Location Based Services 01/2012;
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    Susanna Kaiser, Mohammed Khider, Patrick Robertson
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    ABSTRACT: Foot mounted indoor positioning systems work remarkably well when using additionally the knowledge of floor-plans in the localization algorithm. Walls and other structures naturally restrict the motion of pedestrians. No pedestrian can walk through walls or jump from one floor to another floor when considering a building with different floor-levels. By incorporating known floor-plans in sequential Bayesian estimation processes such as Particle Filters (PF), long term error stability can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians’ motion. In this paper a new motion model based on maps and floor-plans is introduced that is capable of weighting the possible headings of the pedestrian as a function of the local environment. The motion model is derived from a diffusion algorithm that makes use of the principle of a source effusing gas and is used in the weighting step of a PF implementation. The diffusion algorithm is capable of including floor-plans as well as maps with areas of different degrees of accessibility. The motion model more effectively represents the probability density function of possible headings that are restricted by maps and floor-plans than a simple binary weighting of particles (i.e. eliminating those that crossed walls and keeping the rest). We will show that the motion model will help to obtain better performance in critical navigation scenarios where two or more modes may be competing for some of the time (multi-modal scenarios).
    EURASIP Journal on Wireless Communications and Networking 08/2011; · 0.54 Impact Factor
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    Susanna Kaiser, Mohammed Khider, Patrick Robertson
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    ABSTRACT: 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.
    IPIN 2011; 01/2011
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    ABSTRACT: 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.
    Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on; 10/2010
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    ABSTRACT: Indoor and urban canyons are application areas that are becoming increasingly important for navigation application. However, achieving the required accuracy and availability is still a challenge. Multisensor navigation is one of the techniques that has shown promising results in addressing the challenges of such areas. Being able to incorporate raw low-level sensor data is advantageous as it allows to incorporate all available information and more accurate estimation models. In this paper a Particle Filter based multisensor positioning system is extended to use pseudorange measurements of a GPS sensor instead of a calculated position solution. Using pseudoranges, any number of visible satellites can improve positioning accuracy, when combined with measurements from other sensors like an electronic compass, a barometric altimeter or a foot mounted inertial measurement unit (IMU). Additionally, statistical error models for pseudoranges are integrated and tested. Our results show that by using the models within the Bayesian framework yields promising results in terms of error mitigation.
    Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION; 06/2010
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    ABSTRACT: In this paper we present an extension to odometry based SLAM for pedestrians that incorporates human-reported measurements of recognizable features, or “places” in an environment. The method which we have called “PlaceSLAM” builds on the Simultaneous Localization and Mapping (SLAM) principle in that a spatial representation of such places can be built up during the localization process. We see an important application to be in mapping of new areas by volunteering pedestrians themselves, in particular to improve the accuracy of “FootSLAM” which is based on human step estimation (odometry). We present a description of various flavors of PlaceSLAM and derive a Bayesian formulation and particle filtering implementation for the most general variant. In particular we distinguish between two important cases which depend on whether the pedestrian is required to report a place's identifier or not. Our results based on experimental data show that our approach can significantly improve the accuracy and stability of FootSLAM and this with very little additional complexity. After mapping has been performed, users of such improved FootSLAM maps need not report places themselves.
    Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION; 06/2010
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    ABSTRACT: The accuracy provided by a Global Navigation Satellite System (GNSS) is sufficient for many outdoor applications, but it strongly suffers in indoor and urban canyon environments from multipath and signal blockage. In consequence, the quality of location based services is often poor indoors. One of the successful approaches to tackle this problem is to combine GNSS position outputs with other sensors in order to increase the accuracy, availability and integrity of GNSS positioning. Examples of such sensors are inertial sensors, compasses, mobile phone networks (TOA, AOA and Cell ID), WLAN and RFIDs. Maps and floor-plans can provide additional information that improves navigation. In this paper a Sequential Bayesian Estimator that performs enhanced fusion of heterogeneous sensors and pedestrian mobility models will be presented. Additionally, the performance of an implementation of this estimator will be evaluated. In order to utilize GPS in indoor and urban canyons, where often less than 4 satellites are visible it is advantageous to use individual pseudoranges in the fusion process, instead of the receiver´s estimated position. To facilitate this, the following steps are carried out: - Raw pseudo-ranges and other satellite parameters are extracted from the receiver. - Standard ionospheric, tropospheric, satellite and user clock error models are implemented. - Correction models for the remaining (un-modeled) errors are built. Namely fast and slow errors. - Spatial and temporal correlation multipath error model is additionally implemented. Sequential Bayesian positioning estimators are used widely to combine such sensors due to their ability to include the dynamics of the pedestrian " movement models" and additionally, propagate the estimates using probabilistic representations. With such probabilistic representations different sensors can be represented with their appropriate accuracy. This representation is the basis for optimally combining measurements of heterogeneous sensors. Several other sensors are included in the framework. In this paper, various combinations of foot mounted inertial sensors, compass, altimeter, floor-plans with GNSS and the resulting accuracy are investigated quantitatively. Mobility models: The prediction stage of sequential Bayesian positioning estimators depends entirely on the movement model to determine the probability density function of the pedestrian´s location and motion at each time step. A movement model that accurately represents the pedestrian´s motion ensures that measurement data used for positioning is consistent with how a pedestrian might move. The three-dimensional pedestrian movement model presented in [1] is used. Its incorporation of the knowledge of maps and floor-plans is one of the advantages of this model. Fusion algorithm: The proposed sensor fusion scheme is based on a cascaded estimation architecture. At a lower level an extended Kalman filter is used for the foot mounted inertial sensor to estimate the step-wise change of position and direction of one or optionally both feet respectively. These estimates are combined in turn as measurements in an upper Rao-Blackwellised particle filter with the measurements of the other sensors. The two levels are needed since the inertial sensor provides output in much higher data rate than the other sensors. A Rao-Blackwellised particle filter is used in order to reduce the complexity resulting from the increase of the number of states when working on the pseudo-range level. Examples of these states are fast errors, slow errors and multipath errors for each visible satellite. This is in addition to user clock, user position, speed, altitude and direction. Performance Analysis: Quantitative and qualitative analysis of the performance of the framework was carried out. Several ground truth were carefully measured to the centimeter accuracy indoor and outdoor in our office environment. Several indoor/outdoor walks were done were the error between the estimated position and the ground truth points were calculated when passing by each of them. Some results of the estimation error during these walks will be shown.
    IEEE/ION PLANS 2010; 05/2010
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    ABSTRACT: Imagine walking into a strange place — a shopping mall or an office building — wandering around for 20 minutes or so, and then coming out with a map of the facility that others could use to navigate through it — say, a fire rescue crew or simply someone looking for an office suite. A team of researchers at the German Aerospace Center are working on a “walk-about” solution that uses GPS to initialize the beginning of the traverse and tie the position data to an absolute coordinate frame. Then, foot-mounted inertial sensors increase the map accuracy when a person revisits previous points in the building.
    Inside GNSS. 05/2010; 5(2010-05-3):48-59.
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    ABSTRACT: This paper discusses our ongoing work on a system for collecting, managing and distributing relevant information in disaster relief operations. It describes the background and conditions under which the system is being developed and employed. We present our methodology, the requirements and current functionality of the system and the lessons learned in exercises and training, involving a large number of international disaster management experts. We found that the viability of this kind of tool is determined by three main factors, namely reliability, usability and frugality. The system has gone through many prototype iterations and has matured towards becoming operational in a specific type of mission, i.e. assessment missions for large scale natural and man-made disasters. This paper aims at making a wider audience of disaster management experts aware of that system and the support it may provide to their work. Other researchers and developers may find our experience useful for creating systems in similar domains.
    ISCRAM 2010; 05/2010