Papers by Mohammed Khider
Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Sep 25, 2009
He is currently with DLR and a PhD student at the University of Ulm. His research interests are n... more He is currently with DLR and a PhD student at the University of Ulm. His research interests are navigation, multi-sensors fusion, mobility models, signal processing and context aware services. Susanna Kaiser, received a Ph.D. in the field of error concealment and iterative decoding for MPEG-2 at the Technical University of Munich in 1998. She is currently with DLR, where her present research interests are communication and navigation, mobility models, and sensor based context aware systems. Patrick Robertson, received a Ph.D. from the University of the Federal Armed Forces, Munich, in 1995. He is currently with DLR, where his research interests are navigation, sensor based context aware systems, signal processing, and novel systems and services in various mobile and ubiquitous computing contexts. Michael Angermann, received a Ph.D. from the University of Ulm in 2004. He is currently with DLR, where his research interests are advanced and integrated communication and navigation systems.
Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimu... more Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Often, a position with GNSS is impossible in these scenarios. On the other hand, cellular wireless communication systems such as the third
Proceedings of the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2012), Sep 21, 2012
ABSTRACT Global navigation satellite systems (GNSSs) can deliver very good position estimates und... more 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.
A rapidly growing market for pedestrian location-based services has developed in recent years. Of... more A rapidly growing market for pedestrian location-based services has developed in recent years. Offering the pedestrian the right service, at the right time and in the right place requires accurate knowledge of their position. Global navigation satellite systems (GNSSs) - the best known type of positioning system - fail to provide accurate positioning in indoor and urban canyon environments due to multipath propagation and signal blockage. A substantial quantity of work has recently been carried out in developing positioning approaches that are reliable in all environments. As all single-sensor positioning systems fail, multisensor positioning - where information from two or more positioning sources is combined - represents the state-of-the-art solution. Bayesian positioning algorithms have shown promising results in optimally combining information from different positioning sources. The goal of this work is the development of an optimal pedestrian position estimator able to provide sufficient accuracy and availability in both indoor and outdoor environments. To this end, the use of GNSSs in multisensor positioning approaches has been enhanced through appropriately combining satellite-to-user range measurements with human odometry and position information from other sources. Using satellite-to-user range measurements instead of GNSS receiver position solutions reduces the number of satellite signals required. Moreover, it allows the incorporation of range measurement error models. With the aim of developing an optimal position estimator, two novel pedestrian movement models able to realistically represent the stochastic nature of pedestrian movement have been developed. Incorporating such movement models into Bayesian position estimators is beneficial as they allow pedestrian position and direction in the event of measurement unavailability to be predicted, and moreover help filter erroneous sensor outputs. An optimal Bayesian position estimator has been developed incorporating state-of-the-art fusion algorithms, the movement models developed, appropriately modeled satellite-to-user range measurements, human odometries and other position-related measurements.
ISCRAM, May 1, 2010
This paper discusses our ongoing work on a system for collecting, managing and distributing relev... more 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.
Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Sep 25, 2009
Biography Patrick Robertson received a Ph.D. from the University of the Federal Armed Forces, Mun... more Biography Patrick Robertson received a Ph.D. from the University of the Federal Armed Forces, Munich, in 1995. He is currently with DLR, where his research interests are navigation, sensor based context aware systems, signal processing, and novel systems and services in various mobile and ubiquitous computing contexts. Michael Angermann received a Ph.D. from the University of Ulm in 2004. He is currently with DLR, where his research interests are advanced and integrated communication and navigation systems.
Proceedings of the Satellite Division's International Technical Meeting, Oct 28, 2020
Raw GNSS measurement APIs (Application Programming Interfaces) in the Android system are a bridge... more Raw GNSS measurement APIs (Application Programming Interfaces) in the Android system are a bridge between the advances of positioning techniques and the applications of positioning for billions of Android users in the mass market. The publication trend since the debut of these APIs in 2016 has shown an increasing attention from both academia and industry. Among all GNSS topics, high precision positioning on smartphones may be the most attractive one, not just because it spawns over 1/3 of publications, but more importantly, it may trigger a plethora of possible novel applications, such as lane-level driving, mapping, and precise augmented reality. To facilitate this process, in this paper, Google releases 60+ datasets collected from phones in the Android GPS team, together with corrections from SwiftNavigation Inc. and Verizon Inc. These datasets were collected on highways in the US San Francisco Bay Area in the summer of 2020. They will be released at g.co/gnsstools by November 2020. Ground truth files collected by a NovAtel SPAN system will also be provided in NMEA file format. Furthermore, Google also provides the source codes in Matlab to evaluate the positioning performance. This paper provides details of these datasets, corrections, ground truth files, and utilities.
CRC Press eBooks, Mar 25, 2015
ABSTRACT Maps representing aspects of an environment that affect pedestrian motion can be very in... more 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.
To develop and demonstrate accurate indoor pedestrian navigation, we implemented a flexible locat... more To develop and demonstrate accurate indoor pedestrian navigation, we implemented a flexible location framework which is able to use various sensors as positioning sources. In the current setup, the main positioning data is derived from an active long range RFID system which collects RSS values from various RFID tags in the environment. The position is calculated using particle filtering algorithms. The demonstration shows the real time tracking of a person on a remote visualization screen.
Proceedings of the Satellite Division's International Technical Meeting, Oct 20, 2022
ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation
Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), 2020
Raw GNSS measurement APIs (Application Programming Interfaces) in the Android system are a bridge... more Raw GNSS measurement APIs (Application Programming Interfaces) in the Android system are a bridge between the advances of positioning techniques and the applications of positioning for billions of Android users in the mass market. The publication trend since the debut of these APIs in 2016 has shown an increasing attention from both academia and industry. Among all GNSS topics, high precision positioning on smartphones may be the most attractive one, not just because it spawns over 1/3 of publications, but more importantly, it may trigger a plethora of possible novel applications, such as lane-level driving, mapping, and precise augmented reality. To facilitate this process, in this paper, Google releases 60+ datasets collected from phones in the Android GPS team, together with corrections from SwiftNavigation Inc. and Verizon Inc. These datasets were collected on highways in the US San Francisco Bay Area in the summer of 2020. They will be released at g.co/gnsstools by November 2020. Ground truth files collected by a NovAtel SPAN system will also be provided in NMEA file format. Furthermore, Google also provides the source codes in Matlab to evaluate the positioning performance. This paper provides details of these datasets, corrections, ground truth files, and utilities.
A rapidly growing market for pedestrian location-based services has developed in recent years. Of... more A rapidly growing market for pedestrian location-based services has developed in recent years. Offering the pedestrian the right service, at the right time and in the right place requires accurate knowledge of their position. Global navigation satellite systems (GNSSs) - the best known type of positioning system - fail to provide accurate positioning in indoor and urban canyon environments due to multipath propagation and signal blockage. A substantial quantity of work has recently been carried out in developing positioning approaches that are reliable in all environments. As all single-sensor positioning systems fail, multisensor positioning - where information from two or more positioning sources is combined - represents the state-of-the-art solution. Bayesian positioning algorithms have shown promising results in optimally combining information from different positioning sources. The goal of this work is the development of an optimal pedestrian position estimator able to provide sufficient accuracy and availability in both indoor and outdoor environments. To this end, the use of GNSSs in multisensor positioning approaches has been enhanced through appropriately combining satellite-to-user range measurements with human odometry and position information from other sources. Using satellite-to-user range measurements instead of GNSS receiver position solutions reduces the number of satellite signals required. Moreover, it allows the incorporation of range measurement error models. With the aim of developing an optimal position estimator, two novel pedestrian movement models able to realistically represent the stochastic nature of pedestrian movement have been developed. Incorporating such movement models into Bayesian position estimators is beneficial as they allow pedestrian position and direction in the event of measurement unavailability to be predicted, and moreover help filter erroneous sensor outputs. An optimal Bayesian position estimator has been developed incorporating state-of-the-art fusion algorithms, the movement models developed, appropriately modeled satellite-to-user range measurements, human odometries and other position-related measurements.
ABSTRACT Global navigation satellite systems (GNSSs) can deliver very good position estimates und... more 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 2012 IEEE/ION Position, Location and Navigation Symposium, 2012
Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimu... more Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Often, a position with GNSS is impossible in these scenarios. On the other hand, cellular wireless communication systems such as the third
Page 1. The Effect of Maps-Enhanced Novel Movement Models on Pedestrian Navigation Performance Mo... more Page 1. The Effect of Maps-Enhanced Novel Movement Models on Pedestrian Navigation Performance Mohammed Khider, Susanna Kaiser, Patrick Robertson, Michael Angermann, German Aerospace Center (DLR), Germany BIOGRAPHY ...
Uploads
Papers by Mohammed Khider