Markus Ulmschneider

Multipath assisted positioning for pedestrians using LTE signals

The rapid growth of available services depending on location awareness has led to a more and more increasing demand for positioning in challenging environments. Global navigation satellite system (GNSS) based positioning methods may fail or show weak performance in indoor and urban scenarios due to blocking of the signals and multipath propagation. In contrast, cellular radio signals provide better reception in these scenarios due to a much higher transmit power. Also, they offer high coverage in most urban areas. However, they also undergo multipath propagation, which deteriorates the positioning performance. In addition, there are often only one or two base stations within communication range of the user. Both of these problems can be solved by means of a multipath-assisted positioning approach. The idea is to exploit multipath components (MPCs) arriving at the receiver via multiple paths due to scattering or reflections. Such approaches highly depend on the ability to resolve the MPCs at the receiver. This is why multipath-assisted positioning schemes typically assume ultra-wideband systems. Todays cellular radio systems work with much smaller bandwidths, though. The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard uses bandwidths up to 20 MHz. The aim of this paper is to show by means of measurements that multipath-assisted positioning is possible using 3GPP-LTE signals with only two base stations. We apply an advanced signal processing algorithm to track MPCs arriving at the mobile terminal, and to estimate the position of the mobile terminal. Since each of the MPCs can be regarded as being sent from some physical or virtual transmitter, we estimate the positions of transmitters in addition. Assuming only the starting position and direction of the mobile terminal to be known, the results show that the root mean square positioning error of the mobile terminal is always below 1.8 meters. In 90% of the cases, it is below 1.25 meters.

Simultaneous localization and mapping in multipath environments

This paper presents and extends the idea of multipath assisted positioning, named Channel-SLAM. Channel-SLAM uses multipath propagation to allow positioning for an insufficient number of transmitters or increase the accuracy otherwise. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) which are time synchronized to the physical transmitter and fixed in their position. To use the information of the MPCs, Channel-SLAM estimates the receiver position and the position of the VTs simultaneously using simultaneous localization and mapping (SLAM) and does not require any prior information such as room-layout or a database for fingerprinting. This paper investigates mapping, where we derive a probabilistic map representation based on the receiver positions. Thus, if the receiver knows its current location, the information in the probabilistic map helps to estimate the trajectory of further receiver movement. Hence, as soon as the receiver returns to an already mapped position, information of the probabilistic map can be used for the movement to obtain better estimations of the receiver position. The algorithm is evaluated based on measurements with one fixed transmitter and a moving pedestrian which moves on partially overlapping loops.

Decoding of repeated-root cyclic codes up to new bounds on their minimum distance

The well-known approach of Bose, Ray-Chaudhuri, and Hocquenghem and its generalization by Hartmann and Tzeng are lower bounds on the minimum Hamming distance of simple-root cyclic codes. We generalize these two bounds to the case of repeated-root cyclic codes and present a syndrome-based burst error decoding algorithm with guaranteed decoding radius based on an associated folded cyclic code. Furthermore, we present a third technique for bounding the minimum Hamming distance based on the embedding of a given repeated-root cyclic code into a repeated-root cyclic product code. A second quadratic-time probabilistic burst error decoding procedure based on the third bound is outlined.

Positioning Using Terrestrial Multipath Signals and Inertial Sensors

This paper extends an algorithm that exploits multipath propagation for position estimation of mobile receivers named Channel-SLAM. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) and estimates the positions of the VTs simultaneously with the mobile receiver positions. For Channel-SLAM it is essential to obtain angle of arrival (AoA) measurements for each MPC in order to estimate the VT positions. In this paper, we propose a novel Channel-SLAM implementation based on particle filtering which fuses heading information of an inertial measurement unit (IMU) to omit AoA measurements and to improve the position accuracy. Interpreting all MPCs as signals originated from VTs, Channel-SLAM enables positioning also in non-line-of-sight situations. Furthermore, we propose a method to dynamically adapt the number of particles which significantly reduces the computational complexity. A posterior Cramer-Rao lower bound for Channel-SLAM is derived which incorporates the heading information of the inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a single fixed transmitter and a moving pedestrian carrying the receiver and the IMU. The evaluations show that accurate position estimation is possible without the knowledge of the physical transmitter position by exploiting MPCs and the heading information of an IMU.

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust long-term SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements.

Simultaneous localization and mapping for pedestrians using low-cost ultra-wideband system and gyroscope

Ultra-wideband (UWB) is a promising positioning system that has undergone massive research development in recent years. Most UWB systems assume prior knowledge on the positions of the UWB anchors. Without knowing the anchor positions, an accurate position estimate of a user is difficult. Hence, this paper presents a novel simultaneous localization and mapping (SLAM) approach for pedestrian localization using a UWB system, where the locations of the anchors are unknown. We fuse the distance estimates of the UWB system with heading information obtained from an inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a moving pedestrian and fixed anchors with unknown positions. The evaluations show that an accurate position estimation of both the pedestrian and the anchors is possible without any prior knowledge on the anchor positions.

Multipath assisted positioning in vehicular applications

Precise localization and tracking in intelligent transportation systems has aroused great interest since it is required in a large variety of applications. The positioning accuracy of global navigation satellite systems is unreliable and insufficient enough for many use cases. In urban canyons or tunnels, the positioning performance degrades due to a low received signal power, multipath propagation, or signal blocking. Instead we exploit the ubiquitous access to cellular mobile radio networks. Cellular networks are designed to cover the access to the network in an area by a single link to reduce the risk of interference from neighboring base stations. The idea of Channel-SLAM is to exploit the numerous multipath components (MPCs) of a radio signal arriving at the receiver for positioning. Each MPC can be regarded as being sent from a virtual transmitter in a pure line-of-sight condition. Within this paper, we show how to apply multipath assisted positioning in an urban scenario. Therefore, we analyze how a road user equipped with a circular antenna array is tracked in an urban scenario in the presence of only one physical transmitter. We further jointly estimate the positions of the physical and the virtual transmitters to enrich maps.