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 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. Index Terms Bound on the minimum distance, burst error, efficient decoding, folded code, repeated-root cyclic code, repeated-root cyclic product cod

User Tracking with Multipath Assisted Positioning-based Fingerprinting and Deep Learning

Multipath assisted positioning schemes allow localizing a user with only a single physical transmitter by treating multipath components (MPCs) as line-of-sight signals from virtual transmitters. The user position and the locations of the physical and virtual transmitters can be estimated jointly with simultaneous localization and mapping (SLAM). While such approaches often show very good positioning performance, they come at the cost of a high computational complexity. To reduce this complexity, multipath assisted positioning schemes based on SLAM may be combined with fingerprinting, where the fingerprints are features of the wireless radio channel. Within this paper, we present such an approach, where a deep neural network (DNN) is trained on data from a multipath assisted positioning scheme to predict the user position and the corresponding uncertainty from channel information. Based on the DNN, a Kalman filter can accurately and efficiently track the user position. We show by simulations that the positioning performance is improved by a factor of 1.5 while the computational complexity is crucially lower than that of multipath assisted positioning-based SLAM

Entropy of Transmitter Maps in Cooperative Multipath Assisted Positioning

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. The locations of physical and virtual transmitters are typically unknown, but can be estimated jointly with the location of a mobile terminal using simultaneous localization and mapping (SLAM). When users cooperate by exchanging maps of estimated positions of physical and virtual transmitters, the positioning performance can be improved drastically. Within this paper, we investigate such transmitter maps that are shared among users. We derive an approximation of the entropy of transmitter maps that is based on the unscented transform and analyze the evolution of this entropy over time. Our simulations indicate that the transmitter maps converge quickly

Cooperative Estimation of Maps of Physical and Virtual Radio Transmitters

In multipath assisted positioning, the spatial information contained in multipath components (MPCs) is exploited, as MPCs are regarded as line-of-sight signals from virtual transmitters. The positions of physical and virtual transmitters can be estimated jointly with the receiver position with simultaneous localization and mapping (SLAM). In our multipath assisted positioning approach called Channel-SLAM, the estimates from a channel estimator are used in a Rao-Blackwellized particle filter which implements SLAM. While the original Channel-SLAM algorithm is a single-user positioning system, we present a comprehensive framework for cooperative Channel-SLAM within this paper. Users cooperate by exchanging maps of estimated transmitter locations. With prior information about the locations of physical and virtual transmitters, the positioning performance of the users increases significantly. The more users contribute to such a transmitter map, the more increases the positioning performance. With simulations in an indoor scenario, we show that the positioning performance is bounded for cooperative Channel-SLAM in the long run

Multipath Assisted Positioning with Transmitter Visibility Information

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. Instead of trying to mitigate the influence of MPCs, the spatial information contained in MPCs is exploited for localization. The locations of the physical and virtual transmitters are in general unknown but can be estimated with simultaneous localization and mapping (SLAM). Recently, a multipath assisted positioning algorithm named Channel-SLAM for terrestrial radio signals has been introduced. It simultaneously tracks the position of a receiver and maps the locations of physical and virtual radio transmitters. Maps of estimated transmitter locations can be augmented by additional information. Within this paper, we propose to extend the Channel-SLAM algorithm by mapping information about the visibility of transmitters. A physical or virtual transmitter is visible, if its signal is received in a LoS condition. We derive a novel particle filter for Channel-SLAM that estimates and exploits visibility information on transmitters in addition to their locations. We show by means of simulations in an indoor scenario that our novel particle filter improves the positioning performance of Channel-SLAM considerably

Simultaneous Localization of a Receiver and Mapping of Multipath Generating Geometry in Indoor Environments

This paper presents an algorithm that aims at combining the pre-existing and dominating applications of radio signals, namely communication, navigation and sensing of an environment into an integrated approach. In wireless propagation the transmitted signal is reflected and scattered by objects. Especially in indoor or urban scenarios, the signal reaching the receive antenna consists of multiple paths, called multipath. Multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. With Channel-SLAM we introduced an algorithm which uses multipath components (MPCs) for positioning instead of mitigating them. In this paper, we show that MPCs allow us in addition to estimating the position of a receiver, to estimate the locations of reflecting surfaces and scatterers. We show that these estimations relate to floor plans of indoor environments. To verify the proposed algorithm, we evaluate the algorithm based on measurements using an ultra-wideband (UWB) system, the Decawaves DW1000 UWB transceiver

Multiple Hypothesis Data Association for Multipath-Assisted Positioning

Global navigation satellite system denied scenarios such as urban canyons or indoors cause a need for alternative precise localization systems. Our approach uses terrestrial signals of opportunity in a multipath-assisted positioning scheme. In multipath-assisted positioning, each multipath component arriving at a receiver is treated as a line-of-sight signal from a virtual transmitter. While the locations of the virtual transmitters are unknown, they can be estimated simultaneously to the user position using a simultaneous localization and mapping (SLAM) approach. An essential feature of SLAM is data association. This paper addresses the data association problem in multipath-assisted positioning, i.e., the identification of correspondences among physical or virtual transmitters. If a user recognizes a previously observed transmitter, it can correct its own position estimate. We generalize a previous version of our multiple hypothesis tracking scheme for data association in multipath-assisted positioning and show by means of simulations how data association improves the positioning accuracy

WiFi-RTT Indoor Positioning

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. Using WiFi for indoor positioning is a common approach because WiFi infrastructure is widely deployed. Recently the WiFi IEEE 802.11-2016 standard was released, which includes a fine timing measurement (FTM) protocol, more commonly known as WiFi-round-trip-time (WiFiRTT) protocol, for WiFi ranging. Thus, this paper researches timing based positioning algorithms, in this case using WiFiRTT distance estimates. Based on two measurement campaigns, in an antenna measurement chamber and in a typical indoor environment, a WiFi-RTT distance error model is derived. Both measurement campaigns show, that the distance is underestimated, hence, the estimated distance is lower than the true distance. The WiFi-RTT distance error model is afterwards included in the likelihood function of a particle filter (PF) and the positioning performances is evaluated in an indoor scenario. These evaluations show clearly the possibility of using WiFi-RTT distance estimates for indoor positioning

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