Single Antenna Anchor-Free UWB Positioning based on Multipath Propagation

Radio based localization and tracking usually require multiple receivers/transmitters or a known floor plan. This paper presents a method for anchor free indoor positioning based on single antenna ultra wideband (UWB) measurements. By using time of arrival information from multipath propagation components stemming from scatterers with different, but unknown, positions we estimate the movement of the receiver as well as the angle of arrival of the considered multipath components. Experiments are shown for real indoor data measured in a lecture room with promising results. Simultaneous estimation of both receiver motion, transmitter and scatterer positions is performed using an factorization based approach followed by non-linear least squares optimization. A RANSAC approach to automatic matching of data has also been implemented and tested. The resulting reconstruction is compared to ground truth motion as given by the antenna positioner. The resulting accuracy is in the order of one cm

A Unifying Approach to Minimal Problems in Collinear and Planar TDOA Sensor Network Self-Calibration

This work presents a study of sensor network calibration from time-difference-of-arrival (TDOA) measurements for cases when the dimensions spanned by the receivers and the transmitters differ. This could for example be if receivers are restricted to a line or plane or if the transmitting objects are moving linearly in space. Such calibration arises in several applications such as calibration of (acoustic or ultrasound) microphone arrays, and radio antenna networks. We propose a non-iterative algorithm based on recent stratified approaches: (i) rank constraints on modified measurement matrix, (ii) factorization techniques that determine transmitters and receivers up to unknown affine transformation and (iii) determining the affine stratification using remaining non-linear constraints. This results in a unified approach to solve almost all minimal problems. Such algorithms are important components for systems for self-localization. Experiments are shown both for simulated and real data with promising results

Tracking and positioning using phase information from estimated multi-path components

High resolution radio based positioning and tracking is a key enabler for new or improved cellular services. In this work, we are aiming to track user movements with accuracy down to centimeters using standard cellular bandwidths of 20-40 MHz. The goal is achieved by using phase information from the multi-path components (MPCs) of the radio channels. First, an extended Kalman filter (EKF) is used to estimate and track the phase information of the MPCs. Each of the tracked MPCs can be seen as originating from a virtual transmitter at an unknown position. By using a time difference of arrival (TDOA) positioning algorithm based on a structure-of-motion approach and translating the tracked phase information into propagation distances, the user movements can be estimated with a standard deviation of the error of 4.0 cm. The paper should be viewed as a proof-of-principle and it is shown by measurements that phase based positioning can be a promising solution for movement tracking in cellular systems with extraordinary accuracy

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Polynomial Solvers for Geometric Problems - Applications in Computer Vision and Sensor Networks

Given images of a scene taken by a moving camera or recordings of a moving smart phone playing a song by a microphone array, how hard is it to reconstruct the scene structure or the moving trajectory of the phone? In this thesis, we study and solve several fundamental geometric problems in order to provide solutions to these problems. The key underlying technique for solving such geometric problems is solving systems of polynomial equations. In this thesis, several general techniques are developed. We utilize numerical schemes and explore symmetric structures of polynomial equations to enable fast and stable polynomial solvers. These enable fast and robust techniques for reconstruction of the scene structures using different measurements. One of the examples is structure from sound. By measuring the time-of-arrivals of specific time instances of a song played on a phone, one can reconstruct the trajectory of the phone as well as the positions of the microphones up to precision of centimeters

Pose Estimation with Unknown Focal Length using Points, Directions and Lines

In this paper, we study the geometry problems of estimating camera pose with unknown focal length using combination of geometric primitives. We consider points, lines and also rich features such as quivers, i.e. points with one or more directions. We formulate the problems as polynomial systems where the constraints for different primitives are handled in a unified way. We develop efficient polynomial solvers for each of the derived cases with different combinations of primitives. The availability of these solvers enables robust pose estimation with unknown focal length for wider classes of features. Such rich features allow for fewer feature correspondences and generate larger inlier sets with higher probability. We demonstrate in synthetic experiments that our solvers are fast and numerically stable. For real images, we show that our solvers can be used in RANSAC loops to provide good initial solutions

Stratified Sensor Network Self-Calibration From TDOA Measurements

This paper presents a study of the sensor network calibration time-difference-of-arrival (TDOA) measurements. Such calibration arise in several applications such as calibration of (acoustic or ultrasound) microphone arrays, bluetooth arrays, and radio antenna networks. We propose a non-iterative algorithm that apply a three-step stratification process, (i) using a set of rank constraints to determine the unknown offsets, (ii) applying factorization techniques to determine transmitters and receivers up to unknown affine transformation and (iii) determining the affine stratification using the remaining constraints. This results in novel algorithms for direct recovery of both transmitter and receiver positions using time-difference-of-arrival measurements, down to 6 receivers and 8 transmitters. Experiments are shown both for simulated and real data with promising results