Dispositif de reperage d'un homme ou d'un objet dans un milieu liquide

The invention concerns a device for automatically and rapidly locating (10) a person or an object at sea in poor meteorological conditions, comprising a radio wave transmitting device (20) provided on the person or the object and a reception device (30), provided in a boat, and designed to receive directly said waves and to process them without any other intermediary than the radio link (40) established between them. The transmitting device (20) comprises a transmitter (21) supplied by a power source (23), and delivering a radio signal to an antenna (22). Said transmitter is activated by an actuating member (24). The reception device (30) consists of a receiver (31) for the signal transmitted by the transmitter (21) and picked up by an antenna array (32) co-operating with an antenna switch (33). The antenna array (33) is connected to the receiver (31) whereof an output is connected to said unit processing (34) the received signal. A display unit (37), connected to said processing unit (34) via a user interface (35), enables the display of the information delivered by said unit (34). An audio warning (36) is connected to the activation of the transmission device (20).

Parameter Estimation with GNSS-Reflectometry and GNSS Synthetic Aperture Techniques

Miguel Angel Ribot Sanfelix

Aside from intentional interference, multipath is the most significant error source for Global Navigation Satellite Systems (GNSS) receivers in many operational scenarios. In this thesis, we study the multipath estimation from two different perspectives: to retrieve useful information from it using GNSS-Reflectometry (GNSS-R) techniques; and to mitigate its effects or to estimate its direction-of-arrival (DOA) as well as the line-of-sight (LOS) signal¿s using synthetic aperture (SA) processing. The first part of the thesis focuses on precision bounds for GNSS-R techniques for ground-based receivers, in scenarios where a single antenna simultaneously receives the LOS signal and a specular reflection. First, we derive the Cramér-Rao bound (CRB) of the receiver¿s height and the reflection coefficient, with the latter depending on the surface¿s electrical properties. More specifically, we propose a CRB derivation applicable to GNSS-R techniques that make use of the phase information and long observation times, such as the interference pattern technique (IPT). The derivation is based on the parameter transformation of the Fisher information matrix. We study the dependence of the computed CRB on the scenario and the receiver bandwidth. The CRB results for the simulated scenarios are consistent with the precision reported for many GNSS-R techniques used in these scenarios. The proposed CRB is meant to benchmark and compare new and existing techniques. Besides the derived CRB, we propose an algorithm to obtain the maximum-likelihood (ML) estimator of the parameters of interest with the IPT: the segmented ML estimator (SML). The SML transforms a complex multivariate optimization problem into multiple simpler ones by dividing the parameter search space taking advantage of the cost function¿s particular structure. The SML is validated with simulated signal and asymptotically cross-validates the CRB results. The second part of the thesis is devoted to the study of the SA processing of GNSS signals. The goal is to estimate the DOA of the signals received, and mitigate errors in the navigation solution caused by interfering signals, such as multipath. We start by deriving the CRB for the SA context, as a function of the antenna trajectory. This CRB considers the effect of the antenna complex gain, and we show in simulations that it is possible to achieve meaningful DOA estimation only by changing the antenna¿s orientation. We continue by proposing a development framework built upon a signal tracking architecture integrating SA processing. Before any SA processing, it is necessary to estimate and compensate any carrier phase contribution not related to the antenna motion. To do so, we propose two new sequential techniques based on the extended Kalman filter (EKF): EKF1 and EKF2. Also, we develop an open-loop version of the proposed SA tracking architecture, more robust than its closed-loop counterpart. Finally, we validate the proposed architecture and SA-based techniques with synthetic GPS signals at first, and then with real signals, recorded using an antenna mounted on a mechanical rotating arm. The obtained results validate the implemented techniques and show how the proposed SA architecture can ultimately mitigate the position bias error observed in environments with severe multipath interference.

EPFL2018

Method for wireless transmission between two electroacoustic transducers, and device for carrying out said method

Michel Declercq, Patrick Favre

The invention relates to a method and a device for wireless transmission between at least one first transducer (T1) arranged in a first location and at least one second transducer (T2) arranged in a second location remote from the first location without using radio waves. Since the two transducers (T1, T2) are electroacoustic transducers, acoustic waves (W) are generated in the first transducer (T1), said acoustic waves are emitted in the direction of the second transducer (T2), the acoustic waves are captured in the second transducer (T2), and the acoustic waves received in the second transducer (T2) are converted at least in part into electrical energy. Said electrical energy is stored in a storage element (M) of the transducer (T2) and can be used for processing information by means of a processing device (P) and transmitting said information to the first transducer (T1) using modulated reflected acoustic waves (WR).

2010

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature values in different geographical locations, information diffusion in social networks, functional activities in the brain, or 3D meshes in computer graphics. The representation, analysis, and compression of such data is a challenging task and requires the development of new tools that can identify and properly exploit the data structure. In this thesis, we formulate the processing and analysis of structured data using the emerging framework of graph signal processing. Graphs are generic data representation forms, suitable for modeling the geometric structure of signals that live on topologically complicated domains. The vertices of the graph represent the discrete data domain, and the edge weights capture the pairwise relationships between the vertices. A graph signal is then defined as a function that assigns a real value to each vertex. Graph signal processing is a useful framework for handling efficiently such data as it takes into consideration both the signal and the graph structure. In this work, we develop new methods and study several important problems related to the representation and structure-aware processing of graph signals in both centralized and distributed settings. We focus in particular in the theory of sparse graph signal representation and its applications and we bring some insights towards better understanding the interplay between graphs and signals on graphs. First, we study a novel yet natural application of the graph signal processing framework for the representation of 3D point cloud sequences. We exploit graph-based transform signal representations for addressing the challenging problem of compression of data that is characterized by dynamic 3D positions and color attributes. Next, we depart from graph-based transform signal representations to design new overcomplete representations, or dictionaries, which are adapted to specific classes of graph signals. In particular, we address the problem of sparse representation of graph signals residing on weighted graphs by learning graph structured dictionaries that incorporate the intrinsic geometric structure of the irregular data domain and are adapted to the characteristics of the signals. Then, we move to the efficient processing of graph signals in distributed scenarios, such as sensor or camera networks, which brings important constraints in terms of communication and computation in realistic settings. In particular, we study the effect of quantization in the distributed processing of graph signals that are represented by graph spectral dictionaries and we show that the impact of the quantization depends on the graph geometry and on the structure of the spectral dictionaries. Finally, we focus on a widely used graph process, the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that depends on the graph topology and exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm.