Section: New Results

Sensors Networks: Tracking, Localization and Communication

Participants : Emmanuel Delande, Emmanuel Duflos, Pierre Chainais, Philippe Vanheeghe.

The sensor management problem

The aim of this work is to manage a set of sensors to track vehicles or groups of people in land applications. Our work focuses on sensor management in the frame of the random finite sets where the Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. We have proposed an extension of Mahler's work to the multi-sensor case by providing an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations ( [70] ). This work is done in collaboration with Thales Communications. The multi-sensor / multi-target filtering problem by using PHD filtering methods are topics developed in the The PhD thesis of Emmanuel Delande. This PhD thesis entitled "Multi-sensor PHD filtering with application to sensor management" will be defended in December 2011. In addition to the different questions described above, see also [22] and [23] . Then, a new approach using operational objectives, related to the type of application, for sensor manager is proposed.

Statistical signal processing: application to civil engineering

We have obtained a PICS (International Project for Scientific Cooperation) from the CNRS in 2008 for 3 years to work in cooperation with the Department of Civil and Environmental Engineering of the University of Waterloo (Canada). During this cooperation we have developed a belief functions based method to track the building materials on a construction site. ( [71] ). Based on this cooperation, during 2011 a new common research project with the same department of the University of Waterloo has been built, and is actually submitted for funding. The topic of this project is the using of nonparametric Bayesian models in the area of Non-destructive Testing.

Accurate Localization using Satellites in Urban Canyons

Today, Global Navigation Satellite Systems (GNSS) have penetrated the transport field through applications such as monitoring of containers. These applications do not necessarily request a high availability, integrity and accuracy of the positioning system. For safety applications (as complete guidance of autonomous vehicles), performances require to be more stringent. For, sensors may deliver very erroneous measurements because of such hard external conditions which reduce significantly the possibilities to receive direct signals. The consequences of environmental obstructions are unavailability of the service and reception of reflected signals that degrades in particular the accuracy of the positioning. Indeed, NLOS (Non Line Of Sight) signals, i.e. signals received after reflections on the surrounding obstacles, frequently occur in dense environments and degrade localization accuracy because of the delays observed on the propagation time measurement creating additional error on pseudorange estimation. In the previous years we have proposed new algorithms to improve the localization precision. This algorithm are based on two principles : a jump multimodel approach and a joint state - noise density estimation. We have focused this year on an approach using Dirichlet Process Mixture to track the noise density in urban canyon while estimating the position of the vehicle. Algorithm have been validated on real data collected in a French town : Belfort. Nicolas Viandier has defended his PhD on this subject on June 2011 . ( [76] , [75] , [84] , [85] [4] ). These results will be presented to the Workshop Non Parametric Bayes at the NIPS Conference en Decembre 2011 ([62] ) and to the ICASSP 2011 Conference ([37] ).

Internet of Things : Mitigation of Impulsive Noise Effects

The term "Internet of Things" has come to describe a number of technologies and research disciplines that enable the Internet to reach out into the real world of physical objects. Technologies like RFID, short-range wireless communications, real-time localization and sensor networks are now becoming increasingly common, bringing the Internet of Things into commercial use. In such applications the data sent by a thing to another may generate an impulse noise in the reception channel of objects in the neighbourhood. The noise appearing in such applications can be considered as α-stable. In this context, we've tackled the problem of interference mitigation in ad hoc networks. In such context, the multiple access interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption can not be considered to model this type of interference. Contrariwise, it can be accurately modeled by stable distributions. Here, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α-stable model for the signal distortion due to MAI. We have proposed a method for the joint estimation of the transmitted multicarrier signal and the noise parameters.Based on sequential Monte Carlo (SMC) methods, the proposed scheme allows the online estimation using a Raoblackwellized particle filter. These results have been presented to the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011) [29] . We are now focusing on bayesian non linear filtering with non stationnary alpha stable measumement noise. We have shown that a Dirichlet Process Mixture can improve the estimation by modelling the noise by both a infinite Cauchy mixture or a infinite alpha stable mixture. These first results will be presented to the Workshop Non Parametric Bayes at the NIPS Conference en Decembre 2011 [62] .

Image processing and statistical image modeling

Pierre Chainais arrived in SequeL in september 2010 with the purpose of a thematic evolution toward non parametric Bayesian approaches. This represents an important investment in very new directions on an emerging topic at the interface between machine learning and signal/image processing. Discussions have begun with Emmanuel Duflos and Philippe Vanheeghe on the use of non parametric Bayesian approaches to blind deconvolution of noisy natural images. The main objective is to use together the typical structure and sparsity of space-scale representations of images.

Pierre Chainais has continued working on several older projects. One of them deals with the segmentation of nanotubes in microscopic imaging [12] , [43] . B. Lebental at IFFSTAR works on the conception of new nano-sensors based on the use of carbon nanotubes to build a nano-membrane. P. Chainais has developed an image processing pipeline to analyse images of these nanomembranes so as to characterize their properties in a precise and objective manner. Among other properties, the histograms of orientations of the nanotubes is provided. This tool will be very useful since such nanosensors are becoming more and more common.

In solar astronomy [7] , [21] , we have proposed a tool for the virtual super-resolution of scale invariant textured images. The aim of this project was to provide astronomers with plausible high-resolution images to calibrate next generation spatial telescopes. In particular, our images can be used to optimize the compression algorithm to be embedded in a spatial telescope. In collaboration with M. Chevaldonné and J-M. Favreau (Université Clermont-Ferrand I), we work a software for texture synthesis on 3D surfaces [42] based on multifractal processes. A first version of the software is under current development. More marginal is our work on the use of stochastic processes for the simulation of turbulent pressure fields in collaboration with M. Pachebat (Laboratoire de Mécanique et d'Acoustique de Marseille) and Nicolas Totaro (LVA, INSA Lyon).