ABOUT TELECOM SUDPARIS:
Telecom SudParis is a public graduate school for engineering, which has been recognized on the highest level in the domain of digital technology. The quality of its courses is founded on the scientific excellence of its faculty and on teaching techniques that emphasize project management, innovation and intercultural understanding. Telecom SudParis is part of the Institut Mines-Telecom, the number one group of engineering schools in France, under the supervision of the Minister for Industry. Telecom SudParis with Ecole Polytechnique, ENSTA Paris, ENSAE Paris and Telecom Paris are co-founders of the Institut Polytechnique de Paris, an institute of Science and Technology with an international vocation.
Its assets include: a personalized course, varied opportunities, the no.3 incubator in France, an ICT research center, an international campus shared with Institut Mines-Telecom Business School and over 60 student societies and clubs.
The objective of the project is to develop an approach to adaptively allocate sensing resources in multisensor multi-target tracking surveillance networks based on fundamental concepts in network information theory and decision-theoretic criteria.
Motivation: The objective of this project is to develop the underpinning methods required for autonomous distributed sensor management and fusion in challenging multi-target environments. The tools developed will help reduce the labour-intensive burden of monitoring single sensor feeds, and enable adaptive decisions to be taken to optimise the operation of multimodal networks and enhance the overall knowledge of the surveillance region. The focus on information-theoretic representations of multi-target tracking scenarios will enable verification of whether sensor feeds can be reliably fused, to avoid the potential of data corruption. The project will deliver key advanced in intelligent sensing to enable the continuous and adaptive surveillance
in dynamic environments. These will be scaleable for large-scale tracking of many targets from multiple distributed sensors.
The project will involve the development of algorithms that are able to automatically track multiple targets, classify, and allocate resources based on information received from multiple platforms with data association uncertainty and high false-alarm rates. Building on recent developments by the investigator in multi-target tracking and distributed sensor fusion, this work programme will develop methods for autonomous sensor allocation in large-scale multi-sensor multi-target tracking applications based on information-theoretic criteria. This will be achieved by re-evaluating the key tools in information theory applied to the challenges of multi-target surveillance based on point process theory, which is designed to accommodate uncertainty in the states of individual targets and the target number. The information-theoretic methods developed will be applied to multi-sensor problems to enable decisions to be made on how to allocate sensor resources in addition to refining the knowledge of the scene.
(i) The primary outcomes of the work will be the preparation of a journal article for a leading IEEE Transactions journal, such as Information Theory, Signal processing, or Aerospace and Electronic Systems.
(ii) Development of sensor management algorithm(s)
(iii) Report document which details the sensor management algorithm(s) and a detailed technical description of how the benefit of these are quantified
(iv) Code routines demonstrating the utility of a sensor management algorithm developed.
The key scientific outcome of the scientific work will be the preparation of a journal article for a leading IEEE Transactions journal, such as Information Theory, Signal processing, or Aerospace and Electronic Systems. Additionally, the work will be disseminated at a leading international conference such as the ISIF International Conference on Information Fusion.
 Multi-Sensor Network Information for Linear-Gaussian Multi-Target Tracking Systems, DE Clark, IEEE Transactions on Signal Processing 69, 4312-4325 2021
 A Formulation of the Adversarial Risk for Multi-object Filtering
A Narykov, E Delande, DE Clark, IEEE Transactions on Aerospace and Electronic Systems 57 (4), 2082-2092
 An Algorithm for Large-Scale Multitarget Tracking and Parameter Estimation
MA Campbell, DE Clark, F de Melo, IEEE Transactions on Aerospace and Electronic Systems 57 (4), 2053-2066
LEVEL OF TRAINING AND/OR EXPERIENCE REQUIRED FOR POSTDOC:
- PhD / doctorate
LEVEL OF TRAINING AND/OR EXPERIENCE REQUIRED FOR PhD SCHOLARSHIP:
-Masters in course related to statistics/mathematical engineering or French Engineering Diploma with strong mathematical component
ESSENTIAL SKILLS, KNOWLEDGE AND EXPERIENCE:
- Knowledge of statistical filtering methods and applications of information theory
- Experience programming in MATLAB and/or python
ABILITIES AND SKILLS:
- Self-motivated and driven to do research
- Good communication skills with project investigators and stakeholders
ADDITIONAL INFORMATION AND APPLICATION:
- Application deadline: June 30, 2022
- Nature of the contract: CDD/limited contract 12 months
- Job category and profession: II - P, Post-doctoral fellow
- The positions offered for recruitment are open to all with, on request, accommodations for candidates with disabilities