Both estimation and performance analysis for linear/nonlinear filtering problems are the core issues of my research interests.

The major fields of my PhD research can be elicited as follows:

• Bearings-Only Tracking. The aim of bearings-only tracking is to estimate position, velocity and possible extra features of one or several targets (targets' trajectories are modelled as random processes) using bearings-only measurements noise corrupted obtained by different sensors. A lot of solutions have been proposed to solve this nonlinear filtering problem, based on Kalman filter and more recently using sequential Monte Carlo methods. However, lots of issues remain to be further investigated to better understand the problem and develop robust algorithms!
  
• Performance Analysis and Sensor Management. The posterior Cramér-Rao bound (PCRB) gives a lower bound for mean square error. Therefore, to assess the performance of filtering algorithms and develop sensor management strategies, the PCRB is a powerful tool. During the last year, in collaboration with Jean-Pierre Le Cadre, I have derived an original closed-form PCRB for bearings-only tracking, which is based on an original coordinates system: the Logarithmic Polar Coordinates system.
  
• Distributed Tracking, Sensors Networks, Data Fusion.