Caractérisation des problèmes conjoints de détection et d'estimation

The general theme of my research deals with the characterization of the joint detection-estimation problem arising frequently both in passive and active systems of measurement (radar, telecoms, sonar): the estimation of the deterministic (non random) parameters of an intermittent source of signal of interest embedded in a permanent noisy environment. This problem can be modelled as a binary hypothesis testing: H0 (noise signal only) and H1 (noise signal and signal of interest). My primary interest for deterministic parameters originates in my involvement in numerous studies dedicated to active radar estimation performance where the deterministic parametric model is the privileged model. Under the framework of deterministic parametric modelling, the problem under consideration can be addressed progressively, in terms of theoretical and computational complexity, in two steps: * the assessment of unconditional estimation performance, that is without a prior detection test, by resorting to lower bounds on estimation performance. In this first step, there is a single observation model H1. * the assessment of conditional estimation performance, that is including a prior detection test and leading to the characterization of the joint detection-estimation problem. This far more complex problem is investigated by studying the influence of a detection step on lower bounds on estimation performance when applied to two models of observations (the monopulse antenna and the Gaussian conditional observation model) for which some analytical expressions exist.