16 research outputs found
Exact Conditional and Unconditional Cram\`er-Rao Bounds for Near Field Localization
This paper considers the Cram\`er-Rao lower Bound (CRB) for the source
localization problem in the near field. More specifically, we use the exact
expression of the delay parameter for the CRB derivation and show how this
exact CRB can be significantly different from the one given in the literature
and based on an approximate time delay expression (usually considered in the
Fresnel region). This CRB derivation is then generalized by considering the
exact expression of the received power profile (i.e., variable gain case)
which, to our best knowledge, has been ignored in the literature. Finally, we
exploit the CRB expression to introduce the new concept of Near Field
Localization (NFL) region for a target localization performance associated to
the application at hand. We illustrate the usefulness of the proposed CRB
derivation and its developments as well as the NFL region concept through
numerical simulations in different scenarios
Multiple objective optimization Applied to Speech enhancement problem
Enhancement of speech corrupted by broadband noise is subject of interest in many applications. For several years, the investigation of methods of denoising the vocal signal has yielded very satisfactory results, but certain problems and questions still remain. The term speech quality in speech enhancement is associated with clarity and intelligibility. So, one of these issues is to reach a compromise between noise reduction, signal distortion and musical noise. In this paper, we studied one of the classical techniques based on the spectral subtraction developed by Boll and improved by Berouti where two parameters α and β to control the effects of the distortion and the musical noise are introduced. However, the choice on these parameters (α and β) remains empirical. Our works is to find a compromise between these two parameters to obtain an optimal solution depending on the environment, the unknown noise and its level. Moreover, we propose in this paper, an algorithm based on bi-objective approach precisely Particle Swarm Optimization (PSO) technique in association with speech enhancement technique proposed by Berouti et al. Comparative results show that the performance of our proposed method with several types of noise, depending on the environment and on various noise levels, are better than those of spectral subtraction methods of Boll or Berouti
Etude des performances limites en résolution pour le Radar MIMO actif fonctionnant en mode retournement temporel : SINR seuil et résolution limite
A New Robust Adaptive Algorithm for Second Order Blind Source Separation
This paper deals with the adaptive blind source separation problem in presence of impulsive noise. New algorithms extending the well known SOBI method from batch to adaptive scheme are introduced. At first, the standard Gaussian noise case is considered, leading to our first algorithm referred to as Adaptive SOBI (A-SOBI). Later on, a robust version of A-SOBI, referred to as RA-SOBI is derived to handle the impulsive noise case. RA-SOBI relies on robust subspace tracking for the whitening stage together with robust correlation estimation for the separation stage. All proposed algorithms are of relatively low complexity and allow to achieve good separation quality as illustrated by our simulation results