We consider a stochastic system of N $$ interacting particles with constant diffusion coefficient and drift linear in space, time-depending on two unknown deterministic functions. Our concern here is the nonparametric estimation these functions from continuous observation process [ 0 , T ] \left[0,T\right] for fixed large . define collections projection estimators belonging to finite-dimensiona...

This paper presents a blind adaptive detector for asynchronous DS-CDMA in multipath channels. The receiver exploits the underlying subspace structure for recursive estimation of the desired signal vector, and eliminates the need of explicit channel or delay estimation. The detector is constrained to the signal subspace (hence reduced rank), leading to computational efficiency while also benefit...

We describe a novel algorithm for the estimation of the colored noise level in audio signals with mixed noise and sinusoidal components. The noise envelope model is based on the assumptions that the envelope varies slowly with frequency and that the magnitudes of the noise peaks obey a Rayleigh distribution. Our method is an extension of a recently proposed approach of spectral peak classificat...

In this paper, adaptive estimation based on noisy quantized observations is studied. A low complexity adaptive algorithm using a quantizer with adjustable input gain and offset is presented. Three possible scalar models for the parameter to be estimated are considered: constant, Wiener process and Wiener process with deterministic drift. After showing that the algorithm is asymptotically unbias...