Modified quasi‐maximum likelihood estimator for polynomial phase signal
نویسندگان
چکیده
منابع مشابه
Statistical Analysis of Adaptive Maximum - Likelihood Signal Estimator
A classical problem in many radar and sonar applications is the adaptive detection/esti-mation of a given signal in the presence of zero mean Gaussian noise. Reed, Mallett, and Brennan (RMB) derived and analyzed an adaptive detection scheme where the noise adaptation and non-trivial nature of their analysis resulted from the use of a noise sample covariance matrix (SCM). The case now considered...
متن کاملSOM as likelihood estimator for speaker clustering
A new approach is presented for clustering the speakers from unlabeled and unsegmented conversation, when the number of speakers is unknown. In this approach, Self-Organizing-Map (SOM) is used as likelihood estimators for speaker model. For estimation of the number of clusters the Bayesian Information Criterion (BIC) is applied. This approach was tested on the NIST 1996 HUB-4 evaluation test in...
متن کاملMaximum likelihood estimator for magneto-acoustic localisation
This paper is devoted to the localization of magnetoacoustic sources moving in a straight line at a constant speed. Our technique is based on the association of narrow band acoustic signals and magnetostatic measurements. First of all, we describe features that make possible the association of magnetic and acoustic data, secondly, we show that positioning accuracy is much improved by this assoc...
متن کاملOn blind signal copy for polynomial phase signals
quency, the demodulated and sampled received signal can be modeled by, The problem of separating and estimating signals received by an array whose array manifold has an unknown structural form is usually referred to as the blind N X jÁ (t ) n k signal copy problem. In this paper we consider the x(t ) = a e +n(t ) = As(t ) +n(t ) (2) k n k k k blind signal copy problem for polynomial phase sign=...
متن کاملLecture 22: Maximum Likelihood Estimator
In the first part of this lecture, we will deal with the consistency and asymptotic distribution of maximum likelihood estimator. The second part of the lecture focuses on signal estimation/tracking. An estimator is said to be consistent if it converges to the quantity being estimated. This section speaks about the consistency of MLE and conditions under which MLE is consistent.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IET Signal Processing
سال: 2020
ISSN: 1751-9675,1751-9683
DOI: 10.1049/iet-spr.2019.0079