Zobrazeno 21 - 30
of 90
pro vyhledávání: ''
Publikováno v:
ICASSP
In this paper, a novel noise-robust recognition method for high-resolution range profile (HRRP) data is proposed based on target scatterer pattern to enhance its recognition performance under the test condition of low SNR. The target dominant scatter
Publikováno v:
ICASSP
The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a p
Publikováno v:
ICASSP
This paper concerns the line spectral estimation problem within the recent super-resolution framework. The frequencies of interest are assumed to follow a prior probability distribution. To effectively and efficiently exploit the prior information, w
Publikováno v:
ICASSP
In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional
Autor:
Mostafa Kaveh, Cheng-Yu Hung
Publikováno v:
ICASSP
Estimation of directions-of-arrival (DoA) in the spatial co-variance model is studied. Unlike the compressed sensing methods which discretize the search domain into possible directions on a grid, the theory of super resolution is applied to estimate
Publikováno v:
ICASSP
Tensor robust principal component analysis (PCA) approaches have drawn considerable interests in many applications such as background subtraction, denoising, and outlier detection, etc. In this paper we propose an online tensor robust PCA where the m
Autor:
Brittany Terese Fasy, Bei Wang
Publikováno v:
ICASSP
Topological data analysis (TDA) has rapidly grown in popularity in recent years. One of the emerging tools is persistent local homology, which can be used to extract local structure from a dataset. In this paper, we provide a survey that explores thi
Publikováno v:
ICASSP
We present a novel algorithm — ProSparse Denoise — that can solve the sparsity recovery problem in the presence of noise when the dictionary is the union of Fourier and identity matrices. The algorithm is based on a proper use of Cadzow routine a
Publikováno v:
ICASSP
In this paper, we consider the problem of quickly detecting an abrupt change of linear coefficients in linear regression models. In particular, the observer sequentially observes a sequence of observations {(xn, yn)}∞n=1, which is assumed to obey a
Publikováno v:
ICASSP
Consider a multicell multiuser MIMO (multiple-input multiple-output) system with a very large number of antennas at each base station (BS). The number of users in each cell is assumed to be fixed as the number of BS antennas grows large. Under certai