Blind direction-of-arrival estimation in acoustic vector-sensor arrays via tensor decomposition and Kullback-Leibler divergence covariance fitting
A Weiss - IEEE Transactions on Signal Processing, 2020 - ieeexplore.ieee.org
A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-
Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured …
Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured …
A bilateral bound on the mean-square error for estimation in model mismatch
A bilateral (ie, upper and lower) bound on the mean-square error under a general model
mismatch is developed. The bound, which is derived from the variational representation of …
mismatch is developed. The bound, which is derived from the variational representation of …
Maximum likelihood estimation of a low-rank probability mass tensor from partial observations
We consider the problem of estimating the Probability Mass Function (PMF) of a discrete
random vector (RV) from partial observations, namely when some elements in each …
random vector (RV) from partial observations, namely when some elements in each …
A Novel Range-Selective Generalized Weighted Centroid Method for Source Localization in Bearing-Only Sensor Networks
JA Luo, XP Zhang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
In this study, the source localization problem is investigated using a group of networked
sensors if only bearing measurements are available. The existing bearing-only source …
sensors if only bearing measurements are available. The existing bearing-only source …
Asymptotically optimal blind calibration of uniform linear sensor arrays for narrowband Gaussian signals
An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband
Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) …
Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) …
Unified subspace fitting framework and its performance analysis for direct position determination in the presence of multipath propagation
J Du, H Yu, D Wang, D Zhang, G Liu - IEEE Access, 2019 - ieeexplore.ieee.org
The cost functions and their performances of direct position determination (DPD) methods in
the presence of multipath propagation are investigated. We first establish a general DPD …
the presence of multipath propagation are investigated. We first establish a general DPD …
A novel efficient estimator for three‐dimensional bearings‐only source localisation with unknown sensor altitude and systematic measurement errors
HY Hu, JA Luo, DL Peng - IET Radar, Sonar & Navigation, 2024 - Wiley Online Library
A novel two‐stage profile maximum likelihood estimator is proposed to estimate the source
location and the systematic errors jointly, with the aim of addressing the problem of source …
location and the systematic errors jointly, with the aim of addressing the problem of source …
Estimation of a low-rank probability-tensor from sample sub-tensors via joint factorization minimizing the Kullback-Leibler divergence
Recently there has been a growing interest in the estimation of the Probability Mass
Function (PMF) of discrete random vectors (RVs) from partial observations thereof (namely …
Function (PMF) of discrete random vectors (RVs) from partial observations thereof (namely …
Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems
F Chen, K Ji - 2020 Chinese Control And Decision Conference …, 2020 - ieeexplore.ieee.org
Aiming at the problem of redundant parameters in the over-parameterized model, a
maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based …
maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based …