A Bayesian Approach to Beamforming for Uncertain Direction-Of-Arrival. Chunwei Jethro Lam
Author: Chunwei Jethro Lam
Published Date: 03 Sep 2011
Publisher: Proquest, Umi Dissertation Publishing
Original Languages: English
Book Format: Paperback::208 pages
ISBN10: 1243551712
Filename: a-bayesian-approach-to-beamforming-for-uncertain-direction-of-arrival.pdf
Dimension: 189x 246x 11mm::381g
Download Link: A Bayesian Approach to Beamforming for Uncertain Direction-Of-Arrival
MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization Bayesian PCA is a probabilistic approach to PCA that infers on a generative model of the data with appropriately chosen priors (Bishop, 1999). In particular, a key feature of this approach is that
Robust Regularized Least-Squares Beamforming Approach to Signal Estimation Mohamed Suliman, Tarig Ballal, and Tareq Y. Al-Naffouri. Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah Province, Saudi Arabia.
We present a Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array. The prior is a description of the dynamic behavior we expect for the targets which is modeled as constant velocity motion with a Gaussian disturbance acting
A Genetic Algorithm-based Beamforming Approach for Delay-constrained Networks Hao Guo, Behrooz Makki, Tommy Svensson Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden,fbehrooz.makki, Abstract In this paper, we study the performance of initial access
Bayesian focusing method minimizes the mean-square error of the techniques are effective in rejecting interference signals whose incident directions of arrivals takes into account the uncertainty of the DOAs modelling them as random
To deal with uncertainty in sig- Keywords robust adaptive beamforming, signal-to- nal arrival direction, we propose robust adaptive beam- interference-plus-noise ratio (SINR), Bayesian approach, forming algorithm based on a Bayesian approach, which signal steering vector mismatch balances the use of observed sample data and a priori knowledge about source direction of arrival (DOA).
Robust Adaptive Beamforming Using Worst-Case Performance Optimization: A Solution to the Signal Mismatch Problem Sergiy A. Voroov, Member, IEEE, Alex B. Gershman, Senior Member, IEEE, and Zhi-Quan Luo, Member, IEEE Abstract Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment,
Bayesian Beamforming for DOA Uncertainty: Theory and Implementation Chunwei Jethro Lam, Student Member, IEEE, and Andrew C. Singer, Senior Member, IEEE Abstract A Bayesian approach to adaptive narrowband beamforming for uncertain source direction-of-arrival (DOA) is presented.
Abstract: A Bayesian approach to beamforming is used to derive a sequential adaptive beamformer for estimating Gauss-Markov signals when the source direction-of-arrival (DOA) is uncertain.
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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract A Bayesian approach to adaptive narrowband beamforming for uncertain source direction-of-arrival (DOA) is presented. The DOA is modeled as a random variable with prior statistics that describe its level of uncertainty. The Bayesian beamformer is the
Abstract. A recursive Bayesian beamforming is proposed for the steering vector uncertainty and strong interferences. Signal and noise powers are unknown, and beamforming weight is modeled as a complex Gaussian vector that characterizes the level of projected steering vector uncertainty.
DOA, Beamforming and Sound | ResearchGate, the professional network for scientists. A Bayesian Approach to Informed Spatial Filtering With Robustness Against is robust to uncertain or erroneous direction-of-arrival (DOA) information.
Direction-of-arrival (DOA) estimation refers to the localization of sound sources on an angular grid from noisy measurements of the associated wavefield with an array of sensors. For accurate localization, the number of angular look-directions is much larger than the number of sensors, hence, the problem is underdetermined and requires
direction-of-arrival (DOA) angles of multiple targets using an acoustic array in the such as the partitioning approach [2], [16], or other Bayesian approaches [17]. Than just beamforming [2] [4], (iii) is robust against changes in target signal Particle filters for tracking an unknown number of sources, IEEE Trans. On.
The estimation of bearing angle (or direction of arrival (DOA)) of underwater channel's impulse response, the frequency-difference beamforming method for in the uncertain ocean environment, such as the signal look direction beamforming for spatially extended sources in a Bayesian formulation.
The performance of adaptive array beamforming algorithms substantially estimating the direction of arrival (DOA) of the actual signal from observations. In addition, it has strong robustness to the uncertainty of actual signal DOA and ratio (SINR) Bayesian approach signal steering vector mismatch.
on direction-of-arrival (DOA) estimation is based on the MUltiple SIgnal Classification (MUSIC) algorithm for finding the Position Location (PL) of the desired user. The beamformer steer the main beam towards the desired user and nullify all other interferer, through adaptive beamforming using Least Mean Square (LMS) algorithm.
dimensional direction of arrival estimation method based on gridless compressive sensing as the Capon's Beamformer [1], Multiple Signal Classification. (MUSIC) [2] Note that the uncertainty along elevation is significantly more than signals based on sparse Bayesian learning, IEEE Sensors J., vol. 16, no. 7, pp.
Adaptive beamforming is widely used in array signal processing for enhancing a In [11], a method of pattern synthesis with sparse arrays based on Bayesian Due to the spatial sparseness of the arriving signal, CS theory is adopted to direction and nulls are steered to the directions of interferences.
Abstract: An adaptive beamformer that is robust to uncertainty in source direction-of-arrival (DOA) is derived using a Bayesian approach. The DOA is assumed to be a discrete random variable with a known a priori probability density function (PDF) that reflects the level of uncertainty in the source DOA.
sparse Bayesian learning (SBL) for 2D beamforming in az- imuth and elevation. Been proposed and used for 2D direction-of-arrival (DOA) es- timation [12, 13
Abstract: A Bayesian approach to adaptive narrowband beamforming for uncertain source direction-of-arrival (DOA) is presented. The DOA is modeled as a random variable with prior statistics that describe its level of uncertainty.
Direction-of-arrival (DOA) estimation refers to the localization of sound sources on an angular grid from Since the spatial distribution of sources is unknown, the Bayesian estimation theory provides a systematic way to.
This paper demonstrates this using Sparse Bayesian learning (SBL) method for direction-of-arrival estimation via sparse Bayesian learning, IEEE Trans. C. F. Mecklenbräuker, Sparse Bayesian learning with uncertainty
A Bayesian approach to adaptive narrowband beamforming for uncertain source direction-of-arrival (DOA) is presented. The DOA is modeled as a random variable with prior statistics that describe its level of
T1 - A sequential Bayesian beamformer FOR Gauss-Markov signals. AU - Lam, Chun Wei J. AU - Singer, Andrew Carl. PY - 2002/1/1. Y1 - 2002/1/1. N2 - A Bayesian approach to beamforming is used to derive a sequential adaptive beamformer for estimating Gauss-Markov signals when the source direction-of-arrival (DOA) is uncertain.
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