Preliminary Adaptation of Speech Source Localization Algorithm for Reduced Bandwidth of Interest in Tornadic Infrasound Signals

2022 ◽  
Author(s):  
Brandon C. White ◽  
Ujjval Patel
Keyword(s):  
Source Localization ◽  
Localization Algorithm ◽  
Speech Source Localization

scholarly journals Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography

2021 ◽  
Author(s):  
Abhishek S. Bhutada ◽  
Chang Cai ◽  
Danielle Mizuiri ◽  
Anne Findlay ◽  
Jessie Chen ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Source Localization ◽  
Clinical Population ◽  
Evoked Responses ◽  
Localization Algorithm ◽  
Equivalent Current Dipole ◽  
Current Dipole ◽  
Tumor Patients ◽  
Equivalent Current ◽  
Sensory Evoked

AbstractMagnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

scholarly journals Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiaqi Song ◽  
Haihong Tao
Keyword(s):  
Source Localization ◽  
Near Field ◽  
Nuisance Parameter ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Dual Polarization ◽  
Rank Reduction ◽  
Localization Accuracy ◽  
Noncircular Signals ◽  
Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

scholarly journals Adaptive behaviors in multi-agent source localization using passive sensing

2016 ◽  
Vol 24 (6) ◽  
pp. 446-463 ◽  
Author(s):  
Mansoor Shaukat ◽  
Mandar Chitre
Keyword(s):  
Source Localization ◽  
Single Agent ◽  
Analytical Models ◽  
Localization Algorithm ◽  
Two Phase ◽  
Temporal Sampling ◽  
Passive Sensing ◽  
Source Signal ◽  
Localization Strategy ◽  
Multi Agent

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent’s adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent’s sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

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