Detection and Location Capabilities of Multiple Infrasound Arrays

Abstract

In the first phase of this contract, we have developed an integrated approach to using wavenumber parameters and their covariance properties from a collection of local arrays for estimating location, along with an uncertainty ellipse. Hypothetical wavenumber estimators and their uncertainties are used as input to a Bayesian nonlinear regression that produces fusion ellipses for event locations using probable configurations of detecting stations in the proposed global infrasound array. The network capability is characterized as a function of separate local-array characteristics, including signal-to-noise ratios, bandwidth, array geometry, local correlation and coherent interfering signals. A summary map displays the average areas of the 90% posterior probability ellipses for each hypothetical location, assuming a random configuration of detecting stations. In the second phase of the project, we are developing local-array parameters that will be used as input for estimating the capabilities of the global International Monitoring System (IMS). A small-array theory has been given in previous work that characterizes the detection probabilities and large sample variances of the local-array optimal maximum likelihood detectors. We are working on assessing the local-array performance of the multiple signal F-statistic as well as those of alternative high resolution detectors produced by Capon (1969) and the multiple signal classification (MUSIC) algorithm proposed by Schmidt (1979). For pure and mixed infrasound signals from two explosions, we find that all statistics have comparable resolution. The F-statistic retains a number of theoretical advantages, namely (1) a known large sample distribution that yields detection and false alarm probabilities (2) direction of arrival (DOA) estimators with means and covariance matrix determined by the Cramer-Rao lower bound and (3) easily estimated signal-to-noise ratios.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2001
Accession Number
ADA526313

Entities

People

  • Robert H. Shumway

Organizations

  • University of California

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bandwidth
  • Covariance
  • Data Science
  • Databases
  • Detection
  • Detectors
  • Estimators
  • Explosions
  • False Alarms
  • Frequency
  • Information Science
  • Monitoring
  • Nuclear Explosions
  • Probability
  • Statistics
  • Uncertainty
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Phased Array Antenna Design.
  • Seismology
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms