Due to civilian noise complaints and damage claims, there is a need to establish an accurate record of impulse noise generated at military installations. Current noise monitoring systems are susceptible to false positive detection of impulse events due to wind noise. In order to analyze the characteristics of noise events, multiple channel data methods were investigated. A microphone array was used to collect four channel data of military impulse noise and wind noise. These data were then analyzed using cross-correlation functions to characterize the input waveforms. Four different analyses of microphone array data are presented. A new value, the min peak correlation coefficient, is defined as a measure of the likelihood that a given waveform originated from a correlated noise source. Using a sound source localization technique, the angle of incidence of the noise source can be calculated. A method was also developed to combine the four individual microphone channels into one. This method aimed to preserve the correlated part of the overall signal, while minimizing the effects of uncorrelated noise, such as wind. Lastly, a statistical method called the acoustic likelihood test is presented as a method of determining if a signal is correlated or not.

No abstract

No abstract

No abstract

The ability of terrorist nehvorks to conduct sophisticated arid simultaneous attacks -the most recsnf one on March I I , 2004 iri Madrid, Spain -suggests that there is a sigiiificaiif need for. developing irlfoi-mation technology tools for corrnrer-terrorisni analysis. These rerlinologies corrld empouer iritelligerice analysts to find irlformatiorr fastel; sharr, arid collaborate across agencies, "connect the dots" better, and coridrrct qiricker and better aualyses. One srrch technology, the Adap:ive Safer) Analjsis arid Monitoring (ASAM) systenr is irrider development at the Uriiiai-sir). of Connecticrrt. In iliis paper, the ASAM system is introduced and ifs capabilities are d i m m e d . Tlie virlnerabilities at the Athens 28904 Oljmnpics are modeled and patteins of anomalous behavior are identified risirig a coriibirratiori of featirreaided niriltiple target tracking, hidden Markov models (HMMs), and Bayesian nehvorks (BNs). Furictiorialiry of the ASAM system is illustrated by nny of applicarioii to two hypotlietical models of fer{-oris1 activities at the Athens 2004 Olyriipics.