Detecting and localizing impulsive acoustic sources in the daytime using distributed elevated acoustic sensors with large
baseline separations has distinct advantages over small ground-based arrays. There are generally two reasons for this:
first, during the daytime, because of more direct and less encumbered propagation paths, signal levels are generally
larger at altitude than near the ground. Second, larger baselines provide improved localization accuracy. Results are
reported from a distributed array of acoustic sensors deployed during an experiment near Bourges, France during June of
2008. The distributed array consisted of microphones and GPS receivers attached to the tether lines of three widely
separated aerostats. The sound sources were various impulsive devices. Results from the measurements are presented
and discussed. Localization errors (GPS accuracy, propagation calculation, and aerostat motion, etc) are discussed.
Possible ways to improve the localization accuracy are suggested.
KEYWORDS: Land mines, Laser Doppler velocimetry, Modulation, Mining, Acoustics, Signal processing, Doppler effect, Frequency modulation, Motion measurement, Signal detection
Exciting the ground with an acoustic tonal projected by a loud speaker is one method for detecting buried landmines. The subsequent ground motion is measured with a laser Doppler vibrometer (LDV). The LDV data contain the tonal in a frequency modulated form. One approach for demodulating the data and extracting the tonal uses a Hilbert transform. The ground velocity can be obtained from these data to identify mine presence or absence. An alternate approach to mine detection is to perform consecutive fast Fourier transforms on the modulated LDV data, and to average the output powers in each spectral bin. This results in a ground velocity distribution function in the spectrum that is manifested by a broadband of modulated frequencies. The proximity of the beams to a mine (over, near, not near) can be determined from the bandwidth of the modulation. Furthermore, the velocity distribution functions provide additional information that previous techniques do not. Such information may be useful for separating mines from false targets. This technique is discussed, and the results from measured MB-LDV data are presented. This paper is based upon work supported by the U. S. Army Communications-Electronics Command Night Vision and Electronic Sensors Directorate under Contract DAAB15-02-C-0024.
Non-linear signal processing algorithms, developed originally by R.A. Wagstaff for ocean acoustics and named "AWSUM(K)," have been applied in this laboratory to atmospheric acoustic signals measured in the presence of severe intermittent noise. It has been found that the AWSUM(K) processors, which filter out strong signals and pass weak signals, can provide dramatic gains in the signal-to-noise ratio for a steady sinusoidal signal strongly degraded by intermittent manmade noise and intermittent wind noise. Further, applications of the processors to field data have shown a number of systematic behaviors that so far have not been explained or understood quantitatively. This article presents a theoretical analysis of the AWSUM(K) processors for a steady sinusoidal signal in the presence of exponential (Rayleigh) noise and intermittent (non-Rayleigh) noise. The theory quantitatively explains the observed behaviors of the AWSUM(K) processors. In particular, it is shown that in the limit of large sample number, the AWSUM(K) gain in the (signal+noise)-to-noise ratio is independent of the sample number and processor order (for K≥2). For a steady sinusoidal signal, the gain is determined solely by the shape of the noise distribution function near zero. For Rayleigh noise, for example, the gain is given by exp(SNR)/(SNR+1), where SNR is the usual linear signal-to-noise ratio (e.g., SNR = 1 corresponds to a signal-to-noise ratio of 0 dB). For intermittent manmade noise and intermittent wind noise, the measured noise distribution function is strongly peaked near zero, so that gains in approaching 20 dB are predicted, even for small values of SNR. The predictions are in accord with field data from an atmospheric sound propagation experiment.
KEYWORDS: Acoustics, Missiles, 3D acquisition, Wavefronts, Numerical analysis, Solids, Signal to noise ratio, Data centers, IRIS Consortium, Seismic sensors
It is well known that lines of bearing to an airborne broadband target can be easily measured on a small ground-based array of microphones. With a stationary target and two arrays, the target location can be estimated by direct triangulation, i.e., by the crossing point of bearing lines. With a moving source, however, one must identify arrival times on the arrays that correspond to a common emission point or, equivalently, a common emission time. This paper shows that, with two arrays, the three-dimensional track of a moving airborne target can be determined by finding the stationary points of an iterative non-linear equation. The equation is of the form (tau) geo((tau) bt) equals (tau) 'bt where (tau) geo is the difference in travel times determined from geometry, (tau) bt is the travel time difference taken from the bearing-time curves for two different arrays, and (tau) bt is the estimated value for (tau) bt. The stationary points, i.e., where (tau) geo equals (tau) bt, allow the target track to be computed directly from triangulation. Examples are discussed using simulated data.
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