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: Signal detection, Signal to noise ratio, Signal processing, Temporal coherence, Interference (communication), Acoustics, Sensors, Automatic tracking, Fourier transforms, Lithium
Temporal coherence is an important property of many acoustic signals. This paper discusses two fluctuation-based
signal processors that improve the temporal coherence of phase and amplitude. Then they exploit the improved
coherences to achieve substantial gains, such as, elimination of all noise to achieve exceptionally large "noise-free"
automatic detections of temporally coherent signals. Both processors are discussed. One exploits phase fluctuations and
the other one exploits amplitude fluctuations. The exploited parameters and signal processors are defined. Results are
presented for automatic signal detection of a heavy treaded / tracked vehicle, a helicopter, a fast-boat in shallow coastal
water, and a submerged source in the ocean.
A signal processing model is presented for acoustic sensors on ground and unmanned aerial vehicles
(UAV). Such sensors normally experience more flow noise than stationary sensors, because moving
platforms must vary their velocity to accomplish their missions. In the case of the UAV, this includes
sufficient speed to remain airborne. Unfortunately, high airflow speeds over the sensor cause turbulence
noise that tends to confound the acoustic detection of signals from sources of interest on the ground. This
model transforms the fluctuations in the magnitudes and the phase angles of signals and turbulence noise.
The temporal coherences of the signals are improved to the point where detections can be made
unambiguously, and be based on temporal coherence rather than on the signal-to-noise ratio, which is the
customary way to detect signals. Additionally, because the flow noise is temporally incoherent, it is easily
discriminated against. The model transforms phase and amplitude fluctuations in a such a manner that the
temporal coherences of the signals are increased. This makes them more easily exploited to achieve signal
processing gains, such as increases in signal-to-noise ratio and automatic detection. The rationale for this
model is that both signal and noise posses magnitude, but only signals posses temporal coherence. Two
transformations are presented herein. One transforms the phase angles, and the other one transforms the
spectral amplitudes. The transformations give the amplitudes and phase angles similar exploitable
coherence characteristics, while the corresponding noise incoherence is easily attenuated.
KEYWORDS: Signal processing, Signal detection, Signal to noise ratio, Interference (communication), Temporal coherence, Signal attenuation, Acoustics, Sensors, Spatial resolution, Atmospheric propagation
A continuously adapting acoustic signal processor with an automatic detection/decision aid is presented. Its purpose is to preserve the signals of tactical interest, and filter out other signals and noise. It utilizes single sensor or beamformed spectral data and transforms the signal and noise phase angles into "aligned phase angles" (APA). The APA increase the phase temporal coherence of signals and leave the noise incoherent. Coherence thresholds are set, which are representative of the type of source "threat vehicle" and the geographic area or volume in which it is operating. These thresholds separate signals, based on the "quality" of their APA coherence. An example is presented in which signals from a submerged source in the ocean are preserved, while clutter signals from ships and noise are entirely eliminated. Furthermore, the "signals of interest" were identified by the processor's automatic detection aid. Similar performance is expected for air and ground vehicles. The processor's equations are formulated in such a manner that they can be tuned to eliminate noise and exploit signal, based on the "quality" of their APA temporal coherence. The mathematical formulation for this processor is presented, including the method by which the processor continuously self-adapts. Results show nearly complete elimination of noise, with only the selected category of signals remaining, and accompanying enhancements in spectral and spatial resolution. In most cases, the concept of signal-to-noise ratio looses significance, and "adaptive automated /decision aid" is more relevant.
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.
KEYWORDS: Signal processing, Signal to noise ratio, Filtering (signal processing), Interference (communication), Signal detection, Phased arrays, Acoustics, Electronic filtering, Sensors, Projection systems
A technique is presented for generating a modified cross- spectral density matrix (CSDM) that is sensitive to fluctuations in the frequency bin amplitudes and phases of sinusoidal signals (plus noise) and noise. The beamformed output of such a fluctuation-based CSDM provides signal-to- noise ratio (SNR) gains in excess of those achieved using a conventional CSDM. The additional gains are inversely proportional to the beamformed ratio of the signal bin fluctuations to the noise bin fluctuations. An unalerted auto-detection capability is another advantage of this new technique.
KEYWORDS: Signal detection, Signal to noise ratio, Interference (communication), Signal processing, Target detection, Sensors, Filtering (signal processing), Oceanography, Acoustics, Objectives
Amplitude and phase fluctuations are an inherent characteristic of many types of propagation media including sound in the marine environment. Generally, fluctuations are regarded as a nuisance to be ignored, avoided, or eliminated. However, this paper shows that fluctuations can be effectively exploited to enhance the detection of targets that fluctuate less in amplitude than the clutter and background noise. In fact, a case can be made that the exploitation of fluctuations constitutes a third dimension for achieving gain to supplement the other two well know dimensions of frequency resolution gain and array aperture gain. Results from measurements at sea are presented to support the claim that a new dimension of gain is being accessed and to demonstrate that the additional gains can be substantial.
KEYWORDS: Signal to noise ratio, Signal detection, Interference (communication), Signal processing, Solids, Statistical analysis, Acoustics, Data processing, Data modeling, Analytical research
Fluctuations are always present in underwater sound propagation, and are generally viewed as a complication in signal detection and identification. However, in some cases where the signals fluctuate less than the noise, it is possible to take advantage of the different magnitudes of fluctuations of signal and noise to improve detection. Wagstaff's integration silencing processor (WISPR) is an example of such a processor. The original version of the WISPR processor utilized power values derived from complex pressures in a given frequency bin, but ignored the phases of these complex pressures. An improved processor that takes advantage of the phase as well as the amplitude is described below. Its performance is verified using measured data, where detection has been accomplished by a margin of 4 decibels. Simulations using synthetic data show that the new processor can be effective for signal-to-noise ratios greater than minus 20 decibels.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.