For several years ARL has studied acoustics to track vehicles, helicopters, Unmanned Aerial Vehicles
(UAV) and others targets of interest. More recently these same acoustic sensors were placed on a
"simulated" buoy in an attempt to detect and track aircraft over a large body of water. This report will
investigate the advantages of using acoustic arrays to track air and water craft from a fixed floating
platform as well as potential concerns associated with this technology. Continuous monitoring of aircraft
overflight will increase situational awareness while persistent monitoring of commercial and military flight
paths increases overall homeland security.
An acoustic array, integrated with an algorithm to discriminate potential Launch (LA) or
Impact (IM) events, was augmented by employing the Launch Impact Discrimination
(LID) algorithm for mortar events. We develop an added situational awareness capability
to determine whether the localized event is a mortar launch or mortar impact at safe
standoff distances. The algorithm utilizes a discrete wavelet transform to exploit higher
harmonic components of various sub bands of the acoustic signature. Additional features
are extracted via the frequency domain exploiting harmonic components generated by the
nature of event, i.e. supersonic shrapnel components at impact. The further extrapolations of these features are employed with a neural network to provide a high level of confidence for discrimination and classification. The ability to discriminate between these events is of great interest on the battlefield. Providing more information and developing a common picture of situational awareness. Algorithms exploit the acoustic sensor array to provide detection and identification of IM/LA events at extended ranges. The integration of this algorithm with the acoustic sensor array for mortar detection provides an early warning detection system giving greater battlefield information for field commanders. This paper will describe the integration of the algorithm with a candidate sensor and resulting field tests.
KEYWORDS: Acoustics, Error analysis, Detector arrays, Signal processing, Signal detection, Data modeling, Sensors, Wavefronts, Monte Carlo methods, Expectation maximization algorithms
This paper compares three methods that estimate the location of an acoustic event based on measurements
of its time-of-arrival (TOA) and direction-of-arrival (DOA) at a set of microphone arrays. We propose first a
Least-Square (LS) estimator for source location for this combined DOA-TOA measurement model. We then look
at the Maximum Likelihood (ML) estimator, comparing both estimators to the Cramer-Rao lower bound (CRB).
Our third estimator is based on the Maximum A Posteriori (MAP) formulation and is designed to handle the
association problem, where detections at different arrays must be matched if they correspond to a single event.
Simulations show that the LS estimator performs slightly better than the ML estimator when the observation
noise is not the expected one. Both methods exhibit a bias in the range estimate, which accounts for most of
the square error. The MAP estimator, applied to live fire data, was accurate and successfully resolved multiple
targets from outlier and multipath noise.
Infrasonics offers the potential of long-range acoustic detection of explosions, missiles and even sounds created by manufacturing plants. The atmosphere attenuates acoustic energy above 20 Hz quite rapidly, but signals below 10 Hz can propagate to long ranges. Space shuttle launches have been detected infrasonically from over 1000 km away and the Concorde airliner from over 400 km. This technology is based on microphones designed to respond to frequencies from .1 to 300 Hz that can be operated outdoors for extended periods of time with out degrading their performance.
The US Army Research Laboratory and Los Alamos National Laboratory have collected acoustic and infrasonic signatures of static engine testing of two missiles. Signatures were collected of a SCUD missile engine at Huntsville, AL and a Minuteman engine at Edwards AFB. The engines were fixed vertically in a test stand during the burn. We
will show the typical time waveform signals of these static tests and spectrograms for each type. High resolution, 24-bit data were collected at 512 Hz and 16-bit acoustic data at 10 kHz. Edwards data were recorded at 250 Hz and 50 Hz using a Geotech Instruments 24 bit digitizer. Ranges from the test stand varied from 1 km to 5 km. Low level and upper level meteorological data was collected to provide full details of atmospheric propagation during the engine test.
Infrasonic measurements were made with the Chaparral Physics Model 2 microphone with porous garden hose attached for wind noise suppression. A B&K microphone was used for high frequency acoustic measurements. Results show primarily a broadband signal with distinct initiation and completion points. There appear to be features present in the signals that would allow identification of missile type. At 5 km the acoustic/infrasonic signal was clearly present. Detection ranges for the types of missile signatures measured will be predicted based on atmospheric modeling.
As part of an experiment conducted by ARL, sounding rocket launches have been detected from over 150 km. A variety of rockets launched from NASA’s Wallops Island facility were detected over a two year span. Arrays of microphones were able to create a line of bearing to the source of the launches that took place during different times of the year. This same experiment has been able to detect the space shuttle from over 1000 km on a regular basis. These two sources represent opposite ends of the target size, but they do demonstrate the potential for the detection and location of rocket launches.
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