Introduction to SPIE Defense and Commercial Sensing conference 11746: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III
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.
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.
The Army Research Laboratory (ARL) has conducted experiments using acoustic sensor arrays
suspended below tethered aerostats to detect and localize transient signals from mortars, artillery, and small
arms fire. The airborne acoustic sensor array calculates an azimuth and elevation to the originating transient,
and immediately cues a collocated imager to capture the remaining activity at the site of the acoustic
transient. This single array's vector solution defines a ground-intersect region or grid coordinate for threat
reporting. Unattended ground sensor (UGS) systems can augment aerostat arrays by providing additional
solution vectors from several ground-based acoustic arrays to perform a 3D triangulation on a source
location. The aerostat array's advantage over ground systems is that it is not as affected by diffraction and
reflection from man-made structures, trees, or terrain, and has direct line-of-sight to most events.
There is an ongoing need for more sensitive sensors and instrumentation to monitor parameters of human physiological functions. Often it is desired that this measurement process be as non-intrusive as possible, thereby requiring lightweight, high performance sensors and data acquisition systems. In the current application it is desired to measure and extract as much information from a single non-intrusive sensor as possible to avoid encumbering mission personnel with motion-inhibiting harnesses. One particular signal processing task of interest is extraction of respiration information by analysis of heart rate. Thus, signal- processing algorithms are described which perform this task using communication, non-uniform sampling, and spectrum analysis techniques.
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