Detection and Localization with an Acoustic Array on a Small Robotic Platform in Urban Environments

Report No. ARL-TR-2575
Authors: Stuart H. Young and Michael V. Scanlon
Date/Pages: January 2003; 18 pages
Abstract: Sophisticated robotic platforms with diverse sensor suites are quickly replacing the eyes and ears of soliders on the complex battlefield. The U.S. Army Research Laboratory (ARL) in Adelphi, Maryland, has developed a robot-based acoustic detection system that will detect an impulsive noise event, such as a sniper's weapon firing or a door slamming, and will activate a pan tilt to orient a visible and infrared camera toward the detected sound. Once the cameras are cued to the target, on-board image processing can then track the target and/or transmit the imagery to a remote operator for navigation, situational awareness, and target detection. Such a vehicle can provide reconnaissance, surveillance, and target acquisition for soldiers, law enforcement, and rescue personnel and can remove these people from hazardous environments. ARL's primary robotic platforms contain 16-inch diameter, eight-element acoustic arrays. Additionally, a 9-inch array is being developed in support of the Defense Advanced Research Project Agency tactical mobile robot program. The robots have been tested in both urban and open terrain. The current acoustic processing algorithm has been optimized to detect the muzzle blast from a sniper's weapon and to reject many interfering noise sources such as wind gusts, generators, and self-noise. However, other detection algorithms for speech and vehicle detection/tracking are being developed for implementation on this and small robotic platforms. The collaboration between two robots, both with known positions and orientations, can provide useful triangulation information for more precise localization of the acoustic events. These robots can be mobile sensor nodes in a larger, more expansive, sensor network that may include stationary ground sensors unmanned aerial vehicles, and other command and control assets. This report documents the performance of the robot's acoustic localization, describes the algorithm, and outlines future work.
Distribution: Approved for public release
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Last Update / Reviewed: January 1, 2003