A gunshot location system can be implemented in complex urban environments using a distributed array of acoustic
sensors. A primary difficulty in computing the source location is that unknown path obstructions in the environment
interfere with the reception of the sound at the sensor, by blocking the sound entirely, by refracting the path, or by
creating echoes. Other complications are created by the similarity between gunshot sounds and other less interesting
urban noises, frequency-dependent absorption of sound, and possible computational difficulty when multiple
gunshots generate large data sets that stress real-time analysis routines.
The ShotSpotter Gunshot Location System®1, deployed in over two dozen cities in the United States, detects and
locates gunfire using a network of acoustic sensors placed on rooftops and utility poles, on moving vehicles, or on
personnel. This sensor network, combined with a software system to collate and compute location results from the
array of sensors, accurately locates gunshot sounds in complex urban environments. A classifier discards solutions
incorporating non-gunshot audio pulses produced by the complex environment. Examples of difficult detection
problems, including gunshots from a moving source, show that the detection and classification algorithms described
are effective at recovering useful results from signals found in real-world urban scenarios.
The ShotSpotter Gunshot Location System® has a flexible architecture that employs a wireless network of sensors
mounted on buildings, vehicles, or soldiers. These distributed arrays with redundant acoustic paths combine audio
time of arrival and/or angle of arrival from multiple sensors to calculate locations in challenging environments with
obstructions or reflections. Muzzle and bullet sounds can be used depending on the proximity of the sensors to the
bullet trajectory. Large array geometries allow not only close-range sniper detection but also wide-area situational
awareness of enemy weapon activity. Examples of acoustic detections are presented in this paper using data from a
combination of fixed and mobile sensors.