Sensor Network-Based Countersniper System Gyula Simon Miklós Maróti Ákos Lédeczi György Balogh Branislav Kusy András Nádas Gábor Pap János Sallai Ken Frampton Institute for Software Integrated Systems Vanderbilt University 2015 Terrace Place, Nashville, TN 37203, USA Phone: (+1) 615- 343-7472 e-mail: {gyula.simon, miklos.maroti, akos.ledeczi}@vanderbilt.edu ABSTRACT An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance of the proposed system is superior to that of centralized countersniper systems in such challenging environment as dense urban terrain. In this paper, in addition to the overall system architecture, the acoustic signal detection, the most important middleware services and the unique sensor fusion algorithm are also presented. The system performance is analyzed using real measurement data obtained at a US Army MOUT (Military Operations in Urban Terrain) facility. Categories and Subject Descriptors B.7.1 [Integrated Circuits]: Types and Design Styles – Algorithms implemented in hardware, C.2.2 [Computer-Communication Networks]: Network Protocols – Routing protocols, G.1.0 [Mathematics of Computing]: Numerical Analysis – Numerical algorithms, J.7. [Computer Applications]: Computers in Other Systems – Military General Terms Algorithms, Design, Measurement, Performance Keywords Sensor Networks, Middleware Services, Time Synchronization, Message Routing, Data Fusion, Acoustic Source Localization 1. INTRODUCTION Detecting and accurately locating shooters has been an elusive goal of armed forces and law enforcement agencies for a long time now. Among the several systems developed in the past decade only a few can be used in such challenging environments as urban terrain. The main problems degrading the performance of these systems are the poor coverage due to the shading effect of the buildings and the presence of multipath effects. Several physical phenomena can be used for sniper detection purposes. The Viper system built by Maryland Advanced Development Lab utilizes an infrared camera to detect the muzzle flash of the weapon [17]. It is augmented with a microphone to detect the muzzle blast for range estimation. Both sensors require direct line of sight. Other limitations include the possibility of flash suppression by the shooter and a relatively high false alarm rate that is reduced by employing two disparate sensors [21]. Another approach measures the thermal signature of the bullet in flight [21]. Illuminating the sniper’s scope with a laser and measuring the reflections can also provide accurate bearing estimates [21]. None of these approaches, however, provide a comprehensive solution to the problem. Despite the efforts of using different information sources for sniper detection, so far acoustic signals, such as muzzle blasts and shock waves provide the easiest and most accurate way to detect shots, and the majority of the existing countersniper systems use them as the primary information source [20]. The most obvious acoustic event generated by the firing of a conventional (non-silenced) weapon is the blast. The muzzle blast is a loud, characteristic noise originating from the end of the muzzle and propagating spherically away at the speed of sound. Typical rifles fire projectiles at supersonic velocities, thereby producing acoustic shocks along their trajectory [20]. Shockwaves can be used to accurately determine projectile trajectories, because the shock waveform is distinctive and cannot be produced by any other natural phenomenon. The simplified geometry of the bullet trajectory and the associated muzzle blast and shockwave fronts are shown in Figure 1. Commercial acoustic sniper detection systems use these phenomena. They measure the time of arrival (TOA) and some other characteristics of shockwaves and/or the TOA of muzzle blasts. BBN’s Bullet Ears system utilizes one or two small arrays of microphones, providing estimates of the caliber, speed and trajectory of the projectile, and also a range estimate for the shooter. The average accuracy of the azimuth and elevation estimators is approximately 1.2 and 3 degrees, respectively, while the distance estimator’s accuracy is approx. 1.6% [4]. The similar French Pilar system uses two microphone arrays achieving bearing and range accuracy of ±2° and ±10%, respectively [11]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SenSys’04, November 3–5, 2004, Baltimore, Maryland, USA. Copyright 2004 ACM 1-58113-879-2/04/0011…$5.00.The main drawback of the current centralized systems is that if some of the few sensors cannot detect the signal then the system does not have enough data to perform the localization accurately. Measurement errors can easily occur if the sensors do not have direct line-of-sight of the shooter (no muzzle blast detection) or the projectile trajectory is shaded (no shockwave detection). An even more troublesome source of error is when the sensors pick up echoes resulting in poor localization accuracy. A straightforward solution can be the utilization of many sensors providing good coverage in a large area of interest. In this way there is a high probability that multiple sensors detect the direct signal. The individual sensor measurements can be less accurate, since the measurements are independent and come from different locations; thus the sensors can be less sophisticated and much smaller. Using large number of sensors not only enhances the accuracy, but it also increases the robustness of the overall system. Based upon the above idea, we developed an experimental countersniper system called PinPtr. The system utilizes an ad hoc wireless sensor network built from inexpensive sensor nodes. After deployment, the sensor nodes synchronize