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Target ID and Radar Backtracking of Anti-Aircraft Projectiles

Description:

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Anti-aircraft artillery (AAA) are used to protect all high value military assets on the ground. Detection of these AAAs can use to cue automatic target detection (ATR) of near-by military targets. Also, there is a critical need to detect anti-aircraft artillery (AAA) shells and future railgun projectiles and extended range artillery with rocket assisted seekers (radar and IR guided). AAA shells alone have been responsible for most aircraft losses in modern warfare. They were responsible for most of the damaged and destroyed aircraft in Desert Storm I. 

DESCRIPTION: Legacy AAAs are already lethal to 35,000 feet and will become more lethal as projectiles become smarter and as radar tracking improves. In the near future, they will be more lethal at much higher attitudes as numerous hyper-velocity technologies come online such as railguns and extended range artillery with smart seeker shells come online. These systems currently threaten fighter aircraft and unmanned aircraft vehicles (UAVs). Also, given our low-cost, penetrating UAV strategy to combat modern threats, $1-2 million surface to air missiles (SAMs) or $250,000 infrared missiles would never be used against these low cost platforms. In these scenarios, AAAs are clearly the most significant threat and they will play a more significant role in all scenarios in the near future. Plus, the Air Force could use this technology now -- in current asymmetric conflicts. DARPA's Special Ops Program Manager is interested in this technology to greatly expand the con-ops of its current AC-130 missions. There are numerous characteristics of AAAs that can be used to feed a knowledge based Bayesian decision network to detect and locate AAAs. Most AAAs have multiple gun barrels in close proximity which provide a distinct multi-path return. The radar cross section of the AAA cannon barrels will have distinctively different signatures from resonant and non-resonant frequencies when scanned with multiple frequencies. Also, the scintillation characteristics of these barrel will be very distinctive especially if the AAA has an armored plate or a radar associated with it. Most obvious, AAAs have unique topographical siting requirements that can be used to exclude large areas. All these properties together will correlate to a fixed location on the ground. In addition, the artillery shells in flight can be detected and back-tracked in order to determine or confirm target locations. Similar techniques already exist for "counter battery" artillery and mortar shell tracking systems from US Army ground based radars, but the clutter characteristic will be different for airborne radar. Novel detection schemes to exploit sparse representations and to leverage other forms of prior knowledge may also prove beneficial if justified by a proposed concept of operations. Schemes which leverage multiple receive channels on a single platform or multiple coordinated transmit/receive platforms could also be considered to improve system performance. The proposed approaches should identify and improve standard detection metrics, with a particular emphasis on obtaining high-resolution detection with low probability of false alarm. This capability would represent a substantial improvement over current state-of-the-art performance for airborne radars tasked to detect ground threats. A limited data set containing AAA systems and AAA mock-ups may be provided by the government as part of Phase II. No government materials, equipment, data, or facilities will be provided under Phase I. 

PHASE I: Survey the most prevalent AAA systems and their characteristics. Evaluate signatures of various AAA systems and determine their salient most likely operating parameters. Model the radar cross section (RCS) and scintillation statistics of most prevalent AAA systems. Determine the signal to clutter ratio for various airborne radar systems including AC-130 or X-band weather radars. Also investigate the feasibility of detecting and back-tracking AAA shells in flight in various clutter conditions. Develop a knowledge aided decision algorithm to determine the most likely AAA threat locations. 

PHASE II: Evaluate various algorithms improvements, determine operating requirements, and complete trade-off studies for various airborne radars and waveforms. Develop, demonstrate, and validate the most promising AAA detection techniques using measured data. Determine computational requirements. 

PHASE III: Construct a prototype system (hardware and analysis software) and validate in production representative environment. Determine performance parameters through experiments and prototype fabrication. Follow-on activities include specific application integration and creation of any customer-unique requirements, training and operation documentation. Develop commercialization plan and market analysis. 

REFERENCES: 

1: Summary of Desert Storm I losses (aircraft lost or damaged by AAA): http://www.rjlee.org/air/ds-aaloss/

2:  "Efficient Particle-Based Track-Before-Detect in Rayleigh Noise," Mark G. Rutten, Neil J. Gordon and Simon Maskell, Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, PO Box 1500, Edinburgh, SA 5111, Australia

KEYWORDS: Radar, Signal Processing, Nonlinear, Tracking, Particle Filter, Kalman Filter, Track Before Detection, TBD, Measurement, F-35, F-22, Anti-aircraft Artillery, Anti-aircraft Gun, Railgun 

CONTACT(S): 

David Sobota 

(937) 713-8560 

david.sobota.1@us.af.mil 

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