Description:
TECHNOLOGY AREA(S): Sensors, Weapons
OBJECTIVE: Develop and demonstrate an automatic target classifier for integration into small caliber/close-combat weapon systems that are organic to an infantry squad. The goal is to detect, classify, recognize, and identify all potential targets within the engagement range of small caliber weapon systems in a variety of environmental conditions.
DESCRIPTION: The automation of fire control technology has drastically improved probability of hit (P(h)) and reduced target engagement times for almost all weapon systems over the past century. Small caliber weapon systems have lagged behind large caliber weapon systems with such improvements due to limitations in size, weight, power, and onboard computing/processing. Modern combat-proven electro-optics have allowed major strides toward closing this gap; however, significant soldier-to-weapon interaction continues to generate considerable delivery error. Given that numerous fire control solution efforts are currently being conducted, classification, recognition, and identification of objects and targets is the next engagement sequence technology to be addressed. The process of knowing the characteristics of a target is called classification. Classification is what enables an Automatic Target Recognition (ATR) system to distinguish targets between non-targets, including identification from background noise, clutter, and cover. The latest developments in optical sensors provide the technical capability to automatically identify and track potential targets. Coupled with advancements in software-based computing, it is feasible to automatically acquire, track, and identify moving and stationary targets with a sensor subsystem that is integrated with a small caliber weapon system. The primary value added to a weapon system that utilizes ATR is engagement timeline reduction for target(s) acquisition.
PHASE I: The offeror will investigate various target classifier techniques and their applicability to be used on small caliber/close combat weapon systems that are organic to the infantry squad. As noted in the description section of this topic area: "Small caliber weapon systems have lagged behind large caliber weapon systems with such improvements due to limitations in size, weight, power, and onboard computing/processing capabilities." The target classifiers evaluated shall take into account the target sets most encountered (notably human and vehicle targets), environmental factors such as cold, rain, fog, day/night, etc., and engagement scenarios in urban, jungle canopy, and open terrain environments. The ability to further classify human targets with regards to the combatant employing body armor would be an additional desired performance requirement. The architecture developed will detail hardware trade-offs based on computing processing requirements in terms of size, weight, and power of required sensor suite(s), and performance trade-offs based on operational use. For example, target classifiers will only provide a confidence level of a certain percentage when used in rain/fog scenarios with this type of sensor. The Phase I report will encompass the architecture stated above, an experiment test design for use in a modeling and simulation environment, any notable spin off applications of the technology that can be applied to the commercial sector, and a detailed research plan to develop and demonstrate a Phase II proof-of-concept/prototype ATR algorithm for small arms systems.
PHASE II: Develop and demonstrate the approach developed during Phase I, integrate such approach into a small caliber weapon system and test in both virtual and operationally relevant environments. The Phase II demonstrations should operate in all environments during both day and night. The Phase II final delivery should include: • Functional and software performance specifications for ATR small arms systems; • Demonstrated ATR algorithm design that details classifiers design (executable and source code); • Identification of key technical challenges (e.g., interface requirements for cameras applying Snell test, frame rates for target tracking, etc.) affecting system performance potentials.
PHASE III: The offeror will work with available funding sources to transition capability into practical use within Army/DoD open architecture interfaces, while consider options for dual use applications in broader domains that include the Department of Homeland Security agencies and state/local law enforcement agencies. Identify and generalize open architecture interface requirements that includes other compatible sensor platforms. Perform trade study for other tactical applications such as perimeter control, base defense, maritime target sets, etc.
REFERENCES:
1: Ratches, James A. (2011). Review of Current Aided/Automatic Target Acquisition Technology for Military Target Acquisition Tasks. Optical Engineering, 50(7).
KEYWORDS: Automatic Target Recognition (ATR), Automatic Target Classifiers, Clutter, Target Classification
