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Collaborative Fire Control Decision Aids

Description:

TECHNOLOGY AREA(S): Weapons 

OBJECTIVE: Develop a system to aggregate friendly force small arms fire control data to compute and display which individual or team has the highest probability of successfully engaging a target. 

DESCRIPTION: This effort supports the Army Modernization Priority of Soldier Lethality. Smart, networked small arms fire control systems are increasingly commonplace, especially with the proliferation of smartphones. Devices like the Kestrel Weathermeter[1] and the Sig Sauer KILO2400ABS laser rangefinder [2] have the capability to communicate with devices like smartphones to share data on environmental conditions, the weapon itself, and ammunition. These capabilities allow users to quickly create and edit ballistic inputs to maximize effects. In addition, there are techniques [3] that have been developed to compute the probability of hit for a small arms weapon system based upon the uncertainty in parameters like range to target, muzzle velocity, and wind speed. These techniques allow a user to provide a performance estimate for a weapon system given its ballistic parameters and the user's ability to measure and control the other factors that affect the flight of a bullet. By developing a method to connect the fire control systems to a centralized probability of hit calculator, this topic seeks to provide unit commanders with the capability to determine which asset at his disposal (e.g. infantry with an M4 or a sniper with an M110 and a laser rangefinder) would best be able to engage a given target. This would not be based only on user-provided information but would tie in actual measurements from sensors like weather meters, laser rangefinders, and weapon-mounted displays. Target/enemy information, such as enemy position and threat type, could be provided to the calculator from any number of sources, e.g., radar or individual user input. This data will provide the most accurate picture of friendly units' ability to engage threats. The integration of this data would enable a commander to evaluate the impact of moving units and threats around on a map, and to evaluate how the firing solutions and P(hit) calculations change, allowing him to determine which unit should engage each target to maximize the probability of successfully neutralizing the enemy. 

PHASE I: The objective of Phase I is to develop a system architecture and methodology for aggregating fire control data over a generic network that enables the data to be transferred and shared among systems. Document the proposed solution. Demonstrate software that couples simulated data from multiple sources with target profiles to compute a firing solution and probability of hit for each friendly asset. 

PHASE II: Phase II will build on a successful Phase I demonstration to connect physical devices to the probability of hit application and develop a user interface that presents the information on a map. The map should factor local terrain into the firing solution. The application should allow the input of enemy locations from users or from other sources. Demonstrate the capability to concurrently connect over 50 devices to the network and display their computed performance probabilities based upon the entered enemy parameters. 

PHASE III: This technology can be provided to law enforcement to help in deployment of their units in counter-sniper applications. This capability could be extended to other types of munitions, such as vehicle mounted weapons or indirect fire weapons, to help commanders better plan positioning of the units. There is also the potential for this capability to be used in the commercial market, allowing hunters to determine the best place to set up for engaging targets. 

REFERENCES: 

1: H. Chen, "Research on multi-sensor data fusion technology based on PSO-RBF neural network," 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, 2015, pp. 265-269. doi: 10.1109/IAEAC.2015.7428560

2:  http://www.nkhome.com/support/kestrel-support/kestrel-software-and-apps/kestrel-link-ballistics-for-android/

3:  https://www.sigsauer.com/store/kilo2400abs.html

4:  http://www.appliedballisticsllc.com/Articles/ABDOC115_ProbabalisticWEZ.pdf

5:  https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwj2rIKW29beAhURu1MKHebEBdgQFjABegQIBRAB&url=https%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F7422354%2F7428505%2F07428560.pdf&usg=AOvVaw0vHHR2xHluj7W8I7NB-y_F

KEYWORDS: Small Arms, Fire Control, Networked, Probability Of Hit, Sniper 

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