Weapon and Targeting Systems Group

Anderson, United States

Weapon and Targeting Systems Group

Anderson, United States
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Newman A.J.,Weapon and Targeting Systems Group | DeSena J.T.,Electronic Warfare Systems Group
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2013

Intelligence, surveillance, and reconnaissance (ISR) encompasses activities related to planning and operating sensors and systems that collect, process, exploit, and disseminate data in support of military operations. As the number and diversity of sensing assets continues to expand, human operators are less able to effectively manage, control, and exploit the ISR ensemble. Automated support for processing sensor data and controlling sensor assets can relieve the burden on human operators, particularly in dynamic environments, where it is essential to react quickly to information. Our approach is to apply principles of feedback control to ISR operations, "closing the loop" from sensor collections through automated processing to ISR asset control. Closed-loop collaborative ISR (CLCISR) is a feedback process that continually reallocates ISR resources to respond to changing conditions, maximize the relevance of data collected, and reduce errors and uncertainty about a tactical commander's situation of interest. APL has developed a CLCISR prototype that dynamically tasks a diverse ensemble of ISR platforms and sensors in a closed feedback loop with an upstream data fusion process that combines information to produce an accurate and current tactical picture. This article introduces the CLCISR concept and details the primary technical elements, applications, and APLs current research directions.


Newman A.J.,Weapon and Targeting Systems Group | Mitzel G.E.,APL
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2013

Upstream data fusion (UDF) refers to the processing, exploitation, and fusion of sensor data as closely to the raw sensor data feed as possible. Upstream processing minimizes information loss that can result from data reduction methods that current legacy systems use to process sensor data; in addition, upstream processing enhances the ability to exploit complementary attributes of different data sources. Since the early 2000s, APL has led a team that pioneered development of UDF techniques. The most mature application is the Air Force Dynamic Time Critical Warfighting Capability program, which fuses a variety of sensor inputs to detect, locate, classify, and report on a specific set of high-value, time-sensitive relocatable ground targets in a tactically actionable time frame. During the late 2000s, APL began expanding the application of UDF techniques to new domains such as space, maritime, and irregular warfare, demonstrating significant improvements in detection versus false-alarm performance, tracking and classification accuracy, reporting latency and production of actionable intelligence from previously unused or corrupted data. This article introduces the concept, principles, and applicability of UDF, providing a historical account of its development, details on the primary technical elements, and an overview of the challenges to which APL is applying this technology.


Murphy P.K.,Weapon and Targeting Systems Group | Rodriguez P.A.,Weapon and Targeting Systems Group | Peterson C.K.,Weapon and Targeting Systems Group
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2013

A 3-D target detection and recognition algorithm, based on the biologically inspired map-seeking circuit (MSC), is implemented to efficiently solve the template-matching problem in synthetic aperture radar (SAR) and panchromatic grayscale imagery. Given a 3-D model of a target, this algorithm locates the target in a 2-D image and determines its pose (i.e., viewing angles, scale, and spatial translations). A key aspect of the MSC is the simultaneous forward transformation of the model to match the image coupled with a backward path to make the image match the model. The efficiency of the algorithm is a result of the decomposition of the n-dimensional pose transformation space into a series of one-dimensional searches for each of the transformation parameters. Although originally designed for panchromatic electro-optical imagery, we demonstrate that the MSC architecture can also be successfully applied to SAR by simply changing the feature-extraction preprocessing Additionally we introduce modifications to the MSC algorithm that increase the speed of detection and allow efficient classification when multiple targets are present in the same image. We present promising results after applying our algorithm to challenging real-world panchromatic electro-optical and SAR imagery.


Grabbe M.T.,APL | Grabbe M.T.,Weapon and Targeting Systems Group | Hamschin B.M.,APL | Hamschin B.M.,Weapon and Targeting Systems Group
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2013

Passive geo-location of ground targets is commonly performed by surveillance air-craft using direction finding angles. These angles define the line of sight from the aircraft to the target and are computed using the response of an antenna array on the aircraft to the target's RF emissions. Direction finding angles are the inputs required by a geo-location algorithm, which is typically an extended Kalman filter or a batch processor. This modality allows a single aircraft to detect, classify, and localize ground-based signal sources. In this article, the direction finding angles used for geo-location are defined and a mathematical model is developed that relates measurements of these angles to the target's position on Earth. Special emphasis is given to the angle measurement provided by a linear antenna array. An algorithm is presented that uses iterated least squares to estimate a target's position from multiple angle measurements. Simulation results are shown for a single aircraft locating a stationary ground target.

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