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Air Surveillance Radar Classification Improvement


TECHNOLOGY AREA(S): Electronics, 

OBJECTIVE: Develop and demonstrate an advanced target classification algorithm that will allow a ground-based Multi-Mission Radar to discriminate among UAS, manned aircraft, and clutter (i.e., birds, vehicles, ground clutter, etc.) by using signatures, target track characteristics, and other features. 

DESCRIPTION: With the emerging threat of Unmanned Aerial Systems (UAS), the ARMY is enhancing its ground-based Counter Target Acquisition (CTA) Radars with the ability to detect and track UAS. These ground-based Multi Mission Radars (MMR) are able to detect and track airborne objects, such as rockets, artillery, mortars, UAS, manned aircraft, and clutter (i.e., birds, vehicles, etc.). The MMR radars are currently limited in their ability to accurately discriminate among UAS, manned aircraft, and clutter. Misclassifying targets can cause an incorrect responsive action by U.S. forces in critical situations. Significantly improving the classification accuracy of MMR radars is a high Army priority. The main purpose of this effort is to investigate, develop, and demonstrate a classification algorithm that uses target features to discriminate between UAS, manned aircraft, and clutter with high confidence. 

PHASE I: Identify candidate algorithms that address the challenge described in the objective section of this document. Investigate current classifier functionality, develop more suitable parametric models, conduct studies on candidate algorithms (size, power consumption, speed, and complexity, relative to available computer processing resources), investigate high resolution waveforms and the frequency at which they should be scheduled to be effective, and perform laboratory testing on viability of candidate models and algorithms. At end of Phase I, prepare and present a study report to do the following: (1) identify algorithms that improve classification, (2) provide process and schedule for productization into the software baseline, and (3) demonstrate a plan for Phase II. 

PHASE II: Develop and demonstrate improvements to classify UAS, manned aircraft, and clutter into target type and sub-type categories using previously collected Radar Data and during Live Test Events at Yuma Proving Ground, AZ utilizing current ground based ARMY radar systems. 

PHASE III: Productization of improvements into the software baseline: provide analysis, design updates, implementation support, and systems engineering testing for proposed algorithmic updates developed under Phase I and demonstrated in Phase II. Additionally, update the software and firmware to accommodate the final design and provide the following: software source code and executable files, system/subsystem specification updates, and performance specification document updates. Lastly, prepare lab tests, engineering test plans, and procedures to demonstrate the performance of the algorithms during a test event. Effective deployment of this advanced classifier may serve to enhance the performance of current and future ARMY Air Surveillance and Multi-Mission radar systems such as the AN/TPQ-50 and AN/TPQ-53. Both Programs of Record are funded annually for modernization efforts, commonly referred to as Modernization Development Efforts (MDE), which provide a conduit for the integration of improved hardware and emerging software algorithms. This includes initial design and development efforts, laboratory design, productization into the software baseline, and field testing. The general classifier approach also has applicability in non-military radar systems. The same algorithm improvements planned for DoD Radar Systems can be utilized by FAA radar systems and commercial systems to help with bird strike avoidance and UAS detection around airports. 


1: Bilik, Igal, Joseph Tabrikian, and Arnon Cohen. "GMM-based target classification for ground surveillance Doppler radar." IEEE Transactions on Aerospace and Electronic Systems 42.1 (2006): 267-278.

KEYWORDS: Multi-Mission Radar, Target Classification 

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