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Advance Tracking Algorithms to Meet Modern Threats

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-14-M-1775
Agency Tracking Number: F141-186-0814
Amount: $149,942.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF141-186
Solicitation Number: 2014.1
Timeline
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-05-28
Award End Date (Contract End Date): 2015-02-23
Small Business Information
100 Wall Street
Princeton, NJ 08540-
United States
DUNS: 096845169
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kathleen Paget
 Senior Scientist
 (609) 921-3892
 kmp@scitec.com
Business Contact
 James Lisowski
Title: CEO
Phone: (609) 921-3892
Email: jjl@scitec.com
Research Institution
 Stub
Abstract

ABSTRACT: Modern and emerging threats pose a challenge for current fighter radars paired with traditional tracking algorithms, requiring novel algorithmic solutions. Standard Kalman Filter (KF) algorithms are based on linear motion and noise models. The high-alpha maneuvers and low, scintillating RCS signature presented by modern supermaneuverable fighter and UAVs can defeat these assumptions. To extend the capabilities of air-to-air tracking radars, SciTec proposes to develop and optimize advanced tracking algorithms (ATAs) for non-linear tracking including Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) algorithms. The ATAs will be tuned to maximize performance against highly maneuverable, low-SNR, and scintillating RCS targets using a combination of simulated and target-injected ambient data. BENEFIT: This proposed product will result in algorithmic kernels for the ATAs, the clutter and target simulator, and analysis tools, which will form the basis of a future tool for end-to-end performance evaluation of advanced trackers paired with user-defined radar architectures. Building upon existing non-linear tracking approaches that have been developed and tested for autonomous processing of low-SNR targets in Overhead Persistent Infra-Red (OPIR) data this project extends them to maneuverable targets in air-to-air tracking radar data. These algorithms address the limitations of KF trackers using techniques ranging from local linearization to multi-hypothesis track-before-detect. The proposed effort will provide engineers and acquisition managers critical information needed to decide upgrade paths for the systems employed by next generation fighters.

* Information listed above is at the time of submission. *

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