Advance Tracking Algorithms to Meet Modern Threats

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.