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Detecting and tracking a moving ground target in radar imagery is a challenge intensified by clutter, sensor anomalies, and the substantial signature variations that occur when a target's aspect angle changes rapidly. In its GMTI mode, a radar produces range-Doppler images that contain both kinematic reports and shape features. An HRR signature, when formed as the Fourier transform of the range-Doppler image across its Doppler dimension, becomes a derived measurement and an alternative source of identity information. Although HRR signatures can vary enormously with even small changes in target aspect, such signatures were vital for associating kinematic reports to tracks in this work. This development started with video phase history (VPH) data recorded from a live experiment involving a GMTI radar viewing a single moving target. Since the target could appear anywhere in the range-Doppler image derived from the VPH data, the goal was to localize it in a small range-Doppler "chip" that could be extracted and used in subsequent research. Although the clutter in any given VPH frame generally caused false chips to be formed in the full range-Doppler image, at most one chip contained the target. The most effective approach for creating any chip is to ensure that the object is present in the return from each pulse that contributes to that chip, and to correct any phase distortions arising from range gate changes. Processing constraints dictated that the algorithm for target chip extraction be coded in MATLAB with a time budget of a few seconds per frame. Furthermore, templates and shape models to describe the target were prohibited. This paper describes the nonlinear filtering approach used to reason over multiple frames of VPH data. This nonlinear approach automatically detects and segments potential targets in the range-Doppler imagery, and then extracts kinematic and shape features that are tracked over multiple data frames to ensure that the real target is in the declared chip. The algorithm described was used successfully to process over 84,000 frames of real data without human assistance.
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