15 April 2010 Association ambiguity management in mixed data dimension tracking problems
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Association and fusion of passive direction finding (DF) reports with active radar tracks from airborne targets is challenging because of the low dimensionality of the common kinematic measurement space. Often, multi-target scenarios lead to significant data association ambiguity. Classically, the approach to this problem is a simple hypothesis test wherein a batch of DF sensor measurements is associated with either zero or one of the radar tracks; assignment of multiple DF tracks to a single radar track is allowed without regard to compatibility, and this can lead to detrimental results. This paper develops a new approach for managing the ambiguity. The problem is formulated as a two-dimensional assignment, and any association ambiguity is determined from the k best solutions. Firm association decisions are made only when the ambiguity is at an acceptable level. The ambiguity information is also available in real time as an output to the system operator. An improved batch association score, relative to previous works, is formulated that addresses statistical correlations between individual measurement-to-track residuals; this new score is a likelihood ratio generated from Kalman Filter residuals. Where previous scoring methods lead to incorrect ambiguity assessments in certain scenarios, the new approach yields accurate results. Because the score is recursive, the batch may be extended over an arbitrary number of measurements, helping to manage association ambiguities over time. Simulation results are shown to demonstrate the algorithm.
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James R. Thornbrue, James R. Thornbrue, J. Nate Knight, J. Nate Knight, Benjamin J. Slocumb, Benjamin J. Slocumb, } "Association ambiguity management in mixed data dimension tracking problems", Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 76980M (15 April 2010); doi: 10.1117/12.851177; https://doi.org/10.1117/12.851177

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