Data modeling for nonlinear track prediction of targets through obscurations

Holger Jaenisch; James Handley

doi:10.1117/12.914845

15 May 2012 Data modeling for nonlinear track prediction of targets through obscurations

Holger Jaenisch, James Handley

Proceedings Volume 8393, Signal and Data Processing of Small Targets 2012; 83930P (2012) https://doi.org/10.1117/12.914845
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

A novel algorithm for predicting target tracks through obscurations is introduced. This prediction method uses radar ground track indicators and the hidden transfer function (HTF) to predict future target locations. The HTF method is described in detail, and results provided that quantify track accuracy, forecast accuracy, and the percentage of tracks exiting an obscuration occurring that occur within the forecasted region. Five different classifier methods are shown for labeling short segments of track history. Each classifier method is scored and significance testing used to determine that the Data Model and SMART lookup table (LUT) were significantly better than the other classifier approaches.

Citation Download Citation

Holger Jaenisch and James Handley "Data modeling for nonlinear track prediction of targets through obscurations", Proc. SPIE 8393, Signal and Data Processing of Small Targets 2012, 83930P (15 May 2012); https://doi.org/10.1117/12.914845

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