4 August 2000 Track and bias estimation without data association
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Abstract
Previous nonlinear filtering research has shown that by directly estimating the probability density of the target state, weak and closely spaced targets can be tracked without performing data association. Data association imposes a heavy burden, both in its design where complex data management structures are required and in its execution which often requires many computer cycles. Therefore, avoiding data association can have advantages. However, some have suggested that data association is required to estimate and correct sensor biases that are nearly always present so avoiding it is not a practical option. This paper demonstrates that target numbers, target tracks, and sensor biases can all be estimated simultaneously using association-free nonlinear methods, thereby extending the useful range of these methods while preserving their inherent advantages.
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Stanton Musick, Keith D. Kastella, "Track and bias estimation without data association", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395091; https://doi.org/10.1117/12.395091
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