The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking

Eugen Ernst, Florian Pfaff, Uwe D. Hanebeck, Marcus Baum

Abstract: Different objectives and paradigms exist for tracking multiple targets when measurements do not contain information about the target identities (IDs). The Symmetric Measurement Equation (SME) filter can be used when one is agnostic to the labels and does not attempt to assign different IDs to the different targets. We present an extension of the Kernel-SME filter that, unlike the original variant, uses adaptive kernel widths that depend on the respective uncertainty. In our evaluation, it outperformed existing SMEbased approaches, while it is only second to a more complex global nearest neighbor tracker.