The Kernel-SME filter with false and missing measurements

The recently proposed Kernel-SME filter for multi-object tracking is a further development of the Symmetric Measurement Equation (SME) idea introduced by Kamen in the 1990s. The Kernel-SME constructs a symmetric, i.e., permutation invariant, measurement equation by transforming the measurements to a kernel mixture function. This transformation is scalable to a large number of objects and allows for deriving an efficient closed-form Gaussian filter based on the Kalman filter formulas. This work shows how the Kernel-SME approach can systematically incorporate false and missing measurements.