Crame´r-Rao lower bound for tracking multiple targets

The derivation and computation of the theoretical Crame´r-Rao lower bounds for multiple target tracking has traditionally been considered to be a notoriously difficult problem. The authors present a simple and exact solution based on the assumption that raw sensor data (before thresholding) are available. The multi-target tracking problem can then be formulated as recursive Bayesian track-before-detect estimation. The advantage of this formulation is that it is identical to nonlinear filtering, for which the exact posterior Crame´r-Rao bound is already known. The paper presents several numerical examples in support of the theoretical findings.