Track association and track fusion with nondeterministic targetdynamics

Representative track fusion algorithms and track association metrics are quantitatively compared using a simple linear-Gaussian-Poisson model, under various degrees of nondeterministicity of the target dynamics, i.e., process noises, and of the initial condition uncertainty. Track fusion algorithms are compared using an analytical method, while track association metrics are evaluated by Monte Carlo simulations