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The initialization of the state estimation in a localization scenario basedon ranging and dead reckoning is studied. Specifically, we start with acooperative localization setup and consider the problem of recursively arrivingat a uni-modal state estimate with sufficiently low covariance such thatcovariance based filters can be used to estimate an agent's state subsequently.A number of simplifications/assumptions are made such that the estimationproblem can be seen as that of estimating the initial agent state given adeterministic surrounding and dead reckoning. This problem is solved by meansof a particle filter and it is described how continual states and covarianceestimates are derived from the solution. Finally, simulations are used toillustrate the characteristics of the method and experimental data are brieflypresented.

Recursive Bayesian Initialization of Localization Based on Ranging and Dead Reckoning