Bias estimation or sensor registration is an essential step in ensuring theaccuracy of global tracks in multisensor-multitarget tracking. Most previouslyproposed algorithms for bias estimation rely on local measurements incentralized systems or tracks in distributed systems, along with additionalinformation like covariances, filter gains or targets of opportunity. Inaddition, it is generally assumed that such data are made available to thefusion center at every sampling time. In practical distributed multisensortracking systems, where each platform sends local tracks to the fusion center,only state estimates and, perhaps, their covariances are sent to the fusioncenter at non-consecutive sampling instants or scans. That is, not all theinformation required for exact bias estimation at the fusion center isavailable in practical distributed tracking systems. In this paper, a newalgorithm that is capable of accurately estimating the biases even in theabsence of filter gain information from local platforms is proposed fordistributed tracking systems with intermittent track transmission. Through thecalculation of the Posterior Cram\'er--Rao lower bound and various simulationresults, it is shown that the performance of the new algorithm, which uses thetracklet idea and does not require track transmission at every sampling time orexchange of filter gains, can approach the performance of the exact biasestimation algorithm that requires local filter gains.
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