  Bias estimation or sensor registration is an essential step in ensuring the
accuracy of global tracks in multisensor-multitarget tracking. Most previously
proposed algorithms for bias estimation rely on local measurements in
centralized systems or tracks in distributed systems, along with additional
information like covariances, filter gains or targets of opportunity. In
addition, it is generally assumed that such data are made available to the
fusion center at every sampling time. In practical distributed multisensor
tracking systems, where each platform sends local tracks to the fusion center,
only state estimates and, perhaps, their covariances are sent to the fusion
center at non-consecutive sampling instants or scans. That is, not all the
information required for exact bias estimation at the fusion center is
available in practical distributed tracking systems. In this paper, a new
algorithm that is capable of accurately estimating the biases even in the
absence of filter gain information from local platforms is proposed for
distributed tracking systems with intermittent track transmission. Through the
calculation of the Posterior Cram\'er--Rao lower bound and various simulation
results, it is shown that the performance of the new algorithm, which uses the
tracklet idea and does not require track transmission at every sampling time or
exchange of filter gains, can approach the performance of the exact bias
estimation algorithm that requires local filter gains.
