Bias estimation for practical distributed multiradar-multitarget tracking systems

Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most algorithms previously proposed 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 real distributed multisensor tracking systems, where each platform sends locals 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 accurate bias estimation even in the absence of filter gain information from local platforms, is proposed for distributed tracking systems with irregular track transmission rates. 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.