Covariance Reconstruction for Track Fusion with Legacy Track

The problem of track-to-track association and track fusion has been considered in the literature where the fusion center has access to multiple track estimates and the associated estimation error covariances from local sensors, as well as their crosscovariances. Due primarily to the communication constraints in real systems, some legacy trackers may only provide the local track estimates to the fusion center without any covariance information. In some cases, the local (sensor-level) trackers operate with fixed filter gain and do not have any self assessment of their estimation errors. In other cases, the network conveys a coarsely quantized root mean square (RMS) estimation error of each local tracker. Thus the fusion center needs to solve the track association and fusion problem with incomplete data from legacy local trackers. The problem of track fusion with legacy track sources which lack covariances is handled by reconstructing them using sensor covariance and target maneuvering index information and then using the appropriate association and fusion algorithms. The situation when a coarsely quantized RMS estimation error is available is also discussed. A two-sensor tracking example is used to illustrate the effectiveness of the proposed covariance reconstruction method for track fusion and compared with a centralized interacting multiple model estimator. Manuscript received February 9, 2007; revised August 14, 2008; released for publication October 23, 2008. Refereeing of this contribution was handled by Benjamin Slocumb