On track fusion with communication constraints

Distributed Kalman filters are often used in multisensor target tracking where the fusion center receives local estimates and fuses them to obtain the global target state estimate. With such a fusion architecture, each local tracker can communicate less frequently with the fusion center than the local filter update rate. The global target state estimate via track fusion is usually less accurate than that of the centralized estimator when local estimation errors are correlated and local trackers communicate to the fusion center with bandwidth constraints lower than the measurement rate. This paper focuses on the tradeoff between bandwidth and tracking accuracy for track fusion with communication constraints. We show that the performance degradation increases for track fusion on demand compared with the centralized estimator as the number of local trackers increases. We relate the steady state analysis of track fusion under bandwidth constraints to noisy Wyner-Ziv source coding problem and compare our results with the theoretical rate distortion curve of the quadratic Gaussian CEO problem. We conclude that track fusion on demand is a side-information unaware strategy while the awareness of the correlated estimation errors at each local tracker can improve the track fusion accuracy significantly.