A minimax estimation fusion in distributed multisensor systems is proposed, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown and the normalized estimation errors of local sensors are norm bounded. The proposed estimation fusion is called as the Chebyshev fusion estimation (CFE) because its geometrical interpretation is in coincidence with the Chebyshev center, which is a nonlinear combination of local estimates. Theoretically, the CFE is better than any local estimator in the sense of the worst-case squared estimation error and is robust to the choice of the supporting bound. The simulation results illustrate that the proposed CFE is a robust fusion in localization and tracking and more accurate than the previous covariance intersection (CI) method. 1. Introduction Multi-sensor networks have received an increasing attention in recent years, due to their huge potential in applications, such as communication, signal process, routing and sensor management, and many other areas. In this paper, we focus on a specific and simple estimation fusion model in a distributed multi-sensor system, which is in fact a two-level optimization in the estimation fusion. Every sensor first optimally estimates the state of target based on its own measurements and then transmits its estimate to the fusion center. The problem of estimation fusion is to find an optimal state estimator based on all the received local estimates. Although the centralized fusion which directly makes use of all measurements from the local sensors in time is theoretically the best fusion strategy, sometimes communication or reliability constraints make it impossible to transmit all the sensor measurements to a fusion center. In contrast, the distributed fusion which only needs to fuse all received local estimates has the advantages of lower communication requirements, improved robustness, and so forth. However, the fusion algorithms in distributed system have to deal with troubles that do not exist in centralized fusion. One of the difficulties is that the errors of local estimates to be fused are generally correlated, and as a result the distributed fusion cannot be achieved by a standard centralized algorithm such as the Kalman filter. The reasons of this correlation may be a common process noise in target when the state estimates are not fused at each sampling instant, or common prior information in the estimates from previous communication. Over the last two decades, much research has been performed on distributed fusion
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