Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.

The binary signal detection problem is considered, when a distributed system of sensors operates in a decentralized fashion, i.e. local processing is performed at each sensor. Chernoff's large deviation theorem is used, and the rate of convergence of the error probability to zero is taken as a criterion. It is shown that the optimum quantizer of blocks of data under the above criterion is the likelihood ratio quantizer. A lower bound to the error probability is also developed. The monotonicity of performance with refinement of quantization is proved. The question of how many coarsely quantized sensors can replace the infinitely quantized one is also answered.>