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.

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The problem of decision fusion in distributed sensor system is comidered. Distributed selrsors pass their decisionr about the same hypotheses to a fusion center that cornbines them into a final decision Assuming that the semor decisions are independent from each other conditioned on each hypothesis, we provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability comists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors

Abstract-Wireless Sensor Networks (WSNs) have attracted a great deal of research interest during the last few years. Potential applications make them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Localization is a key aspect of such networks, since the knowledge of a sensor's location is critical in order to process information originating from this sensor, or to actuate responses to the environment, or to infer regarding an emerging situation etc. Indoor localization in the literature is based on various techniques, ranging from simple Received-Signal-Strength (RSS) to the more demanding Time-of-Arrival (ToA) or Directionof-Arrival (DoA) of the incoming signals. In the context of several EU research projects, various WSN platforms for indoor localization have been developed, evaluated and tested within real-world emergency medical services applications. These platforms were selected in order to deal with all principal localization techniques, namely RSSI, ToA and DoA. Deployment and real-world considerations are discussed, measurements results are presented and overall system evaluation conclusions are drawn regarding indoor localization capabilities of WSNs.

The problem of distributed detection involving N sensors is considered. The configuration of sensors is serial in the sense that the (j-1)th sensor passes its decision to thejth sensor and that the jth sensor decides using the decision it receives and its own observation. When each sensor employs the Neyman-Pearson test, the probability of detection is maximized for a given probability of false alarm, at the Nth stage. With two sensors, the serial scheme has a performance better than or equal to the parallel fusion scheme analyzed in the literature. Numerical examples illustrate the global optimization by the selection of operating thresholds at the sensors.