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The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential

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Information fusion is essential for the retrieval of desired information in a sufficiently precise, complete, and robust manner. The Bayesian approach provides a powerful and mathematically funded framework for information fusion. By local Bayesian fusion approaches, the computational complexity of Bayesian fusion gets drastically reduced. This is done by a concentration of the actual fusion task on its probably most task relevant aspects. In this contribution, further research results on a special local Bayesian fusion technique called focussed Bayesian fusion are reported. At focussed Bayesian fusion, the actual Bayesian fusion task gets completely restricted to the probably most relevant parts of the range of values of the Properties of Interest. The practical usefulness of focussed Bayesian fusion is shown by the use of an example from the field of reconnaissance. Within this example, final decisions are based on local significance considerations and consistency arguments. As shown in previous publications, the absolute values of focussed probability statements represent upper bounds for their global values. Now, lower bounds which are obtained from the knowledge about the construction of the focussed Bayesian model are proven additionally. The usefulness of the resulting probability interval scheme is discussed

Bayesian statistics leads to a powerful fusion methodology, especially for the fusion of heterogeneous information sources. If fusion problems are handled under consideration of the full expressiveness and the full range of methods provided by Bayesian statistics, the Bayesian fusion methodology possesses an impressive wide range of applications. We discuss this by having a closer look at selected aspects of Bayesian modeling. Thereby, also parallels to other methods used for information fusion will be drawn. With regard to the practical tractability of Bayesian fusion problems, selected approaches to deal with its potentially high complexity are discussed

Focussed Bayesian fusion reduces high computational costs caused by Bayesian fusion by restricting the range of the Properties of Interest which specify the structure of the desired information on its most task relevant part. Within this publication, it is concisely explained how Bayesian theory and the theory of statistical evidence can be combined to derive meaningful focussed Bayesian models and to rate the validity of a focussed Bayesian analysis quantitatively. Earlier results with regard to this topic will be further developed and exemplified.

Bayesian theory delivers a powerful theoretical platform for the mathematical description and execution of fusion tasks, especially if the information delivering sources are of heterogeneous nature. However, the complexity of Bayesian fusion tasks increases exponentially with the number of sources. A new agent based architecture that is modelled on a successful operating process of the real world, namely criminalistic investigation, circumvents the high computational costs by realizing a local fusion approach. In analogy to criminalists, software agents shall be appointed to perform heterogeneous fusion tasks. In this paper, we give an overview over this concept and the potentials that are provided by it. Then, we focus on translating the proposed ideas into a formal mathematical notation