We describe the deployment of mobile agent in Distributed Sensor Networks (DSNs) to form an improved infrastructure for multisensor data fusion. Compared with the traditional client/server paradigm, mobile agent adopts a new computing model: data stay at the local site, while the execution code is moved to the data sites. Mobile-agent-based DSN (MADSN) saves the network bandwidth and provides a...

Fusion of multisensor data can improve target probability of detection but suffers from a potentially increased false alarm rate. The optimal sensor decision rule in the case of multiple sensor systems and known target location is of course a likelihood ratio test. This approach, however, is not applicable to many practical scenarios, such as sonar, in which the location of the target is not kn...

Various multisensor data fusion architectures have been utilized to support the Maritime Surveillance (MS) in maritime tactical and strategic operations. The military tactical situation is mechanized through data fusion thus improving the quality of target tracking system. One of the major problems is that the surveillance area is generally large, hence making it difficult to arrive at a feasib...

In this paper, fuzzy-based multisensor data fusion is studied and an iterative fuzzy classifier is proposed. Obtained results are given as a set of two maps: a thematic map, and a confidence map (a classification certainty map representing the degree of certainty in the thematic map). The application of this classifier using ERS-1/JERS-1 SAR composites is shown to very promising.

This paper proposed a novel method of decision fusion based on weights of evidence model (WOE). The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The r...

Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multi-source information, where one must integrate different kinds of biological data. Natural computing provides several possibilities in Bioinformatics, especially by presenting interesting nature-inspired methodologies for handling such complex problems. In this article we survey the rol...

A multisensor feature-based fusion approach to target recognition using a framework of model-theory is proposed. The Best Discrimination Basis Algorithm (BDBA) based on the best basis selection technique and the Sensory Data Fusion System (SDFS) based on logical models and theories are applied for feature extraction. The BDBA selects the most discriminant basis. The SDFS rst selects features, w...

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■ We present two examples that show how fusing data from hyperspectral imaging (HSI) sensors with data from other sensors can enhance overall detection and classification performance. The first example involves fusing HSI data with foliage-penetration synthetic aperture radar (FOPEN SAR) data; the second example involves fusing HSI data with high-resolution imaging (HRI) data. The fusion of HSI...