This paper addresses multisensor collaboration for the detection of weak and noisy signals using a network of wireless sensors. We propose a scheme that is designed to detect spatially correlated signals, which is common in many sensor network applications. The proposed scheme allows sensor nodes to automatically form collaborative teams to combine multisensor information for efficient detectio...

Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than coul...

Multisensor data fusion has found widespread application in industry. The objective of data fusion is to provide an improved estimate of a state from a set of data provided by different sensors. Among the various multisensor approaches, Kalman filtering is one of the most significant. Reliability of the data provided by the sensor is a key factor for the integrity of the fusion process. Kalman ...

In this study, a fuzzy-based multisensor data fusion classifier is developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial Fuzzy Membership Maps (FMM) to different thematic classes are first calculated using classes and sensors a priori...

ÐWe present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the ...

As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion str...