This paper addresses multi-sensor collaboration for the detection of weak and noisy signals using wireless sensor networks. The proposed scheme is targeted for the detection of spatially correlated signals, which is common in many sensor network applications. It applies a clustering measure for improving the detection performance with multi-sensor fusion that is traditionally performed by using...

Pixel level image fusion refers to the processing and synergistic combination of information gathered by various imaging sources to provide a better understanding of a scene. We formulate the image fusion as an optimization problem and propose an information theoretic approach in a multiscale framework to obtain its solution. A biorthogonal wavelet transform of each source image is first calcul...

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error cova...

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle’s position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satell...

Sensor data fusion refers to technological solutions aiming at collecting, classifying and complementing data coming from multiple sensors. It has the potential of enabling context awareness which, on the other hand, represents a huge potential to be exploited in the field of IoT applications. Sensor fusion and IoT have to deal with multi-faced issues like heterogeneity, sensor/actuator managem...

Presently, a data fusion contest is conducted to compare the potential of airborne SAR with optical sensors for mapping applications. The goal of the test is to answer two questions: (1) Can state-of-the-art airborne SAR compete with optical sensors in the mapping domain? (2) What can be gained when SAR and optical images are used in combination, i.e., when methods for information fusion are ap...

Due to the falling price and availability of sensors, information capture and processing at a realtime or soft realtime rate is emerging as a dominating application space. This class includes interactive multimedia, robotics, security and surveillance applications and many more. A common denominator of these applications is fusion of data gathered by various sensors and data aggregators. In thi...

The information collected through the airborne remote sensing are useful for many scientific and practical works. In order to use collected information and existing knowledge of the observed object in the most efficient way for the purpose of solving specific problems, we have to examine their characteristics and if possible fuse them. This article is motivated by the existence of two operative...

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion include measurement fusion and state fusion. This paper gives a simple a review of fusion and state fusion, and second...

Sensor networks consist of sensor nodes with small-size, low-cost, lowpower, and multi-functions to sense, to process and to communicate. Minimizing power consumption of sensors is an important issue in sensor networks due to limited power in sensors. Clustering is an efficient way to reduce data flow in sensor networks and to maintain less routing information. In this paper, we propose a multi...