A peer-to-peer collaboration framework for multisensor data fusion in resource-rich radar networks is presented. In the multi-sensor data fusion, data needs to be combined in such a manner that the real-time requirement of the sensor application is met. In addition, the desired accuracy in the result of the multi-sensor fusion has to be obtained by selecting a proper set of data from multiple r...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

the purposes of human/machine perception, and for further Abstract: Image processing techniques primarily focus image-processing tasks such as segmentation, object upon enhancing the quality of an image or a set of detection or target recognition in applications such as images and to derive the maximum information from remote sensing and medical imaging. For example, visiblethem. Image Fusion i...

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor fusion framework (RTSFF) based on distributed intelligent sensor network. The RTSFF ...

This paper focuses on multi-sensor fusion for navigation in difficult environments where none of the existing navigation technologies can satisfy requirements for accurate and reliable navigation if used in a stand-alone mode. A generic multi-sensor fusion approach is presented. This approach builds the navigation mechanization around a self-contained inertial navigator, which is used as a core...

Multi-biometrics, or the fusion of more than one biometric modality, sample, sensor, or algorithm, is quickly gaining popularity as a method of improving biometric system performance and robustness. Despite the recent growth in multibiometrics research, little investigation has been done to explore the possibility of achieving multi-modal fusion from a single sensor. This approach to multi-biom...

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...

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Sensors, which monitor the surrounding environment in order to enhance our decisions, play a major role in our lives and contribute to our actions. A single sensor, however, is not capable of providing enough information; therefore, multiple sensors have to be integrated in a way to perform the additional task of interpretation, which may be more helpful and informative than what can be observe...

Advanced driver assistance systems (ADAS) have increasing demand for several sensor systems. Fusing multi-sensor data enlarges the field of view and increases the certainty and precision of the estimates. A crucial part of a fusion system is the data association, which requires data synchronization. The major synchronization strategies for data fusion are discussed and contrasted with respect t...