Modern multitarget-multisensor tracking systems involve the development of reliable methods for the data association and the fusion of multiple sensor information, and more specifically the partioning of observations into tracks. This paper discusses and compares the application of Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) methods to the fusion of multiple sensor att...

Data gathered from multiple sensors can be effectively fused for accurate monitoring of many engineering applications. In the last few years, one most sought after applications multisensor fusion has been fault diagnosis. Dempster–Shafer Theory Evidence along with Dempster’s Combination Rule is a very popular method which successfully applied to But if information obtained different shows high ...

Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to...

Bias estimation is a prerequisite to data fusion in ATC environments. Radar networks and aircraft transponders have different bias terms which should be cancelled in order to perform a correct multisensor tracking. In this paper, several general architectures to overcome these problems will be proposed, taking into account both radar and aircraft biases. Its performance, both in terms of accura...

This paper studies the design of data fusion algorithm for asynchronous system with integer times sampling. Firstly, the multisensor asynchronous samplings is mapped to the basic axis, accordingly a sampling sequence of single sensor can be taken. Secondly, aiming at the sensor with the densest sampling points, the modified parallel filtering is given. Afterwards, the sequential filtering fusio...

* Corresponding author. ** This work was supported in part by the Research Department of the Government of French Polynesia. Abstract The accuracy of forest classification is generally improved by multisensor data fusion since tree species identification benefits from complementary information. However, hypothesizing multisource fusion can also deteriorate accuracy when a non-relevant source is...

Today many tracking and surveillance systems show multi sensor configurations, which are used to enhance the breadth of measurement and likewise to increase the capability of the system to survive if any individual sensor fails. Currently, multi sensor systems rely on a central processor where global data fusion takes place, or a central communication medium through which all messages between s...

Multisensor distributed fusion Wiener deconvolution estimator is presented in this paper. It does not need to solve the Diophantine equation, and the steady-state Kalman filter gain of the augmented system. It can handle processing of nonstationary signal. White noise estimator and A strom predictor are used in the algorithm. Gevers-Wouters (G-W) algorithm is also used in this paper. In order t...

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the synthetic aperture radar (SAR) interferometry, where a pair of antenna...