Multisensor array processing of noisy measurements has received considerable attention in many areas of signal processing. The optimal processing techniques developed so far usually assume the signal and noise processes are at least wide-sense-stationary, yet a need exists for efficient, effective methods for processing nonstationary signals. While wavelets have proven to be useful tools in dea...

A robot system generally has several degrees of freedom of motions as well as different kinds of sensors. Basic behaviors are planned based on these sensors’ feedback. We take advantage of the Jacobian matrix to describe the differential relations between the sensor feedback and the motor motions. Hence, the relation between two sensors could be formulated by the two respective Jacobian matrice...

An approach of multisensor signals processing at microprocessor or 8 bit microcontrollers is showed in this paper. This approach is based on multisensor individual conversion function or individual characteristic curve identification method with using of neural networks. It allow to reduce an amount of calibration points and increase the precision of identification with comparatively to well kn...

Abstract This paper presents several approaches to realize the data association for modern multisensor data fusion systems. Therefore the requirements of data association algorithms in real multisensor data fusion systems, as applied in air traffic control or in air defence systems are discussed. Especially, the effect and possible integration methods, of the new electronically scanned array ra...

Multisensor multitarget tracking is a complex problem that has only recently received much attention. Several approaches for combining information between sensors may be taken, consisting of various association and fusion architectures. This paper examines measurement and track association and fusion, and the inherent advantages and disadvantages, and concludes that track association techniques...

The multisensor information fusion is a key issue for multisensor system. One of its difficulties lies in the switching of the state of sensor clusters. That is, which direction should the sensor information been fused into at a given moment? An algorithm of multisensor information fusion based on rough set and neural network was proposed in this paper. Firstly, the typical clustering distribut...

In this work, we present a model theory based fusion methodology for multisensor wavelet-features based recognition called Automatic Multisensor Feature-based Recognition System (AMFRS). The goal of this system is to increase accuracy of the commonly used wavelet-based recognition techniques by incorporating symbolic knowledge (symbolic features) about the domain and by utilizing a model theory...

This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence s...

This paper presents the theory and formalism of fuzzy causal probabilistic networks (FCPN) and show their current and potential applications in multisensor data fusion. A fuzzy causal probabilistic network (FCPN) is a directed acyclic graph representing the joint probability distributions of a set of fuzzy random variables describing a problem domain. FCPNs extend causal probabilistic networks ...

In this paper we describe a multiagent system in which agents negotiate to allocate resources and satisfy constraints in a real-time environment of multisensor target tracking. The agents attempt to optimize the use of their own consumable resources while adhering to the global goal, i.e., accurate and effective multisensor target tracking. Agents negotiate based on different strategies which a...