Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview...

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 that the signal and noise processes are at least wide sense stationary, yet a need exists for efficient, effective methods for processing nonstationary signals. Although wavelets have proven to be useful tool...

This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a systemlevel, “top-down,” direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finite-set statistics provides powerful new conceptual and computational methods for dealing with multisensor-mul...

Theory of Evidence is a mathematical theory which allows one to reason with uncertainty and which suggests a way for combining uncertain data. This is the reason why it is used as a basic tool for multisensor data fusion in situation assessment process. Although numerician people know quite well this formalism and its use in multisensor fusion, it is not the case for people used to manipulate l...

A single-sensor single-target Mixture Reduction (MR) data association algorithm is extended for use in multisensor multitarget tracking situations. MR is extended for tracking an arbitrary number of targets using an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. Like the single-sensor single-target MR algorithm, which gives be...

Interest has been growing in the use of multiple sensors to increase the capabilities of intelligent systems. The issues involved in integrating multiple sensorsinto the operation of a system are presented in the context of the type of information these sensors can uniquely provide. The advantages gained through the synergistic use of multisensory information can be decomposed into a combinatio...

This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against...

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...

A method of individual conversion characteristic identification of multisensor using reduced number of its calibration/testing results is described in this paper. The proposed method is based on the neural-based reconstruction (approximation or prediction) of surface points of multisensor conversion characteristic. Each neural network module reconstructs separate point of the surface. Our resul...

1 Multisensor Data Fusion for Next Generation Distributed Intrusion Detection Systems Tim Bass ERIM International & Silk Road Ann Arbor, MI 48113 Abstract| Next generation cyberspace intrusion detection systems will fuse data from heterogeneous distributed network sensors to create cyberspace situational awareness. This paper provides a few rst steps toward developing the engineering requiremen...