The possibilistic correlation-dependent fusion methods for optical detection (original) (raw)

Possibilistic Correlation-Dependent Fusion Methods for Optical Detection

2009

Multi sensor fusion is an important component of applications for systems that use correlated data from multiple sensors to determine the state of a system. As the state of the system being monitored and many sensors are affected by the environmental conditions changing with time, the multi sensor fusion requires a correlation-dependent approach. The behavior of this approach should vary according to the correlation parameter. In this paper, we compare our possibilistic correlation-dependent fusion approach (PCDF) with the possiblistic combiner Dempster-Shafer. We focus in this paper on the mathematical background of this approach so that it can be used in many useful applications.

Detection of Buried Landmines using the Possibilistic Correlation-Dependent Fusion Methods

2007

Multi sensor fusion is an important component of applications for systems that use correlated data from multiple sensors to determine the state of a system. As the state of the system being monitored and many sensors are affected by the environmental conditions changing with time, the multi sensor fusion requires a correlation-dependent approach. The behavior of this approach should vary according to the correlation parameter. In this paper, we compare our possibilistic correlation-dependent fusion approach (PCDF) with the possiblistic combiner Dempster-Shafer. We use time-series infrared images of landmines buried in different types of soil.

Dempster–Shafer fusion of multisensor signals in nonstationary Markovian context

EURASIP Journal on Advances in Signal Processing, 2012

The latest developments in Markov models' theory and their corresponding computational techniques have opened new rooms for image and signal modeling. In particular, the use of Dempster-Shafer theory of evidence within Markov models has brought some keys to several challenging difficulties that the conventional hidden Markov models cannot handle. These difficulties are concerned mainly with two situations: multisensor data, where the use of the Dempster-Shafer fusion is unworkable; and nonstationary data, due to the mismatch between the estimated stationary model and the actual data. For each of the two situations, the Dempster-Shafer combination rule has been applied, thanks to the triplet Markov models' formalism, to overcome the drawbacks of the standard Bayesian models. However, so far, both situations have not been considered in the same time. In this article, we propose an evidential Markov chain that uses the Dempster-Shafer combination rule to bring the effect of contextual information into segmentation of multisensor nonstationary data. We also provide the Expectation-Maximization parameters' estimation and the maximum posterior marginal's restoration procedures. To validate the proposed model, experiments are conducted on some synthetic multisensor data and noised images. The obtained segmentation results are then compared to those obtained with conventional approaches to bring out the efficiency of the present model.

Optimal data fusion strategies using multiple-sensor detection systems

Asilomar Conference on Signals, Systems & Computers, 1997

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions to a fusion center that combines the received decisions from the various sensors into a final global decision. The case where only two sensors are combined using an AND fusion rule was analyzed by Kovattana (1973), and the cases where two and three sensors

A novel multisensor integration approach for distributed detection systems with data fusion

The International Conference on Electrical Engineering, 2010

In this paper, we consider a binary distributed detection system in which a system of multiple sensors monitors a common volume and provides relevant binary decisions about the state of the environment to a data fusion center. The fusion center combines the binary decisions of the individual distributed sensors into a final global decision. We propose a new hard decision integration method for multiple sensor decision fusion systems. The proposed hard decision fusion method determines the false alarm probabilities and the detection probabilities that yield maximum performance. The performance of the proposed method is provided in case of Rayleigh distributed observations and is proved to be simple and efficient.

Information Fusion using Covariance Intersection in the Frame of the Evidence Theory

This paper presents a multisensor and multitarget tracking architecture with objects eclipses. The architecture is composed of two subsystems : a multitarget tracking for each sensor and a multitarget and multisensor fusion center. The multitarget tracking uses the Dempster-Shafer theory with proposed distributions of masses, that are functions of the distance between perceived objects and known objects, the sensor reliability and the perception uncertainty. The multisensor fusion is based on the Covariance Intersection algorithm.