Multi-sensor Data Fusion Problem Research Articles

A Networked Method for Multi-Evidence-Based Information Fusion.

Dempster-Shafer evidence theory is an effective way to solve multi-sensor data fusion problems. After developing many improved combination rules, Dempster-Shafer evidence theory can also yield excellent results when fusing highly conflicting evidence. However, these approaches still have deficiencies if the conflicting evidence is due to sensor malfunction. This work presents a combination method by integrating information interaction graph and Dempster-Shafer evidence theory; thus, the multiple evidence fusion process is expressed as a network. In particular, the credibility of each piece of evidence is obtained by measuring the distance between the evidence first. After that, the credibility of the evidence is evaluated, keeping the unreliable evidence out of the information interaction network. With the fusion of connected evidence, the accuracy of the fusion result is improved. Finally, application results show that the presented method is effective.

A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

Multi-sensor data fusion (MSDF) problems have attracted widespread attention recently. However, it is still an open issue about how to make the fusion process effectively even if the collected data conflict due to several unpredictable reasons. Moreover, most existing approaches mainly concentrated on the distinction of evidence sources, which cannot well consider the feature of individual belief degree and the associated preference of decision-makers. To address such an issue, a dynamic MSDF method based on evidence theory and weighted ordered weighted averaging (WOWA) operator is proposed in this study. A numerical example is analyzed to demonstrate its whole calculation procedure. Two simulation experiments, composed of a motor rotor fault diagnosis and an insulator string target recognition application, are also mentioned to illustrate its effectiveness and applied value. The results show that the proposed methodology can enhance the fusion accuracy in the constrained scenarios with the consideration of preference relation.

A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks.

In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.

An Integrated Model for Robust Multisensor Data Fusion

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 synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM). Compared to some universal classification methods, the proposed one can be directly applied in multisensor data fusion applications, but not only for conventional classifications. Experimental results demonstrate that the proposed model is able to yield robust and reliable results in multisensor data fusion problems. In addition, this paper also draws some meaningful conclusions, which have significant implications for future studies.

Multi-sensor data fusion using support vector machine for motor fault detection

Motor fault diagnosis in dynamic condition is a typical multi-sensor data fusion problem. It involves the use of information collected from multiple sensors, such as vibration, sound, current, voltage, and temperature, to detect and identify motor faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, the multi-sensor based motor fault diagnosis can be viewed as the problem of evidence fusion. In this article we propose and investigate a hybrid method for fault signal classification based on sensor data fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance. Finally, we compare the performance of the proposed system against some other standard fault classification techniques.

An Improved Radius of Gyration Similarity Measure between Generalized Fuzzy Numbers and Its Application to Multisource Data Fusion

The degree of similarity between fuzzy numbers plays an important role in fuzzy information fusion. In this paper, improved ROG-based similarity measure developed from the current ROG method is presented. It is shown that the new method can overcome some shortcomings of the existing similarity measures. Based on the proposed similarity measure, a more flexible and intelligent algorithm is developed to deal with multisensor data fusion problems. A numerical example is used to illustrate the efficiency of the proposed method.

Optimization of Generalized Mean-Square Error in Noisy Linear Estimation

A class of least squares problems that arises in linear Bayesian estimation is analyzed. The data vector ${\bf y}$ is given by the model ${\bf y} = {\bf P}({\bf H}\bm{\theta} + \bm{\eta}) + {\bf w}$, where ${\bf H}$ is a known matrix, while $\bm{\theta}$, $\bm{\eta}$, and ${\bf w}$ are uncorrelated random vectors. The goal is to obtain the best estimate for $\bm{\theta}$ from the measured data. Applications of this estimation problem arise in multisensor data fusion problems and in wireless communication. The unknown matrix ${\bf P}$ is chosen to minimize the expected mean-squared error ${\bf E}(\|\bm{\theta} - \hat{\bm{\theta}}\|^2)$ subject to a power constraint “trace $({\bf P}{\bf P}^*) \le P$,” where $\hat{\bm{\theta}}$ is the best affine estimate of $\bm{\theta}$. Earlier work characterized an optimal ${\bf P}$ in the case where the noise term $\bm{\eta}$ vanished, while this paper analyzes the effect of $\bm{\eta}$, assuming its covariance is a multiple of ${\bf I}$. The singular value decomposition of an optimal ${\bf P}$ is expressed in the form ${\bf V}\bm{\Sigma}\bm{\Pi}{\bf U}^*$ where ${\bf V}$ and ${\bf U}$ are unitary matrices related to the covariance of either $\bm{\theta}$ or ${\bf w}$, and singular vectors of ${\bf H}$, $\bm{\Sigma}$ is diagonal, and $\bm{\Pi}$ is a permutation matrix. The analysis is carried out in two special cases: (i) ${\bf H} = {\bf I}$ and (ii) covariance of $\bm{\theta}$ is ${\bf I}$. In case (i), $\bm{\Pi}$ does not depend on the power P. In case (ii), $\bm{\Pi}$ generally depends on P. The optimal $\bm{\Pi}$ is determined in the limit as the power tends to zero or infinity; a good approximation to an optimal $\bm{\Pi}$ is found for general P.

Incremental learning with balanced update on receptive fields for multi-sensor data fusion.

This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm. Experiments of a two-camera unified positioning system are implemented successfully to test the proposed computation structure and algorithms.

"Statistics 101" for multisensor, multitarget data fusion

This tutorial summarizes the motivations, concepts, techniques, and applications of finite-set statistics (FISST), a system-level, "top-down" direct generalization of ordinary single-sensor, single-target engineering statistics to the multisensor, multitarget realm. FISST provides powerful new conceptual and computational methods for dealing with multisensor, multitarget, and multi-evidence data fusion problems. The paper begins with a broad-brush overview of the basic concepts of FISST. It describes how conventional single-sensor, single-target formal Bayesian modeling is carefully extended to general data fusion problems. We examine a simple example: joint detection and tracking of a possibly non-existent maneuvering target in heavy clutter. The tutorial concludes with a commentary on certain criticisms of FISST.

Data Fusion for Management of Outdoor Stored Grain

The problem of detecting the status of outdoor stored grain whose possible status has been revealed by multi-sensor measurement and simulation model is known as the “multi-sensor data fusion problem about management of outdoor stored grain. The objective of this paper is to develop and test a model for solving the multi-sensor data fusion problem. To do so, an analytical model is developed with existing relationship among some of the physical factors that determine the status of outdoor stored grain. The model is tested with a typical application.

Multi-sensor fusion for steerable four-wheeled industrial vehicles

This paper addresses the problem of multi-sensor data fusion in the navigation of a steerable four-wheeled industrial autonomous vehicle, which experiences substantial load variations of up to twice its weight. The practical considerations in the implementation of the filter are discussed. It aims to achieve a robust fusion algorithm with increased system tolerance against prolonged periods when absolute position updates are missing by improving estimation accuracy during dead-reckoning. The main contributions of this paper include the development of an adaptive estimator based on the extended Kalman filter to realise the multi-model filtering; the representation of the vehicle plant using a modified kinematic model to effectively describe the side-slip bias; the processing of redundant measurements to improve system immunity against noisy observations; and the ability to cope with periodically available odometry measurements and temporary position corrections from a landmark-based local reference system. To allow better adaptation to tyre wear and the wheels’ deflections under varying loads, the wheel encoder's resolution is constantly calibrated. The filter performance is evaluated at different speeds, loading patterns and maneuvers. Statistical tests are carried out to verify the filter consistency.