Performance analysis of statistical optimal data fusion algorithms

Abstract Data fusion is widely used in biometric, multi-media signal and image processing, and wireless sensor networks. Optimal fusion techniques are developed to perform fusion under noisy environments. However, the statistical analysis carried out to evaluate the relative merits of these optimal methods is very less. The aim of this paper is to fill this gap by evaluating four statistical optimal data fusion methods namely, the linearly constrained least squares (LCLS) fusion method, the covariance intersection (CI) fusion method, the linearly constrained least absolute deviation (CLAD) fusion method, and the constrained least square (CLS) fusion method. The CLS fusion method presented here is an improved version of the CLAD fusion method. We further analyze the performances of these four methods in terms of optimality, unbiased estimation, robustness, and complexity. Simulations are used to validate the performance of these fusion algorithms.

[1]  Fang Wang,et al.  The decision fusion in the wireless network with possible transmission errors , 2012, Inf. Sci..

[2]  Prabir Bhattacharya,et al.  A driver fatigue recognition model based on information fusion and dynamic Bayesian network , 2010, Inf. Sci..

[3]  Qingquan Li,et al.  A comparative analysis of image fusion methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[4]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[5]  Nevena Lazic,et al.  Adaptive time-frequency data fusion for speech enhancement , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[6]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[7]  Fabio Roli,et al.  A theoretical and experimental analysis of linear combiners for multiple classifier systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ronald R. Yager,et al.  A framework for multi-source data fusion , 2004, Inf. Sci..

[9]  Henry Leung,et al.  Feature-mapping data fusion , 1996 .

[10]  Rick S. Blum.  Robust image fusion using a statistical signal processing approach , 2005, Inf. Fusion.

[11]  Rick S. Blum.  On multisensor image fusion performance limits from an estimation theory perspective , 2006, Inf. Fusion.

[12]  Henry Leung,et al.  Data-driven based model for flow prediction of steam system in steel industry , 2012, Inf. Sci..

[13]  Pramod K. Varshney,et al.  An Image Fusion Approach Based on Markov Random Fields , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[15]  Tuan D. Pham,et al.  An image restoration by fusion , 2001, Pattern Recognit..

[16]  Swagatam Das,et al.  Multi-sensor data fusion using support vector machine for motor fault detection , 2012, Inf. Sci..

[17]  Yifeng Zhou,et al.  Linearly constrained least squares approach for multisensor data fusion , 1997, Defense, Security, and Sensing.

[18]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[19]  Plamen P. Angelov,et al.  Density-based averaging - A new operator for data fusion , 2013, Inf. Sci..

[20]  Jeffrey K. Uhlmann,et al.  A non-divergent estimation algorithm in the presence of unknown correlations , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[21]  Henry Leung,et al.  A variational Bayesian approach to robust sensor fusion based on Student-t distribution , 2013, Inf. Sci..

[22]  Jake K. Aggarwal,et al.  Multisensor Fusion for Computer Vision , 1993, NATO ASI Series.

[23]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[24]  Ravi K. Sharma,et al.  Probabilistic Image Sensor Fusion , 1998, NIPS.

[25]  Cishen Zhang,et al.  Diffusion Kalman Filtering Based on Covariance Intersection , 2012, IEEE Transactions on Signal Processing.

[26]  Sankar K. Pal,et al.  Soft computing data mining , 2004, Inf. Sci..

[27]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[28]  Yuan Gao,et al.  Sequential covariance intersection fusion Kalman filter , 2012, Inf. Sci..

[29]  Vladimir S. Petrovic,et al.  Subjective tests for image fusion evaluation and objective metric validation , 2007, Inf. Fusion.

[30]  Mrityunjay Kumar,et al.  A Total Variation-Based Algorithm for Pixel-Level Image Fusion , 2009, IEEE Transactions on Image Processing.

[31]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[32]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[33]  Toshihisa Tanaka,et al.  Signal Processing Techniques for Knowledge Extraction and Information Fusion , 2008 .

[34]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[35]  Yves Demazeau,et al.  Principles and techniques for sensor data fusion , 1993, Signal Process..

[36]  Henry Leung,et al.  Neural data fusion algorithms based on a linearly constrained least square method , 2002, IEEE Trans. Neural Networks.

[37]  Pablo O. Arambel,et al.  Estimation under unknown correlation: covariance intersection revisited , 2002, IEEE Trans. Autom. Control..

[38]  Anthony G. Constantinides,et al.  Data Fusion for Modern Engineering Applications: An Overview , 2005, ICANN.

[39]  Leonid I. Perlovsky,et al.  Cognitive high level information fusion , 2007, Inf. Sci..

[40]  Rick S. Blum,et al.  A statistical signal processing approach to image fusion for concealed weapon detection , 2002, Proceedings. International Conference on Image Processing.

[41]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[42]  Mohamed S. Kamel,et al.  Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion , 2007, IEEE Transactions on Image Processing.

[43]  James Ting-Ho Lo,et al.  Synthetic approach to optimal filtering , 1994, IEEE Trans. Neural Networks.

[44]  Chun C. Lai,et al.  Multisensor Fusion and Integration: Theories, Applications, and its Perspectives , 2011, IEEE Sensors Journal.

[45]  Youshen Xia,et al.  An Extended Projection Neural Network for Constrained Optimization , 2004, Neural Computation.

[46]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[47]  Mohamed S. Kamel,et al.  Cooperative Learning Algorithms for Data Fusion Using Novel $L_{1}$ Estimation , 2008, IEEE Transactions on Signal Processing.

[48]  Yifeng Zhou,et al.  Maximum-likelihood approach for multisensor data fusion applications , 1998, Defense, Security, and Sensing.

[49]  Ashraf M. Aziz.  A new adaptive decentralized soft decision combining rule for distributed sensor systems with data fusion , 2014, Inf. Sci..

[50]  Naif Alajlan,et al.  Fusion of supervised and unsupervised learning for improved classification of hyperspectral images , 2012, Inf. Sci..

[51]  Danilo P. Mandic,et al.  Multiscale Image Fusion Using Complex Extensions of EMD , 2009, IEEE Transactions on Signal Processing.

[52]  W. Niehsen,et al.  Information fusion based on fast covariance intersection filtering , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[53]  Stephan Matzka,et al.  A comparison of track-to-track fusion algorithms for automotive sensor fusion , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.