Evidence theoretic classification of ballistic missiles

Approach presents classification of ballistic missiles using evidential theory.Airborne objects classified without prior knowledge in few seconds after detection.Decision criterion proposed to categorize airborne objects into six major classes.Approach performs better than k-NN and decision tree methods with chosen data sets.Validated with real and simulated data sets from single and multiple radars sources. In this paper, using the Dempster-Shafer theory (DST) of evidence, a new decision criterion is proposed which can quickly classify airborne objects without any a priori knowledge, whose data are laced with environmental noise characteristics, within 10seconds (10s) from the time it is detected. Kinematic parameters of an airborne object received from radars are used to classify it into one of the six classes, which include three levels of ballistic target discrimination, aerodynamic, satellite and unknown. The DST is chosen as it can suitably handle the element of uncertainty, limited a priori data and short observation times that exist with the data acquired for the purpose of classification. The focus of the work is on ballistic targets in a theater of war. The approach is compared with the popularly known k-NN and decision tree techniques and is found to perform better with the chosen data sets. This approach is tested using both real flight test data and simulated data.

[1]  Paul Zarchan,et al.  Ballistic missile defense guidance and control issues , 1999 .

[2]  A. Maseleno,et al.  Skin infection detection using Dempster-Shafer theory , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[3]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[4]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[5]  Thierry Denoeux,et al.  Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules , 2011, Int. J. Approx. Reason..

[6]  Christian Posse,et al.  A Layered Dempster-Shafer Approach to Scenario Construction and Analysis , 2007, 2007 IEEE Intelligence and Security Informatics.

[7]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[8]  E. Mandler,et al.  Combining the Classification Results of Independent Classifiers Based on the Dempster/Shafer Theory of Evidence , 1988 .

[9]  Philippe Smets,et al.  Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  P. L. Bogler,et al.  Shafer-dempster reasoning with applications to multisensor target identification systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Frans Voorbraak,et al.  A Computationally Efficient Approximation of Dempster-Shafer Theory , 1988, Int. J. Man Mach. Stud..

[12]  A. Bastière.  Methods for multisensor classification of airborne targets integrating evidence theory , 1998 .

[13]  Henry Leung,et al.  Bayesian and Dempster-Shafer target identification for radar surveillance , 2000, IEEE Trans. Aerosp. Electron. Syst..

[14]  Kimberly Coombs,et al.  Using Dempster-Shafer methods for object classification in the theater ballistic missile environment , 1999, Defense, Security, and Sensing.

[15]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[16]  Ramesh K. Agarwal,et al.  Recent Advances In Aircraft Technology , 2014 .

[17]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[18]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[19]  Qinghua Hu,et al.  Neighborhood classifiers , 2008, Expert Syst. Appl..

[20]  Shou-De Lin,et al.  A Ranking-based KNN Approach for Multi-Label Classification , 2012, ACML.

[21]  J. Kohlas,et al.  A Mathematical Theory of Hints: An Approach to the Dempster-Shafer Theory of Evidence , 1995 .

[22]  YI Xiao,et al.  An Improved Dempster-Shafer Algorithm for Resolving the Conflicting Evidences , 2006 .

[23]  M. Beynon,et al.  The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling , 2000 .

[24]  Matthew D. Blum.  Automatic target recognition based on collection of evidence , 1999 .

[25]  A. Dempster,et al.  Dempster–Shafer models for object recognition and classification: Research Articles , 2006 .

[26]  Bhekisipho Twala,et al.  AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES , 2009, Appl. Artif. Intell..

[27]  Fisseha Mekuria,et al.  Ensemble multisensor data using state-of-the-art classification methods , 2013, 2013 Africon.

[28]  Peter J. Mantle.  The missile defense equation : factors for decision making , 2004 .

[29]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[30]  Bhekisipho Twala,et al.  Target tracking using multiple classifier systems and statistical process control , 2013, 2013 Africon.

[31]  D. Dutta Majumder,et al.  A Target Classification Architectural Scheme for Secure Decision Making in Network Centric Environment with MPLS-VPN Architecture , 2012 .

[32]  Isabelle Bloch,et al.  Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing , 1997, IEEE Trans. Geosci. Remote. Sens..

[33]  Dennis M. Buede,et al.  A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[34]  Erik Blasch,et al.  Overview of Dempster-Shafer and belief function tracking methods , 2013, Defense, Security, and Sensing.

[35]  Quan Pan,et al.  A new belief-based K-nearest neighbor classification method , 2013, Pattern Recognit..

[36]  Subrata Das.  High-Level Data Fusion , 2008 .

[37]  Scott T. Acton,et al.  Cloud tracking by scale space classification , 2002, IEEE Trans. Geosci. Remote. Sens..

[38]  Arthur P. Dempster,et al.  Dempster–Shafer models for object recognition and classification , 2006, Int. J. Intell. Syst..

[39]  Thierry Denoeux,et al.  A neural network classifier based on Dempster-Shafer theory , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[40]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[41]  Subhash Challa,et al.  An Introduction to Bayesian and Dempster-Shafer Data Fusion , 2003 .

[42]  Bor-Chen Kuo,et al.  A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[43]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[44]  Subhash Challa,et al.  Measuring and Managing Uncertainty Through Data Fusion for Application to Aircraft Identification System , 2012 .

[45]  Ronald R. Yager,et al.  Classic Works of the Dempster-Shafer Theory of Belief Functions , 2010, Classic Works of the Dempster-Shafer Theory of Belief Functions.