Target identification with Bayesian networks

Tracking algorithms can tell fairly reliable where target is heading. That is enough in civilian aviation, but in defence applications it might be not. Target's type and its hostility are at least as important. Normally, identification of type or friend or foe cannot be determined from target's kinematic information. To identify a target we also need other information. Every plane type has its own specialities e.g. we know that certain type has two engines which affects directly to heat of exhaust fumes. This kind of speciality is generally referred as an attribute information. Because attribute information is type depended, it must be modelled by an expert, who has beforehand knowledge of the target's causality relations. One of the best theories to get expert's knowledge into a tracking system is Bayesian networks. Bayesian networks is a model that describes relationships between attributes. In this paper we concentrate to identification problem. Question is how comprehension of the target's type changes with time when observations are corrupted by noise. We illustrate theory of Bayesian networks and explain its place in racking system. Finally we analyze performance of Bayesian networks in case where the problem is to identify targets from noisy data set.

[1]  Antonio A. F. Oliveira,et al.  An image processing and belief network approach to face detection , 1999, XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481).

[2]  Kevin P. Murphy,et al.  A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables , 1999, UAI.

[3]  Dragomir Anguelov,et al.  A General Algorithm for Approximate Inference and Its Application to Hybrid Bayes Nets , 1999, UAI.

[4]  M.J. Larkin,et al.  Sensor fusion and classification of acoustic signals using Bayesian networks , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[5]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[6]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[7]  Kevin P. Murphy,et al.  Inference and Learning in Hybrid Bayesian Networks , 1998 .

[8]  Geoffrey Zweig,et al.  Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.

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

[10]  W. H. Hsu,et al.  A position paper on statistical inference techniques which integrate neural network and Bayesian network models , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[11]  Kuo-Chu Chang,et al.  Target identification with Bayesian networks in a multiple hypothesis tracking system , 1997 .

[12]  Jonathan J. Oliver,et al.  A Bayesian analysis of the doomsday argument , 1997 .

[13]  Ann E. Nicholson,et al.  Fall Diagnosis using Dynamic Belief Networks , 1996, PRICAI.

[14]  Alice M. Agogino,et al.  Inference Using Message Propagation and Topology Transformation in Vector Gaussian Continuous Networks , 1996, UAI.

[15]  Alice M. Agogino,et al.  A bayesian decision-theoretic framework for real-time monitoring and diagnosis of complex systems: theory and application , 1996 .

[16]  Lutz Volkmann Fundamente der Graphentheorie , 1996, Springer Lehrbuch Mathematik.

[17]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[18]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[19]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

[20]  Ann Elizabeth Nicholson,et al.  Monitoring discrete environments using dynamic belief networks (robotics) , 1992 .

[21]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[22]  Ross D. Shachter,et al.  Fusion and Propagation with Multiple Observations in Belief Networks , 1991, Artif. Intell..

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[25]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[26]  Yaakov Bar-Shalom,et al.  Multitarget-multisensor tracking: Advanced applications , 1989 .

[27]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[28]  Y. Bar-Shalom Tracking and data association , 1988 .

[29]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[30]  Henry Stark,et al.  Probability, Random Processes, and Estimation Theory for Engineers , 1995 .

[31]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[32]  Paul Nahin,et al.  NCTR Plus Sensor Fusion Equals IFFN or can Two Plus Two Equal Five? , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[33]  G. R. Noakes Physics—Classical and Modern , 1951, Nature.