Computation of Association Probabilities for Single Target Tracking with the Use of Adaptive Neuro-Fuzzy Inference System

In this study, a simple method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented for computing the association probabilities. The computed association probabilities are used to track the single manoeuvring target in the cluttered environment. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The tracks estimated by using the method proposed in this study are in very good agreement with the true tracks. Better accuracy with respect to the well known nearest neighbour Kalman filter and probabilistic data association algorithms is obtained.

Anahtar Kelimeler:

Target tracking, data association, ANFIS, neuro-fuzzy inference system

Computation of Association Probabilities for Single Target Tracking with the Use of Adaptive Neuro-Fuzzy Inference System

Keywords:

Target tracking, data association, ANFIS, neuro-fuzzy inference system,

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Y. Bar-Shalom, E. Tse, “Tracking in a Cluttered Environment with Probabilistic Data Association,” Automatica, Vol. 11, pp. 451-460, 1975.
Y. Bar-Shalom, T.E. Fortmann, Tracking and Data Association, San Diego: CA: Academic Press, 1988.
Y. Bar-Shalom, Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, 1995.
R.J. Fitzgerald, “Development of Practical PDA Logic for Multitarget Tracking by Microprocessor,” in Proc. of the American Control Conference, Seattle, Washington, pp. 889-897, 1986.
D. Sengupta, R.A. Iltis, “Neural Solution to the Multitarget Tracking Data Association Problem,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 25, No. 1, pp. 96-108, 1989.
H. Leung, “Neural-Network Data Association with Application to Multiple-Target Tracking,” Optical Engineer- ing, Vol. 35, No. 3, pp. 693-700, 1996.
R.A. Iltis, P.Y. Ting, “Computing Association Probabilities Using Parallel Boltzmann Machines,” IEEE Trans- actions on Neural Networks, Vol. 4, No. 2, pp. 221-233, 1993.
F. Wang, J. Litva, T. Lo, E. Bosse, “Performance of Neural Data Associator,” IEE Proc.- Radar, Sonar Navigation, Vol. 143, No. 2, pp. 71-78, 1996.
C. Moore, C. Harris, E. Rogers, “Utilizing Fuzzy Models in the Design of Estimators and Predictors: An Agile Target Tracking Example,” in Proc. of the 2ndIEEE Conference on Fuzzy Systems, pp. 679-684, 1993.
A. Tummert, “Fuzzy Technology Implemented in Sonar Systems,” IEEE J. Oceanic Eng., Vol. 18, No. 4, pp. 483-490, 1993.
O. Hossam, M. Farooq, T. Quach, “Fuzzy Logic Approach to Data Association,” in Proc. SPIE 2755, pp. 313-321, 1996.
R.P. Singh, W.H. Balley, “Fuzzy Logic Applications to Multi-Sensor Multitarget Correlation,” IEEE Transac- tions on Aerospace and Electronic Systems, Vol. 33, No. 3, pp. 752-769, 1997.
Y.M. Chen, H.C. Huang, “Fuzzy Logic Approach to Multisensor Data Association,” Mathematics and Computers in Simulation, Vol. 52, pp. 399-412, 2000.
J.S.R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 23, pp. 665-685, 1993.
J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, Upper Saddle River, NJ, 1997.
M. Brown, C. Haris, Neurofuzzy Adaptive Modeling and Control, Prentice-Hall, Englewood Cliffs, NJ, 1994.
V.A. Constantin, Fuzzy Logic and Neuro-Fuzzy Applications Explained, Prentice-Hall, Englewood Cliffs, NJ, 1995.
C.T. Lin, C.S.G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice-Hall, Upper Saddle River, NJ, 1996.
J. Kim, N. Kasabov, “HyFIS: Adaptive Neuro-Fuzzy Inference Systems and Their Application to Nonlinear Dynamical Systems,” Neural Networks, Vol. 12, pp. 1301–1319, 1999.
K. Guney, N. Sarikaya, “Adaptive Neuro-Fuzzy Inference System for the Input Resistance Computation of Rectangular Microstrip Antennas with Thin and Thick Substrates,” Journal of Electromagnetic Waves and Applications (JEWA), Vol. 18, pp. 23-39, 2004.
K. Guney, N. Sarikaya, “Computation of Resonant Frequency for Equilateral Triangular Microstrip Antennas with the Use of Adaptive Neuro-Fuzzy Inference System,” International Journal of RF and Microwave Computer- Aided Engineering, Vol. 14, pp. 134-143, 2004.
S. Ozer, K. Guney, A. Kaplan, “Computation of the Resonant Frequency of Electrically Thin and Thick Rectangular Microstrip Antennas with the Use of Fuzzy Inference Systems,” International Journal of RF and Microwave Computer-Aided Engineering, Vol. 10, pp. 108-119, 2000.
A. Kaplan, K. Guney, S. Ozer, “Fuzzy Associative Memories for the Computation of the Bandwidth of Rect- angular Microstrip Antennas with Thin and Thick Substrates,” International Journal of Electronics (IJE), Vol. 88, pp. 189-195, 2001.
S. Sagiroglu, K. Guney, “Calculation of Resonant Frequency for an Equilateral Triangular Microstrip Antenna with the Use of Artificial Neural Networks,” Microwave and Optical Technology Letters, Vol. 14, pp. 89-93, 1997.
S. Sagiroglu, K. Guney, M. Erler, “Resonant Frequency Calculation for Circular Microstrip Antennas using Artificial Neural Networks,” International Journal of RF and Microwave Computer-Aided Engineering, Vol. 8, pp. 270-277, 1998.
S. Sagiroglu, K. Guney, M. Erler, “Calculation of Bandwidth for Electrically Thin and Thick Rectangular Microstrip Antennas with the Use of Multilayered Perceptrons,” International Journal of RF and Microwave Computer-Aided Engineering (Special Issue: Applications of Artificial Neural Networks to RF and Microwave Design), Vol. 9, pp. 277-286, 1999.
D. Karaboga, K. Guney, S. Sagiroglu, M. Erler, “Neural Computation of Resonant Frequency of Electrically Thin and Thick Rectangular Microstrip Antennas,” IEE Proc. Microwaves, Antennas and Propagation, Pt H, Vol. 146, pp. 155-159, 1999.
K. Guney, M. Erler, S. Sagiroglu, “Artificial Neural Networks for the Resonant Resistance Calculation of Electrically Thin and Thick Rectangular Microstrip Antennas,” Electromagnetics, Vol. 20, pp. 387-400, 2000.
K. Guney, S. Sagiroglu, M. Erler, “Comparison of Neural Networks for Resonant Frequency Computation of Electrically Thin and Thick Rectangular Microstrip Antennas,” Journal of Electromagnetic Waves and Applications (JEWA), Vol. 15, pp. 1121-1145, 2001.
K. Guney, S. Sagiroglu, M. Erler, “Generalized Neural Method to Determine Resonant Frequencies of Various Microstrip Antennas,” International Journal of RF and Microwave Computer-Aided Engineering (Special Issue: Applications of Artificial Neural Networks to RF and Microwave Design), Vol. 12, pp. 131-139, 2002.
K. Guney, S. Sagiroglu, M. Erler, “Design of Rectangular Microstrip Antennas with the Use of Artificial Neural Networks,” Neural Network World, Vol. 4, pp. 361-370, 2002.
C. Yildiz, S. Gultekin, K. Guney, S. Sagiroglu, “Neural Models for the Resonant Frequency of Electrically Thin and Thick Circular Microstrip Antennas and the Characteristic Parameters of Asymmetric Coplanar Waveguides Backed with a Conductor,” AEU Int J Electron Commun, Vol. 56, pp. 396-406, 2002.
S. Gultekin, K. Guney, S. Sagiroglu, “Neural Networks for the Calculation of Bandwidth of Rectangular Microstrip Antennas,” Applied Computational Electromagnetics Society (ACES) Journal, Vol. 18, pp. 46-56, 2003.
K. Guney, N. Sarikaya, “Artificial Neural Networks for Calculating the Input Resistance of Circular Microstrip Antennas,” Microwave and Optical Technology Letters, Vol. 37, pp. 107-111, 2003.
K. Guney, N. Sarikaya, “Artificial Neural Networks for the Narrow Aperture Dimension Calculation of Optimum Gain Pyramidal Horns,” Electrical Engineering (Archiv fur Elektrotechnik),Vol. 86, pp. 157-163, 2004.