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[1] P.C. Chen, G Kendall, G.V. Berghe,“An Ant Based Hyper-heuristic for the Travelling Tournament Problem,” Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched 2007), pp.19-26, 2007. [2] Konak, A. & Kulturel-Konak, S., “An Ant Colony Optimization Approach to the Minimum Tool Switching Instant Problem in Flexible Manufacturing System,” Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched 2007), pp.43-48, 2007. [3] M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol.1, pp.53-66, 1997. [4] Montemanni, R., Smith, D.H. & Gambardella, L.M., “Ant Colony Systems for Large Sequential Ordering Problems,” Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pp.60-67, 2007. [5] Complex Intelligent Syst. Lab., Swinburne Univ. of Technol. & Melbourne, Vic.,“Crowding Population-based Ant Colony Optimisation for the Multi-objective Travelling Salesman Problem,” Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM 2007), pp.333-340, 2007. [6] Kanan, H.R., Faez, K., & Hosseinzadeh, M., “Face Recognition System Using Ant Colony Optimization-Based Selected Features,” Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2007), pp.57-62, 2007. [7] Duan, H. & Xiufen Yu,“Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem,” Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007), pp.92-95, 2007. [8] Haibin Duan, Xiufen Yu & Guanjun Ma,“Novel Hybrid Approach for Fault Diagnosis in 3-DOF Flight Simulator Based on BP Neural Network and Ant Colony Algorithm,” Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pp.371-374, 2007. [9] H.Lee & I-J Tahk, ”Generalized Input-Estimation Technique for Tracking Maneuvering Targets, ” IEEE Trans. Aerosp. Electron. Syst. Vol AES-35, pp.1388-1403, 1999. [10] K. A. Fisher & P. S. Maybeck, ” Multiple Adaptive Estimation with Filter Spawning,” IEEE Trans. Aerosp. Electron. Syst.Vol.38, No.3, pp.755-768, 2002. [11] N.Okello & B.Ristic, ”Maximum Likelihood Registration for Multiple Dissimilar Sensors,” IEEE Trans. Aerosp. Electron.Syst. Vol.39, No.3, pp.1074-1083,2003. [12] P.D.Hanlon & P.S. Maybeck, ”Interrelation Ship of Single-Filter and Multiple-Model Adaptive Algorithms,” IEEE Trans. Aerosp. Electron. Syst. Vol. AES-34, pp.934-946, 1998. [13] E.Mazor,J Dayan,A.Averbuch & Y.Bar-Shalom, ”Interacting Multiple Model Methods in Target Tracking: A Survey,” IEEE Trans. Aerosp. Electron. Syst. Vol AES-34, pp.103-124, 1998. [14] D. Sengupta & R. A. Iltis, "Neural solution to the multitarget tracking data association problem," IEEE Trans. Aerosp. Electron. Syst., Vol.25, pp.86-108, 1989. [15] B. Zhou & N.K. Bose, "A comprehensive analysis of neural solution to the multitarget tracking data association problem," IEEE Trans. Aerosp. Electron. Syst.,Vol.29, pp.260-263, 1993. [16] L. Chin, "Application of neural networks in target tracking data fusion," IEEE Trans. Aerosp. Electron. Syst., Vol.30, pp.281-287, 1994. [17] K.C. Chang, C.Y. Chong, & Y. Bar-Shalom, "Joint Probabilistic Data and Association Distributed Sensor Networks," IEEE Trans. Automa. Contr., Vol. AC-31, pp.889-897, Oct .1986. [18] Pau-Choo Chung, Ching-Tsorng Tsai, E-Ling Chen & Yung-Nien Sun “Polygonal Approximation Using A Competitive Hopfield Neural Network, “ Patten Recognition, Vol.27, No.11, pp.1505-1215, 1994. [19] Y. Bar-Shalom & T.E. Fortmann, ” Tracking and Data Association,” Academic Press, INC. 1989. [20] S. Blackman, ”Multiple Target Tracking With Radar Applications, ” Artech House, 1986. [21] Lin, X., Kirybarajan, T., & Bar-Shalom, Y., “Multi-sensor bias estimation with local tracks without a priori association,” Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, vol. 5204, San Diego, CA, Aug. 2003. [22] Stone, L. D., Williams, M., & Tran, T., “Track-to-track association and bias removal,”Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, vol. 4728, Orlando, FL, Apr. 2002. [23] Agate, C, and Sullivan, K. J. Road-constraint target tracking and identification using a particle filter. In Proceedings of Signal and Data Processing of Small Targets, vol. 5204,SPIE, 2003. [24] Lin, L., Kirubarajan, T., & Bar-Shalom, Y., “New assignment-based data association for tracking move-stop-move targets, ” Proceedings of International Conference on Information Fusion. Annapolis, MD, July 2002, pp.943-950. [25] Ristic, B., Arulampalam, S., & Gordon, N. Beyond the Kalman Filter, Particle Filters for Tracking Applications. Norwood, MA: Artech House Publishers, 2004. [26] Zhang, X., Willett, P. and Bar-Shalom, Y. The Cram´er-Rao Bound for Dynamic Target Tracking with Measurement Origin Uncertainty. In The 41st IEEE Conference on Decision and Control, 2002. [27] Hue, C., Le Cadre, J.-P., and P´erez, P. Performance Analysis of Two Sequential Monte Carlo Methods and Posterior Cram´er-Rao Bounds for Multi-Target Tracking. Technical report, IRISA, 2002. [28] Lin, X., Kirubarajan, T., & Bar-Shalom, Y., “Multi-sensor-multi-target bias estimation for asynchronous sensors,” Proceedings of SPIE Conference on Signal Processing, Sensor Fusion, and Target Recognition XIII, vol. 5429, Orlando, FL, Apr. 2004
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