Fusion-Based Sensor Selection for Optimal State Estimation and Minimum Cost (Intelligent Optimization Approach)
Автор: Saeed Mohammadloo, Ali Jabar Rashidi
Статья в выпуске: 4 vol.4, 2012 года.
This paper proposes a heuristic method for the sensor selection problem that uses a state vector fusion approach as a data fusion method. We explain the heuristic to estimate a stationary target position. Given a first sensor with specified accuracy and by using genetic algorithm, the heuristic selects second sensor such that the fusion of two sensor measurements would yield an optimal estimation in a target localization scenario. Optimality in our method means that a trade-off between estimation error and cost of sensory system should be created. The heuristic also investigates the importance of proportion between the range and bearing measurement accuracy of selected sensor. Monte Carlo Simulation results for a target position estimation scenario showed that the error in heuristic is less than the estimate error where sensors are used alone for estimation, while considering the trade-off between cost and accuracy.
Data fusion, sensor selection, multi-objective optimization, genetic algorithm
Короткий адрес: https://readera.org/15010235
Список литературы Fusion-Based Sensor Selection for Optimal State Estimation and Minimum Cost (Intelligent Optimization Approach)
- Harmon, S. Y., “Tools for muitisensor data fusion in autonomous robots”, Highly Redundant Sensing in Robotic Systems. Springer-Verlag, 1990, pp. 103- 125.
- Rashidi, A. J. and Mohammadloo, S., “Simultaneous Cooperative Localization for AUVs Using Range Only Sensors”, International Journal of Information Acquisition, 2011, Vol. 8, No. 2, pp. 117–132.
- Bazzazzadeh, N., “Optimal and Robust Distributed Data Fusion for Joint Target-Detection and Tracking”, School of Engineering and Physical Sciences Heriot Watt University, MSc thesis, 2009.
- Ir. Nada Milisavljević, “Sensor and Data Fusion”, Published by In-Teh, Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria, 2009.
- Jitendra R. Raol, “Multi-Sensor Data Fusion with MATLAB”, Published by Taylor & Francis Group, 2010.
- Ramdaras, U.D. and Bolderheij, F., “Performance-Based Sensor Selection for Optimal Target Tracking”, 12th International Conference on Information Fusion, 2009, pp. 1687-1694.
- Takamasa Koshizen, “Improved Sensor Selection Technique by Integrating Sensor Fusion in Robot Position Estimation”, Journal of Intelligent and Robotic Systems, 2000, Vol. 29, Issue. 1, pp. 79-92.
- Joshi, S. and Boyd, S. “Sensor Selection via Convex Optimization”, IEEE Trans. On Signal Processing, 2009, Vol. 57, Issue. 2, pp. 451 – 462.
- Yongmian Zhang and Qiang Ji, “Sensor Selection for Active Information Fusion”, Proceedings of the 20th national conference on Artificial intelligence, 2005, Volume 3, pp. 1229-1234.
- Titterton, D. H. and Weston, J. L. “Strapdown Inertial Navigation Technology,” The Institution of Electrical Engineers, 2004.
- C. Grosan and A. Abraham, “A New Approach for Solving Nonlinear Equations Systems”, IEEE Trans. On Systems, Man and Cybernetics, Part A: Systems and Humans, May 2008, VOL. 38, NO. 3.
- K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, "A fast and elitist multi-objective genetic algorithm: NSGA-II" , IEEE Trans. On Evolutionary Computation, 2002, 6(2):182-197.
- N. Nariman-zadeh, K. Atashkari, A. Jamali, A. Pilechi, X. Yao, "Inverse modeling of multi-objective thermodynamically optimized turbojet engine using GMDH-type neural networks and evolutionary algorithms", Taylor & Francis Group, Engineering Optimization, Vol. 37, 2005, pp. 437-462(26).
- K. Atashkari, N. Nariman-zadeh, A. Jamali, A. Pilechi, "Thermodynamic Pareto Optimization of turbojet using multi-objective genetic algorithm", International Journal of Thermal Science, Elsevier, 2005, Vol. 44, No. 11, pp. 1061-1071.
- N. Srinivas, K. Deb, "Multi-objective Optimization using Non-dominated Sorting in Genetic Algorithm", Journal of Evolutionary Computation, 1994, Vol. 2, No. 3, 221-248.