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A Refined Genetic Algorithm for Accurate and Reliable DOA Estimation with a Sensor Array

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Abstract

Maximum likelihood (ML) direction-of-arrival (DOA) estimation algorithm is a nearly optimal technique. In this paper, we present a modified and refined genetic algorithm (GA) to find the exact solutions to the complex, multi-modal, multivariate and highly nonlinear likelihood function. With the newly introduced features such as intelligent initialization and the emperor-selective mating scheme, carefully selected crossover and mutation operators, and fine-tuned parameters such as the population size, the probability of crossover and mutation, the GA-ML estimator achieves fast global convergence. The GA-ML estimator has been compared with various DOA estimation methods in a variety of scenarios, and the simulation results demonstrate that in most scenarios the proposed GA-ML estimator is the fastest and its performance is the best among popular ML-based DOA estimation methods.

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References

  1. Godara L.C. (1997). “Application of Antenna Arrays to Mobile Communications, Part II: Beam-Forming and Direction-of-Arrival Considerations”. Proceedings of IEEE. 85, 1195–1245

    Article  Google Scholar 

  2. Ziskind I., Wax M. (1988). “Maximum Likelihood Localization of Multiple Sources by Alternating Projection”. IEEE Transaction Acoustical, Speech, Signal Processing 36, 1553–1560

    Article  MATH  Google Scholar 

  3. K. Sharman, “Maximum Likelihood Estimation by Simulated Annealing”, Proc. ICASSP’88, New York, pp. 2741–2744, 1988.

  4. Stoica P., Gershman A.B. (1999). “Maximum-Likelihood DOA Estimation by Data-Supported Grid Search”. IEEE Signal Processing Letters 6, 273–275

    Article  Google Scholar 

  5. Stoica P., Sharman K.C. (1990). “Maximum Likelihood Methods for Direction-of-Arrival Estimation”. IEEE Transaction of Acoustical, Speech, Signal Processing 38, 1132–1143

    Article  MATH  Google Scholar 

  6. Man K.F., Tang K.S., Kwong S., Halang W.A. (1997). Genetic Algorithms for Control and Signal Processing. Springer, Berlin Heidelberg New york

    Google Scholar 

  7. Chambers L. (1995). Practical Handbook of Genetic Algorithms: Applications. CRC Press, Boca Ration

    MATH  Google Scholar 

  8. L. Taieb and M. Schoenauer, “Optimization of Direction Finders by Genetic Algorithms”, in Proc. First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, pp. 23–29, 1995.

  9. P. Karamalis, A. Marousis, A. Kanata, and P. Constantinou, “Direction of Arrival Estimation Using Genetic Algorithms”, Proc. VTC Spring 2001, Vol. 1, Rhodes, Greece, pp. 162–166, 2001.

  10. G.D. McClurkin, K.C. Sharman, and T.S. Durrant, “Genetic Algorithms for Spatial Spectral Estimation”, in Proc. Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling, Minneapolis, MN, pp. 318–322, 1988.

  11. Wax M., Ziskind I. (1989) “On Unique Localization of Multiple Sources by Passive Sensors Array”. IEEE Transaction Acoustical, Speech, Signal Processing 37, 996–1000

    Article  Google Scholar 

  12. Stoica P., Nehoral A. (1990). “Performance Study of Conditional and Unconditional Direction-of-Arrival Estimation”. IEEE Transaction Acoustical, Speech, Signal Processing 38, 1783–1795

    Article  MATH  Google Scholar 

  13. Anderson S. (1993). “On Optimal Dimension Reduction for Sensor Array Signal Processing”. Signal Processing 30, 245–256

    Article  MATH  Google Scholar 

  14. Gershman A.B. (1998). “Pseudo-Randomly Generated Estimator Banks: A New Tool for Improving the Threshold Performance of Direction Finding”. IEEE Transaction Signal Processing 46, 1351–1364

    Article  Google Scholar 

  15. Yeo B.K., Lu Y. (1999). “Array Failure Correction with a Genetic Algorithm”. IEEE Transaction Antennas Propagat. 47, 823–828

    Article  Google Scholar 

  16. Deb K. (2001). Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New york

    MATH  Google Scholar 

  17. GA-ML DOA estimator, MATLAB code: http://www.ntu.edu.sg/home/eylu/download/.

  18. MATLAB Genetic Algorithm and Direct Search Toolbox: http://www.mathworks.com/products/gads/.

  19. The Genetic Algorithm Optimization Toolbox (GAOT): http://www.ie.ncsu.edu/mirage/GAToolBox/gaot/.

  20. Stoica P., Nehorai A. (1989). “MUSIC, Maximum Likelihood, and Cramer-Rao Bound”. IEEE Transaction Acoustical, Speech, Signal Processing 37, 720–741

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to Minghui Li.

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Li, M., Lu, Y. A Refined Genetic Algorithm for Accurate and Reliable DOA Estimation with a Sensor Array. Wireless Pers Commun 43, 533–547 (2007). https://doi.org/10.1007/s11277-007-9248-5

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