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Study on Application of Computational Intelligence Methods to the DOA Estimation

Author: WangZuo
Tutor: ZhangChaoZhu
School: Harbin Engineering University
Course: Communication and Information System
Keywords: Direction of Arrival Estimation Maximum likelihood algorithm Particle swarm optimization Simulated annealing algorithm
CLC: TN911.7
Type: Master's thesis
Year: 2009
Downloads: 27
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Abstract


Direction of Arrival (DOA) estimation is an important research direction in the array signal processing field. The direction estimation array signal processing-based DOA estimation method can achieve high resolution at the same time on multiple sources on different directions of space, multiple studies such as radar, sonar, active protection systems, communication systems, and smart antenna the field of a wide range of applications. Maximum likelihood estimation (MLE) is one of the most important, the most widely used method of statistical estimation theory, maximum likelihood for optimal performance, but this estimate algorithms need to be multi-variable nonlinear maximum global search, the large amount of computation. This is the MLE method application bottlenecks. The object of the main applications of computational intelligence methods are optimization problem intractable problems. Therefore, this paper is based on the actual situation in the parameter estimation of non-cooperative particle swarm optimization algorithm, simulated annealing algorithm and its improved algorithm suitable to be used in the maximum likelihood estimation of multi-source DOA the Gordian optimization problem . This paper first introduces the basic theory and model of the array signal processing, and then introduces the basic principles of the classic beamforming method, MUSIC and ESPRIT spatial spectrum estimation algorithm. Then highlight the maximum likelihood algorithm, and some algorithms, such as alternating projection algorithm, genetic algorithm to achieve maximum likelihood algorithm. Particle swarm optimization algorithm algorithm processes the simplicity of its excellent convergence ability, we choose the particle swarm optimization algorithm major implementations algorithm. Correct based on maximum likelihood estimation of particle swarm optimization with linear decreasing weight vector (LDWPSO), gradually narrowing the initial inertia weight vector algorithm, by such a process while retaining the global convergence of the algorithm enhanced on the basis of local convergence of the algorithm, the algorithm is more suitable for DOA maximum likelihood estimation search. Then the paper proposes a dynamic adjustment of the distance between the particles and the optimal particle inertia weight vector improved particle swarm optimization algorithm, and the improved particle swarm optimization algorithm, the average of all the particles in the population. So that the particles to be able to take into account the situation of the entire population in the course of the flight. Use the improved particle swarm optimization algorithm DOA maximum likelihood estimation search through simulation will be corrected LDWPSO algorithm and improved particle swarm optimization algorithm to compare algorithm performance with amendments to improve before. In the final part, the SA-PSO algorithm DOA maximum likelihood estimation search, introduced in the PSO algorithm to accept the concept of probability and annealing temperature of the simulated annealing algorithm, a SA-PSO algorithm to validate the algorithm, and finally through the simulation Search performance in DOA maximum likelihood estimates.

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