| Abstract:In MIMO radar, the position distribution of transmitting and receiving antenna array element directly affects the directional diagram of MIMO radar. Because the distribution of array element position is a nonlinear optimization problem and has certain randomness, both genetic algorithm (GA) and particle swarm algorithm (PSO), which are typical intelligent optimization algorithms are discussed in this paper. And the array position of MIMO radar is optimized through three different improvement strategies respectively. Then the optimized array position and the analysis of simulation results presented in the each end of section.First of all, after analyzing the shortcomings of classic genetic algorithm, an interpretable precocious signs and an adaptive operation to overcome frequent premature phenomenon in genetic algorithm are put forward. Then, to improve the rate of convergence of the algorithm and escape into local optimum trouble, the two different kinds of particle swarm optimization are combined in this paper after the analysis of the global and local versions based on PSO. At last, another improving way——the mixed strategy is adopted. The advantage of genetic algorithm and particle swarm optimization are mixed into a hybrid approach. The hybrid approach proposed in this paper can overcome the shortage of single algorithm and improve the ability of searching the optimal value in the solving process.For reducing calculated amount, the equivalent directed graph optimization model is adopted to optimize array elements position by three different improved algorithms respectively. Simulation results show that three improved intelligent algorithms effectively reduce the peak side lobe level (PSLL) of antenna array in MIMO radar in different extent. Ultimately, the desired direction of MIMO radar diagram is achieved, and the validity and reliability of those improved algorithms are verified in sequence in this paper. This thesis contains25figures,3tables, and69references. |
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