Considering the blindness and slow convergence when initialized the basic particle swarm optimization, and exiting early-maturing in the process of evolution,in this paper,an MIMO system blind equalization model was given. Using the MIMO system blind detection based on particle swarm optimization algorithm, simulated annealing mechanism and chaotic mechanism were introduced, whereby the basis raised an improved algorithm: MIMO system blind detection based on simulated annealing and chaotic particle swarm optimization. Several algorithms and this improved algorithm were simulated. Simulation results show that the improved algorithm has good global convergence, convergence speed, the advantages of low bit error rate. It' s a good solution to solve blind detection and blind equalization.%考虑到基本粒子群算法在初始化时具有盲目性,收敛速度慢,在进化过程中会出现早熟现象.文中给出了MIMO系统的盲均衡模型,在对基本粒子群优化算法的MIMO系统盲检测研究基础上.分别引入了模拟退火机制和混沌机制,据此基础上提出一种改进的算法:基于模拟退火混沌粒子群优化的盲检测算法,并对这几种算法和改进算法的性能进行仿真.仿真结果表明,改进算法具有全局收敛性好、收敛速度快、误码率低的优点,能够很好地解决盲检测盲均衡问题.
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