基于自调节粒子群算法的盲检测

Blind Detection Based on Self-adaptive Particle Swarm Optimization

以往基于粒子群优化的盲算法能成功实现信号盲检测,但具有算法收敛速度慢、容易陷入局部最小的缺点。文中通过分析粒子群算法的机能及参数的设置,提出一种改进的基于自调节粒子群优化的盲检测算法。算法构成思想是:基于MIMO系统的盲检测系统模型将盲检测问题转化为二次优化问题,利用改进的自调节粒子群优化算法对此优化问题进行寻优。仿真表明,改进算法具有全局收敛性好、收敛速度快、误码率低的优点,能够更好地解决盲检测问题。

The blind algorithm based on Particle Swarm Optimization （PSO） can achieve signal blind detection successfully,but has some defects such as converging to local optimum or slow convergence. By analyzing the performance of the PSO algorithm and the parameter setting,an improved blind algorithm based on self-adjustment PSO algorithm is presented. The thoughts are through blind detection system model based on MIMO system, translated the blind detection problem into quadratic optimization problem, and the new PSO algorithm was used to solve the problem. The experiment results show that the new PSO has good features such as strong global search capability ,rapid convergence and short computation time, which confirms the validity and feasibility of this approach.