【作者】 毛琳琳； 张群飞； 黄建国； 雷开卓；

【Author】 MAO Linlin;ZHANG Qunfei;HUANG Jianguo;LEI Kaizhuo;School of Marine Science and Technology,Northwestern Polytechnical University;

【机构】 西北工业大学航海学院；

【摘要】 贝叶斯最大后验概率密度估计法(BM估计)是已知窄带信号源DOA估计的最佳估计。然而,由于多维积分和网格搜索,BM估计计算负担成倍增加,使其很难在实时系统中应用。针对BM估计方法计算量大的问题,将蚁群优化算法与BM估计相结合,研究了基于蚁群算法的贝叶斯方位估计方法(BM ACO),并提出了基于改进蚁群优化算法的贝叶斯估计方法(BM MACO)。BM MACO方法通过使用混沌序列进行状态空间的初始化和增加局部搜索的方式,克服了蚁群算法收敛速度慢、容易陷入局部最优的弊端。仿真结果表明:BM ACO和BM MACO算法均能在显著降低原BM方法计算量的同时,保持BM算法的高分辨性能;BM MACO由于降低了参数对BM ACO算法的影响因子,估计精度及运算速度也有所提高。更多还原

【Abstract】 Bayesian maximum a posterior probability density DOA estimator(BM DOA estimator) is known to be the best estimator in DOA estimation for narrow band sources.However,the exponentially increasing computation burden of the BM estimator,due to multidimensional grid search and integrates,makes it very difficult to use the BM estimator in real-time systems.In this paper,a computation feasible ant colony optimization method(ACO) is applied to lighten the computation burden.In addition,in order to overcome the drawbacks of ACO,such as low convergence speed and being easily trapped in local optimum,chaos initialization and local search are integrated into the classic ACO method,to form a novel method named MACO.Based on MACO,a novel BM DOA estimator named BM_MACO with even lower computational complexity is proposed.It is shown via simulations that both methods could keep the good performance of the original BM DOA estimator and reduce the computation evidently.Due to the initialization via chaotic sequences and local search in the optimization procedure,BMMACO method reduces the sensitivity of parameters,and thus outperforms the BMACO for its higher precision and less computation.更多还原