该文针对短采样宽带信号近似最大似然(AML)方位估计计算量大的问题,将马尔可夫蒙特卡罗方法与近似最大似然方位估计相结合,提出一种基于Gibbs抽样的近似最大似然方位估计新方法(Approximated Maximum Likelihood DOA estimator based on...该文针对短采样宽带信号近似最大似然(AML)方位估计计算量大的问题,将马尔可夫蒙特卡罗方法与近似最大似然方位估计相结合,提出一种基于Gibbs抽样的近似最大似然方位估计新方法(Approximated Maximum Likelihood DOA estimator based on Gibbs Sampling,AMLGS)。研究结果表明,AMLGS方法不但保持了原近似最大似然方位估计方法的优良性能,而且显著减小了计算量。把原方法的计算复杂度从D(L^K)减少到O(K×J×N_S)。展开更多
In present paper, we derive the quasi-least squares estimation(QLSE) and approximate maximum likelihood estimation(AMLE) for the Birnbaum-Saunders fatigue life distribution under multiply Type-Ⅱcensoring. Furthermore...In present paper, we derive the quasi-least squares estimation(QLSE) and approximate maximum likelihood estimation(AMLE) for the Birnbaum-Saunders fatigue life distribution under multiply Type-Ⅱcensoring. Furthermore, we get the variance and covariance of the approximate maximum likelihood estimation.展开更多
文摘该文针对短采样宽带信号近似最大似然(AML)方位估计计算量大的问题,将马尔可夫蒙特卡罗方法与近似最大似然方位估计相结合,提出一种基于Gibbs抽样的近似最大似然方位估计新方法(Approximated Maximum Likelihood DOA estimator based on Gibbs Sampling,AMLGS)。研究结果表明,AMLGS方法不但保持了原近似最大似然方位估计方法的优良性能,而且显著减小了计算量。把原方法的计算复杂度从D(L^K)减少到O(K×J×N_S)。
基金Supported by the NSF of China(69971016)Supported by the Shanghai Higher Learning Science and Technology Development Foundation(04DB24)
文摘In present paper, we derive the quasi-least squares estimation(QLSE) and approximate maximum likelihood estimation(AMLE) for the Birnbaum-Saunders fatigue life distribution under multiply Type-Ⅱcensoring. Furthermore, we get the variance and covariance of the approximate maximum likelihood estimation.
