A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arr...A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.展开更多
There exist many iterative methods for computing the maximum likelihood estimator but most of them suffer from one or several drawbacks such as the need to inverse a Hessian matrix and the need to find good initial ap...There exist many iterative methods for computing the maximum likelihood estimator but most of them suffer from one or several drawbacks such as the need to inverse a Hessian matrix and the need to find good initial approximations of the parameters that are unknown in practice. In this paper, we present an estimation method without matrix inversion based on a linear approximation of the likelihood equations in a neighborhood of the constrained maximum likelihood estimator. We obtain closed-form approximations of solutions and standard errors. Then, we propose an iterative algorithm which cycles through the components of the vector parameter and updates one component at a time. The initial solution, which is necessary to start the iterative procedure, is automated. The proposed algorithm is compared to some of the best iterative optimization algorithms available on R and MATLAB software through a simulation study and applied to the statistical analysis of a road safety measure.展开更多
针对短采样宽带信号近似最大似然(approximated maximum likelihood,AML)方位估计计算量大的问题,将马尔科夫链-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Metropolis-Hastings抽样的近似最大似然方位估计方法(AMLMH)。该...针对短采样宽带信号近似最大似然(approximated maximum likelihood,AML)方位估计计算量大的问题,将马尔科夫链-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Metropolis-Hastings抽样的近似最大似然方位估计方法(AMLMH)。该方法将AML算法的空间谱函数作为信号的概率分布函数,并利用Metropolis-Hastings抽样方法从该概率分布函数中抽样。研究结果表明,AMLMH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,而且减小了计算量。展开更多
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
基金National High-tech Research and Development Program of China (2010AA7010422,2011AA7014061)
文摘A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.
文摘There exist many iterative methods for computing the maximum likelihood estimator but most of them suffer from one or several drawbacks such as the need to inverse a Hessian matrix and the need to find good initial approximations of the parameters that are unknown in practice. In this paper, we present an estimation method without matrix inversion based on a linear approximation of the likelihood equations in a neighborhood of the constrained maximum likelihood estimator. We obtain closed-form approximations of solutions and standard errors. Then, we propose an iterative algorithm which cycles through the components of the vector parameter and updates one component at a time. The initial solution, which is necessary to start the iterative procedure, is automated. The proposed algorithm is compared to some of the best iterative optimization algorithms available on R and MATLAB software through a simulation study and applied to the statistical analysis of a road safety measure.
文摘针对短采样宽带信号近似最大似然(approximated maximum likelihood,AML)方位估计计算量大的问题,将马尔科夫链-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Metropolis-Hastings抽样的近似最大似然方位估计方法(AMLMH)。该方法将AML算法的空间谱函数作为信号的概率分布函数,并利用Metropolis-Hastings抽样方法从该概率分布函数中抽样。研究结果表明,AMLMH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,而且减小了计算量。
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)。
