低信噪比(signal-to-noise ratio,SNR)或小接收快拍数条件下,经典的二维(two-dimensional,2D)波达方向(direction of arrival,DOA)算法存在估计精度低的缺点。针对该问题,充分利用L型阵列接收数据的自、互相关信息,提出一种适用于低SNR...低信噪比(signal-to-noise ratio,SNR)或小接收快拍数条件下,经典的二维(two-dimensional,2D)波达方向(direction of arrival,DOA)算法存在估计精度低的缺点。针对该问题,充分利用L型阵列接收数据的自、互相关信息,提出一种适用于低SNR及小接收快拍数环境下的2D DOA估计新方法。该方法首先通过解析优化2D谱峰搜索问题,获得方位角与仰角之间的特定约束关系,进而将包含2D角度参量的目标函数转化为只包含一维(one-dimensional,1D)角度参量,即可通过1D谱峰搜索获得方位角(或仰角)估计值,最后再次利用该约束关系求得与之对应的仰角(或方位角)估计值。该方法只需1D谱峰搜索,而且所得2D角度估计参数可自动实现配对。计算机仿真验证了该方法在低SNR及小接收快拍数情况下的有效性。展开更多
针对相干源方位估计问题,本文在PVFS(Particle Velocity Field Smoothing)算法的基础上,提出一种新的算法。该算法通过对PVFS算法构造出的协方差矩阵进行特征值分解,利用得到的特征值及特征向量构造新的噪声子空间,然后运用子空间原理...针对相干源方位估计问题,本文在PVFS(Particle Velocity Field Smoothing)算法的基础上,提出一种新的算法。该算法通过对PVFS算法构造出的协方差矩阵进行特征值分解,利用得到的特征值及特征向量构造新的噪声子空间,然后运用子空间原理实现相干源的方位估计。该算法无需已知相干源的信源数目且不会损失阵列孔径,具有较好的相干源方位估计性能,计算机仿真结果验证了本文算法的有效性。展开更多
Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the co...Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density （PSD） is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.展开更多
We consider the MAP/PH/N retrial queue with a finite number of sources operating in a finite state Markovian random environment. Two different types of multi-dimensional Markov chains are investigated describing the b...We consider the MAP/PH/N retrial queue with a finite number of sources operating in a finite state Markovian random environment. Two different types of multi-dimensional Markov chains are investigated describing the behavior of the system based on state space arrangements. The special features of the two formulations are discussed. The algorithms for calculating the stationary state probabilities are elaborated, based on which the main performance measures are obtained, and numerical examples are presented as well.展开更多
文摘低信噪比(signal-to-noise ratio,SNR)或小接收快拍数条件下,经典的二维(two-dimensional,2D)波达方向(direction of arrival,DOA)算法存在估计精度低的缺点。针对该问题,充分利用L型阵列接收数据的自、互相关信息,提出一种适用于低SNR及小接收快拍数环境下的2D DOA估计新方法。该方法首先通过解析优化2D谱峰搜索问题,获得方位角与仰角之间的特定约束关系,进而将包含2D角度参量的目标函数转化为只包含一维(one-dimensional,1D)角度参量,即可通过1D谱峰搜索获得方位角(或仰角)估计值,最后再次利用该约束关系求得与之对应的仰角(或方位角)估计值。该方法只需1D谱峰搜索,而且所得2D角度估计参数可自动实现配对。计算机仿真验证了该方法在低SNR及小接收快拍数情况下的有效性。
文摘针对相干源方位估计问题,本文在PVFS(Particle Velocity Field Smoothing)算法的基础上,提出一种新的算法。该算法通过对PVFS算法构造出的协方差矩阵进行特征值分解,利用得到的特征值及特征向量构造新的噪声子空间,然后运用子空间原理实现相干源的方位估计。该算法无需已知相干源的信源数目且不会损失阵列孔径,具有较好的相干源方位估计性能,计算机仿真结果验证了本文算法的有效性。
基金This project is supported by National Natural Science Foundation of China(No.50675076).
文摘Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density （PSD） is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.
基金Supported by National Social Science Foundation of China(No.11BTJ011)Humanities and Social Sciences Foundation of Ministry of Education of China,2012(No.12YJAZH173)
文摘We consider the MAP/PH/N retrial queue with a finite number of sources operating in a finite state Markovian random environment. Two different types of multi-dimensional Markov chains are investigated describing the behavior of the system based on state space arrangements. The special features of the two formulations are discussed. The algorithms for calculating the stationary state probabilities are elaborated, based on which the main performance measures are obtained, and numerical examples are presented as well.
