Most of the reconstruction-based robust adaptive beamforming(RAB)algorithms require the covariance matrix reconstruction(CMR)by high-complexity integral computation.A Gauss-Legendre quadrature(GLQ)method with the high...Most of the reconstruction-based robust adaptive beamforming(RAB)algorithms require the covariance matrix reconstruction(CMR)by high-complexity integral computation.A Gauss-Legendre quadrature(GLQ)method with the highest algebraic precision in the interpolation-type quadrature is proposed to reduce the complexity.The interference angular sector in RAB is regarded as the GLQ integral range,and the zeros of the threeorder Legendre orthogonal polynomial is selected as the GLQ nodes.Consequently,the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral.The new method has significantly reduced the complexity as compared to most state-of-the-art reconstruction-based RAB techniques,and it is able to provide the similar performance close to the optimal.These advantages are verified by numerical simulations.展开更多
Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face ...Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions.This paper presents an effective DOA estimation approach based on a deep residual network(DRN)for the underdetermined case.We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays.We then provide the input feature to the trained DRN to construct the super resolution spectrum.The DRN learns the mapping relationship between the input feature and the spatial spectrum by training.The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency,independence of source sparseness and adaptive capacity to non-ideal conditions(e.g.,low signal to noise ratio,short bit sequence).Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.展开更多
针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出...针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出了新的频域协方差矩阵,利用平均噪声子空间建立空间谱估计函数,通过谱峰搜索估计出信源的方位角。经仿真对比分析,所提改进方法可以识别多个相干信号,并且在低信噪比、少快拍数条件下仍然获得较好的方位估计性能,估计误差较传统算法降低2%~25%。展开更多
由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方...由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方差矩阵;利用该协方差矩阵对接收信号进行“白化”抑制;利用KF进行室内定位,同时针对KF滤波发散、误差较大的问题,利用RBF神经网络对误差进行在线修正,提升滤波性能。实验结果表明,该方法在NLOS环境下能够获得亚米级的定位精度,并具有较强的环境适应性。展开更多
基金supported by the National Natural Science Foundation of China(618711496197115962071144)。
文摘Most of the reconstruction-based robust adaptive beamforming(RAB)algorithms require the covariance matrix reconstruction(CMR)by high-complexity integral computation.A Gauss-Legendre quadrature(GLQ)method with the highest algebraic precision in the interpolation-type quadrature is proposed to reduce the complexity.The interference angular sector in RAB is regarded as the GLQ integral range,and the zeros of the threeorder Legendre orthogonal polynomial is selected as the GLQ nodes.Consequently,the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral.The new method has significantly reduced the complexity as compared to most state-of-the-art reconstruction-based RAB techniques,and it is able to provide the similar performance close to the optimal.These advantages are verified by numerical simulations.
基金supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(2019JJ10004)。
文摘Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions.This paper presents an effective DOA estimation approach based on a deep residual network(DRN)for the underdetermined case.We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays.We then provide the input feature to the trained DRN to construct the super resolution spectrum.The DRN learns the mapping relationship between the input feature and the spatial spectrum by training.The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency,independence of source sparseness and adaptive capacity to non-ideal conditions(e.g.,low signal to noise ratio,short bit sequence).Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.
文摘针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出了新的频域协方差矩阵,利用平均噪声子空间建立空间谱估计函数,通过谱峰搜索估计出信源的方位角。经仿真对比分析,所提改进方法可以识别多个相干信号,并且在低信噪比、少快拍数条件下仍然获得较好的方位估计性能,估计误差较传统算法降低2%~25%。
文摘由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方差矩阵;利用该协方差矩阵对接收信号进行“白化”抑制;利用KF进行室内定位,同时针对KF滤波发散、误差较大的问题,利用RBF神经网络对误差进行在线修正,提升滤波性能。实验结果表明,该方法在NLOS环境下能够获得亚米级的定位精度,并具有较强的环境适应性。
