针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法...针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法的思想对稀疏信号进行重构,得到信号在空域的稀疏系数矩阵;最后,将稀疏矩阵行向量的l2-范数映射到空域网格上,得到准确的DOA估计值.仿真实验结果表明:在低信噪比、小快拍、多信源条件下,该方法优于子空间类算法、贪婪类算法以及凸优化类算法,具有更低的均方根误差(RMSE)、更高的DOA估计精度和运行效率.展开更多
Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of n...Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.展开更多
For multi-channel synthetic aperture radar（SAR） systems, since the minimum antenna area constraint is eliminated,wide swath and high resolution SAR image can be achieved.However, the unavoidable array errors, consis...For multi-channel synthetic aperture radar（SAR） systems, since the minimum antenna area constraint is eliminated,wide swath and high resolution SAR image can be achieved.However, the unavoidable array errors, consisting of channel gainphase mismatch and position uncertainty, significantly degrade the performance of such systems. An iteration-free method is proposed to simultaneously estimate position and gain-phase errors.In our research, the steering vectors corresponding to a pair of Doppler bins within the same range bin are studied in terms of their rotational relationships. The method is based on the fact that the rotational matrix only depends on the position errors and the frequency spacing between the paired Doppler bins but is independent of gain-phase error. Upon combining the projection matrices corresponding to the paired Doppler bins, the position errors are directly obtained in terms of extracting the rotational matrix in a least squares framework. The proposed method, when used in conjunction with the self-calibration algorithm, performs stably as well as has less computational load, compared with the conventional methods. Simulations reveal that the proposed method behaves better than the conventional methods even when the signal-to-noise ratio（SNR） is low.展开更多
文摘针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法的思想对稀疏信号进行重构,得到信号在空域的稀疏系数矩阵;最后,将稀疏矩阵行向量的l2-范数映射到空域网格上,得到准确的DOA估计值.仿真实验结果表明:在低信噪比、小快拍、多信源条件下,该方法优于子空间类算法、贪婪类算法以及凸优化类算法,具有更低的均方根误差(RMSE)、更高的DOA估计精度和运行效率.
基金supported by the National Natural Science Foundation of China(6100115361271415)+2 种基金the Opening Research Foundation of State Key Laboratory of Underwater Information Processing and Control(9140C231002130C23085)the Fundamental Research Funds for the Central Universities(3102014JCQ010103102014ZD0041)
文摘Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China(2015JM6278)the China Postdoctoral Science Foundation(2015M582586)the China Academy of Space Technology Innovation Fund
文摘For multi-channel synthetic aperture radar（SAR） systems, since the minimum antenna area constraint is eliminated,wide swath and high resolution SAR image can be achieved.However, the unavoidable array errors, consisting of channel gainphase mismatch and position uncertainty, significantly degrade the performance of such systems. An iteration-free method is proposed to simultaneously estimate position and gain-phase errors.In our research, the steering vectors corresponding to a pair of Doppler bins within the same range bin are studied in terms of their rotational relationships. The method is based on the fact that the rotational matrix only depends on the position errors and the frequency spacing between the paired Doppler bins but is independent of gain-phase error. Upon combining the projection matrices corresponding to the paired Doppler bins, the position errors are directly obtained in terms of extracting the rotational matrix in a least squares framework. The proposed method, when used in conjunction with the self-calibration algorithm, performs stably as well as has less computational load, compared with the conventional methods. Simulations reveal that the proposed method behaves better than the conventional methods even when the signal-to-noise ratio（SNR） is low.
