Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning thi...Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.展开更多
针对阵列信号处理领域中的超分辨子空间类算法需计算阵列输出的协方差、协方差矩阵的特征分解及进行谱峰搜索得到波达方向(Direction of Arrival,DOA)估计,计算量较大则实际应用可能受限,提出了一种低计算复杂度的新颖的无需谱峰搜索的...针对阵列信号处理领域中的超分辨子空间类算法需计算阵列输出的协方差、协方差矩阵的特征分解及进行谱峰搜索得到波达方向(Direction of Arrival,DOA)估计,计算量较大则实际应用可能受限,提出了一种低计算复杂度的新颖的无需谱峰搜索的DOA估计算法。通过Nyström方法得到逼近的信号子空间,避免了直接对所有阵列输出计算协方差及对其特征分解从而降低了运算量。通过逼近的信号子空间构建低阶的关于DOA的特征多项式方程,对此低阶多项式方程求根得到DOA估计进一步降低了运算量,且不同于现有的DOA估计求根算法。理论分析和仿真结果表明,所提算法有着良好的估计精度及较低的计算复杂度。展开更多
The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)...The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants No.62027803,No.61601096,No.61971111,and No.61801089in part by the Science and Technology Program under Grants No.8091C24,No.2021JCJQJJ0949,and No.2022JCJQJJ0784in part by the Industrial Technology Development Program under Grant No.2020110C041.
文摘Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.
基金Supported by Projects from NSF of China(10571147)Specialized Research Fund for Doctoral Programof Higher Education of China(20094301110001)+1 种基金NSF of Hunan Province (09JJ3002)Hunan Provincial Innovation Foundation for Postgraduate(S2008yjscx02)
文摘针对阵列信号处理领域中的超分辨子空间类算法需计算阵列输出的协方差、协方差矩阵的特征分解及进行谱峰搜索得到波达方向(Direction of Arrival,DOA)估计,计算量较大则实际应用可能受限,提出了一种低计算复杂度的新颖的无需谱峰搜索的DOA估计算法。通过Nyström方法得到逼近的信号子空间,避免了直接对所有阵列输出计算协方差及对其特征分解从而降低了运算量。通过逼近的信号子空间构建低阶的关于DOA的特征多项式方程,对此低阶多项式方程求根得到DOA估计进一步降低了运算量,且不同于现有的DOA估计求根算法。理论分析和仿真结果表明,所提算法有着良好的估计精度及较低的计算复杂度。
文摘The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.
