This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processo...This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processor’s random output comprises highly accurate closed-form approximations for the probability density function and the autocorrelation function. The solution supports the complex stretch processor’s usage of any conventional range-sidelobe-reduction window. The paper then identifies two practical applications of the derived description. Digital-simulation results for the two identified applications, assuming the complex stretch processor uses the rectangular, Hamming, Blackman, or Kaiser window, verify the derivation’s correctness through favorable comparison to the theoretically predicted behavior.展开更多
针对自主水下机器人在极地漂移海冰特征扫描中面临的路径规划滞后、避障困难及扫描覆盖率低等问题,提出一种融合高斯过程回归(Gaussian Process Regression,GPR)、快速探索随机树(Rapidly-exploring Random Tree Star,RRT*)和动态窗口法...针对自主水下机器人在极地漂移海冰特征扫描中面临的路径规划滞后、避障困难及扫描覆盖率低等问题,提出一种融合高斯过程回归(Gaussian Process Regression,GPR)、快速探索随机树(Rapidly-exploring Random Tree Star,RRT*)和动态窗口法(Dynamic Window Approach,DWA)的扫描方法,实现漂移海冰底部区域的全覆盖探测。首先,利用GPR对海冰历史轨迹建模,并预测其短时位置变化。随后,将预测位置作为目标区域,结合人工势场法优化RRT*的节点扩展策略,生成全局扫描路径。在局部路径规划中,DWA以RRT*生成的全局路径为引导,结合邻近惩罚区域和一致性评价函数,实现动态避障并提高路径跟踪精度。仿真实验结果表明,所提融合算法在扫描覆盖率、避障性能和规划效率方面均优于传统方法,适用于漂移海冰底部的全覆盖扫描。展开更多
Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak t...Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.展开更多
针对复合电能质量扰动检测算法实时性差、时频分辨率低的问题,提出了一种基于改进自适应S变换(improved adaptive S transform, IAST)的电能质量扰动实时检测方法。构建全局自适应高斯窗作为IAST的核函数,可随检测频率变化自适应调整窗...针对复合电能质量扰动检测算法实时性差、时频分辨率低的问题,提出了一种基于改进自适应S变换(improved adaptive S transform, IAST)的电能质量扰动实时检测方法。构建全局自适应高斯窗作为IAST的核函数,可随检测频率变化自适应调整窗函数有效窗长及频谱,避免为提高时频分辨率频繁切换窗口参数,降低算法复杂度。以增强信号能量集中度为参数调优目标选取窗口参数,确保对各类扰动的精确时频定位。采用自动阈值法确定实际扰动信号的主频点,并对主频点进行时频变换,进一步提高算法执行效率。仿真和实测结果表明,相比于现有复合电能质量扰动检测算法,该检测方法实时性好、时频分辨能力强、计算复杂度低,适用于复杂电能质量扰动实时准确检测。展开更多
局部放电(partial discharge,PD)特高频(ultra high frequency,UHF)信号检测过程易受到白噪声和周期性窄带干扰的严重影响。为有效提取PD UHF信号、抑制干扰,提出一种基于奇异值分解(singular value decomposition,SVD)和低秩径向基函数...局部放电(partial discharge,PD)特高频(ultra high frequency,UHF)信号检测过程易受到白噪声和周期性窄带干扰的严重影响。为有效提取PD UHF信号、抑制干扰,提出一种基于奇异值分解(singular value decomposition,SVD)和低秩径向基函数(radical basis function,RBF)神经网络的去噪方法。首先,将染噪局部放电信号构造为Hankel矩阵,并奇异分解到特征矩阵空间;然后,把特征矩阵中奇异值突变点设为阈值,以去除窄带干扰;最后,采用RBF神经网络逼近去干扰后的PD信号,并采用Gaussian窗滤波以提取局放信号。所提方法与逆向分离(reverse separation,RS)和形态学小波综合滤波器(morphology wavelet filter,MWF)进行对比。从仿真和实测结果表明,该方法对周期性窄带干扰和白噪声有着强抑制作用,评价指标更为显著。展开更多
文摘This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processor’s random output comprises highly accurate closed-form approximations for the probability density function and the autocorrelation function. The solution supports the complex stretch processor’s usage of any conventional range-sidelobe-reduction window. The paper then identifies two practical applications of the derived description. Digital-simulation results for the two identified applications, assuming the complex stretch processor uses the rectangular, Hamming, Blackman, or Kaiser window, verify the derivation’s correctness through favorable comparison to the theoretically predicted behavior.
文摘针对自主水下机器人在极地漂移海冰特征扫描中面临的路径规划滞后、避障困难及扫描覆盖率低等问题,提出一种融合高斯过程回归(Gaussian Process Regression,GPR)、快速探索随机树(Rapidly-exploring Random Tree Star,RRT*)和动态窗口法(Dynamic Window Approach,DWA)的扫描方法,实现漂移海冰底部区域的全覆盖探测。首先,利用GPR对海冰历史轨迹建模,并预测其短时位置变化。随后,将预测位置作为目标区域,结合人工势场法优化RRT*的节点扩展策略,生成全局扫描路径。在局部路径规划中,DWA以RRT*生成的全局路径为引导,结合邻近惩罚区域和一致性评价函数,实现动态避障并提高路径跟踪精度。仿真实验结果表明,所提融合算法在扫描覆盖率、避障性能和规划效率方面均优于传统方法,适用于漂移海冰底部的全覆盖扫描。
文摘Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.
文摘针对复合电能质量扰动检测算法实时性差、时频分辨率低的问题,提出了一种基于改进自适应S变换(improved adaptive S transform, IAST)的电能质量扰动实时检测方法。构建全局自适应高斯窗作为IAST的核函数,可随检测频率变化自适应调整窗函数有效窗长及频谱,避免为提高时频分辨率频繁切换窗口参数,降低算法复杂度。以增强信号能量集中度为参数调优目标选取窗口参数,确保对各类扰动的精确时频定位。采用自动阈值法确定实际扰动信号的主频点,并对主频点进行时频变换,进一步提高算法执行效率。仿真和实测结果表明,相比于现有复合电能质量扰动检测算法,该检测方法实时性好、时频分辨能力强、计算复杂度低,适用于复杂电能质量扰动实时准确检测。
