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
高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问...高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问题,提出一种基于GPR的用户量预测优化方法。在滑动窗口方法处理数据的基础上,选择合适的核函数,基于k折交叉验证得到最佳超参数组合以实现GPR模型训练,完成在线用户量的实时预测并进行性能评估。实验结果表明,相比于采用训练集中输出数据方差的50%作为信号噪声估计量的传统方案,所提方法具有较高的预测准确度,并且在测试集均方根误差(root mean square,RMS)、平均绝对误差(mean absolute error,MAE)、平均偏差(mean bias error,MBE)和决定系数R 2这4个评估指标方面均有提升,其中MBE至少提升了43.3%。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
弹药跌落环境模拟系统主要用于对各种弹药(箱装或裸弹)、引信、仪器设备等进行跌落试验。针对野外复杂背景下高速相机采集的目标跌落图像噪声大、边缘复杂不易提取且图像数据量大等问题,设计了融合高斯核函数和边窗思想的滤波算法、基...弹药跌落环境模拟系统主要用于对各种弹药(箱装或裸弹)、引信、仪器设备等进行跌落试验。针对野外复杂背景下高速相机采集的目标跌落图像噪声大、边缘复杂不易提取且图像数据量大等问题,设计了融合高斯核函数和边窗思想的滤波算法、基于感兴趣区域(region of interest,ROI)的改进Hough直线检测算法以及基于序列图像ROI动态更新的边缘直线自动提取算法,解决了去噪过程中目标边缘扩散和序列图像姿态角解算耗时长的问题。实验表明,所提出的改进算法保护了高噪声图像的边缘特征,直线检测耗时低于标准Hough变换耗时的1/10,实现了多帧图像运动目标姿态角的快速自动提取,姿态角解算平均绝对误差为0.36%。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.
文摘高斯过程回归(Gaussian process regression,GPR)是一种基于高斯过程的非参数化贝叶斯回归方法,其可以灵活适应不同类型数据,用于建模和预测数据之间的复杂关系,具有拟合能力强、泛化能力好等特点。针对海量用户场景下用户量实时预测问题,提出一种基于GPR的用户量预测优化方法。在滑动窗口方法处理数据的基础上,选择合适的核函数,基于k折交叉验证得到最佳超参数组合以实现GPR模型训练,完成在线用户量的实时预测并进行性能评估。实验结果表明,相比于采用训练集中输出数据方差的50%作为信号噪声估计量的传统方案,所提方法具有较高的预测准确度,并且在测试集均方根误差(root mean square,RMS)、平均绝对误差(mean absolute error,MAE)、平均偏差(mean bias error,MBE)和决定系数R 2这4个评估指标方面均有提升,其中MBE至少提升了43.3%。
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.
文摘弹药跌落环境模拟系统主要用于对各种弹药(箱装或裸弹)、引信、仪器设备等进行跌落试验。针对野外复杂背景下高速相机采集的目标跌落图像噪声大、边缘复杂不易提取且图像数据量大等问题,设计了融合高斯核函数和边窗思想的滤波算法、基于感兴趣区域(region of interest,ROI)的改进Hough直线检测算法以及基于序列图像ROI动态更新的边缘直线自动提取算法,解决了去噪过程中目标边缘扩散和序列图像姿态角解算耗时长的问题。实验表明,所提出的改进算法保护了高噪声图像的边缘特征,直线检测耗时低于标准Hough变换耗时的1/10,实现了多帧图像运动目标姿态角的快速自动提取,姿态角解算平均绝对误差为0.36%。
文摘针对传统边缘检测方法极易受高斯噪声和椒盐噪声影响导致的伪边缘问题,提出一种各向异性双窗非线性滤波的边缘检测算法。首先,构建各向异性高斯双窗滤波器,并结合灰度分层技术计算每个像素的非线性方向导数向量和边缘强度图(Edge Strength Map,ESM)。然后,根据导数向量最大值估计边缘方向图(Edge Direction Map,EDM)。最后,通过非极大值抑制和双阈值决策获得最终的边缘检测图。对比实验结果表明,所提算法在无噪声、高斯噪声、椒盐噪声和混合噪声干扰的环境下表现优异,尤其在混合噪声下的边缘检测结果明显优于其他算法,具有较强的混合噪声鲁棒性。
