Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative i...Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.展开更多
利用地基观测相机拍摄的以深空为背景的星图受星空复杂背景的影响,往往具有较高的噪声水平。同时由于星图主要由恒星、空间目标和星空背景噪声组成,且成点状分布,星图目标和噪声呈现较大的相似性,传统的图像去噪算法并不适用于星图。为...利用地基观测相机拍摄的以深空为背景的星图受星空复杂背景的影响,往往具有较高的噪声水平。同时由于星图主要由恒星、空间目标和星空背景噪声组成,且成点状分布,星图目标和噪声呈现较大的相似性,传统的图像去噪算法并不适用于星图。为此,该文提出一种基于能量函数的极值中值滤波去噪算法,该算法在去除星图椒盐噪声的同时能够较好地保持图像目标信息。该方法针对疑似噪声点采用二次检测的方式,并且结合改进的自适应中值滤波和能量函数模型进行灰度值恢复。该文分别使用仿真试验和真实星图处理试验对该方法进行验证,在客观评价中,图像峰值信噪比PSNR(Peak Signal to Noise Ratio)最高可提高3倍多,均方误差MSE(Mean Squared Error)减小为加噪图像的3.16×10^(-5)。试验结果表明,该方法可有效地降低传统方法的噪声误检问题,同时提高噪声图像的恢复精度,非常适合星图噪声的去除。展开更多
基金Project(61201381) supported by the National Natural Science Foundation of ChinaProject(YP12JJ202057) supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.
文摘利用地基观测相机拍摄的以深空为背景的星图受星空复杂背景的影响,往往具有较高的噪声水平。同时由于星图主要由恒星、空间目标和星空背景噪声组成,且成点状分布,星图目标和噪声呈现较大的相似性,传统的图像去噪算法并不适用于星图。为此,该文提出一种基于能量函数的极值中值滤波去噪算法,该算法在去除星图椒盐噪声的同时能够较好地保持图像目标信息。该方法针对疑似噪声点采用二次检测的方式,并且结合改进的自适应中值滤波和能量函数模型进行灰度值恢复。该文分别使用仿真试验和真实星图处理试验对该方法进行验证,在客观评价中,图像峰值信噪比PSNR(Peak Signal to Noise Ratio)最高可提高3倍多,均方误差MSE(Mean Squared Error)减小为加噪图像的3.16×10^(-5)。试验结果表明,该方法可有效地降低传统方法的噪声误检问题,同时提高噪声图像的恢复精度,非常适合星图噪声的去除。
