M滤波的自适应背景抑制算法

提出了一种M滤波的自适应背景抑制算法.该算法将目标和观测噪声作为图像背景的混合干扰,依据M估计原理自适应的估计真实背景.算法具有较好的稳健性能,能够自适应的低抗高强度的干扰.仿真和实验表明,与均值滤波(MF)、中值滤波(ModF)以及Wiener滤波(WF)相比,该算法能够更有效地从混合噪声环境下估计背景,增强目标信噪比(SNR).     

一种针对拖尾噪声的鲁棒神经网络信号检测算法

与高斯噪声相比,拖尾有更多的异常值,利用传统的神经网络不能有效的检测信号。该文提出一种基于中值滤波的鲁棒神经网络进行处理,首先利用中值滤波抑制异常值,进一步利用BP (Back Propagation) 神经网络消除残留噪声,检测目标信号。基于误差分析的实验结果表明,与传统神经网络相比,所提出的方法不仅能更好地消除拖尾噪声,有效检测信号,而且能有效检测高斯噪声中的目标信号,具有很好的鲁棒性和自适应特性。     

A complex adaptive notch filter using modified gradient algorithm

A modified gradient algorithm is developed for improving the convergence speed of a first-order complex adaptive IIR notch filter, which is used for estimating an unknown frequency of a complex sinusoidal signal embedded in white Gaussian noise. The new cost function using new error criterion is presented and analyzed theoretically. The proposed technique can significantly improve the convergence speed as compared with a complex notch filter using plain gradient algorithm. The computer simulations are conducted to demonstrate the validity of the proposed complex adaptive notch filter.     

基于非线性抗噪声估计的视觉显著性弱小目标检测

针对红外图像处理技术中弱小目标检测的重要性及关键性,提出一种基于非线性抗噪声估计的检测算法来解决高可靠性、高鲁棒性的弱小目标检测问题。提出的方法基于传统视觉显著度算法及空间距离处理方法,对目标及背景区域采用非线性加权方法进行估计,在不显著降低目标信号信噪比的基础上,削弱孤立微小噪声点对检测算法性能的影响,可提高抗噪性能。首先,采用模块化及非线性映射方式预测背景;然后,融入距离相关因子滤除噪声干扰;最后,在处理结束的图像上进行二值化阈值分割,自动检测并向下一级处理软件输出目标位置信息。实验结果表明:提出的算法与近年来先进的弱小目标检测算法相比,在受试者测试曲线上,在相同的虚警率下,可获得更高的检测率,对背景噪声的抑制很明显;在局部信噪比及背景抑制因子的测试比对数据上,提出的算法可获得更高的检测指标。缺点是算法采用了非线性处理技术,运算效率较低,需进一步优化算法以提高计算速度,实现算法的实时目标检测。     

Bias Removal Algorithm for 2-D Equation Error Adaptive IIR Filters

This paper proposes a bias removal algorithm for equation error-based 2-D adaptive cascade IIR filters with separable denominator function. As well known, equation error-based adaptive IIR filtering algorithms have the advantages of fast convergence and unimodal mean-square-error surface. These advantages, however, come along with the drawback of biased parameter estimates in the presence of measurement noise. The adaptive filter structure in the proposed algorithm is based on the concept of backpropagating the desired signal through a cascade of the denominator vertical and horizontal sections. To handle the bias problem, the proposed algorithm uses a scaled value of the output error of each of the cascaded sections as an estimate for the measurement noise embedded in the signal part of the coefficient-update procedure of that section. Thus, while maintaining the advantages of easy stability monitoring, fast convergence, and low computational load, the effect of the measurement noise is suppressed. Input-Output stability analysis is carried out, and the constraints required to maintain stability are derived. Simulation examples are presented to support the effectiveness and the usability of the proposed bias removal algorithm in 2-D system identification and image enhancement applications.     

基于匹配滤波的脆弱数字音频水印盲检测算法

根据原始水印构造匹配滤波器,并结合小波估计方法,提出脆弱音频水印的盲检测算法。在假定嵌入水印为噪声的情况下,利用小波不需要原始信号辅助信息即可估计该信号的特点,首先根据给定的估计门限估计了加入水印后的原始信号,从而实现了水印与音频信号分离。然后将水印与估计产生的噪声构成的混合信号送入构造的匹配滤波器作相关运算,根据滤波器的输出响应判断水印及载体是否受到攻击。实验结果表明,所提出方法对噪声、有损压缩、滤波、随机插入与删除等操作具有较高的敏感性。     

UFMC系统中的载波频率同步技术

针对UFMC系统对频率偏差敏感的问题,提出一种适合用于UFMC 系统的频率同步算法。为了保留UFMC系统的良好特性、降低UFMC中滤波器的设计复杂度,该算法设计了一种子带间正交的导频序列,并采用非线性最小二乘（NLS）法进行CFO估计。通过序列构造和计算该构造序列与估计信号的相关性对算法进一步改进,使得低信噪比下的估计性能得到改善。理论分析和仿真表明,在高斯和瑞利衰落信道下,子带间正交导频序列的误码率、CFO性能均优于全1导频序列;在低信噪比环境下,改进算法的CFO性能优于NLS算法。     

基于提升小波变换和中值滤波的图像去噪方法研究

针对实际拍摄的背景复杂、目标对比度和信噪比低的图像,在综合考滤图像去噪平滑效果、图像清晰程度和时间复杂度的基础上,提出一种基于提升小波变换和中值滤波的图像去噪方法.首先对含噪图像进行提升小波分解,再在图像高频部分进行中值滤波以改善图像的消噪效果,最后采用信噪比(SNR)与均方根误差(RMSE)和图像灰度曲面图作为图像去噪效果的评估,将提升小波变换和中值滤波相结合的图像去噪方法与小波去噪、小波与中值滤波结合消噪等进行对比实验.实验结果表明,该方法既能消除图像噪声又能达到保持其图像边缘要求,且时间度较低.     

New unbiased adaptive IIR filter: it’s robust and variable step-size versions and application to active noise control

This paper proposes a robust variable step-size adaptive IIR filter realized by a new bias-free structure (BFS). Unlike equation error (EQE) method that uses a desired signal contaminated with observation noise, the BFS employs a filter driven by the output of the plant estimate and this achieves a bias-free estimate of the denominator of the system function. In addition, the adaptation is made robust to the observation noise by the Griffiths’ LMS adaptation, which uses the cross-correlation estimate between the input and the desired signal for its adaptation gradient computation. A robust variable step-size adaptation is also realized by the Griffiths’ gradient. The proposed structure is referred to as BFSGV and has good modeling capability with improved convergence rate and reduced misadjustment. For system identification, the proposed BFSGV algorithm gives a 3 dB improvement in the performance index over EQE method. The proposed BFSGV has been applied to active noise control (ANC). The BSFGV structure is used for secondary path (SP) estimation, and for the main path (MP), BFS structure with step-size varied according to Okello’s method (BSFV) is used. The new ANC system for narrowband noise field is found to be having 4 times faster convergence rate and an additional noise reduction of 15dB over that FIR for MP and the EQE for SP. Further, the use of the proposed ANC IIR algorithm achieves computational savings compared to that of FIR. For the broadband noise field, the proposed method that uses BSFV for MP and BSFGV for SP provides 18 times faster convergence rate and 2.5 dB reduction in ANC error compare to that of the ANC using FIR for MP and the EQE for SP estimation.     

基于最大熵Kalman滤波的机载SINS自标定技术

针对在机载捷联惯导系统(SINS)自标定过程中,量测噪声呈非高斯分布,导致经典Kalman滤波性能降低的问题,该文提出了基于最大熵Kalman滤波(MCKF)的机载SINS自标定技术。该方法采用最大相关熵准则(MCC)替代经典Kalman滤波的最小均方误差准则,有效利用信号的高阶矩信息,并将其应用于机载SINS自标定系统中。仿真结果表明,在非高斯噪声条件下,该方法能够估计出机载SINS待标定参数,且算法的鲁棒性和误差项估计精度均优于经典Kalman滤波,具有一定的工程应用价值。