基于Alpha稳定分布杂波模型的雷达目标检测方法

针对非高斯相关杂波背景下,移动目标检测(MTD)技术的检测性能严重下降的问题,该文基于Alpha稳定分布杂波模型和本征滤波理论,提出一种非高斯相关杂波背景下的雷达目标检测方法。该方法基于Alpha稳定分布杂波模型,通过幂变换抑制杂波的非高斯特性,以及通过分数低阶相关矩阵白化杂波,在此基础上应用本征滤波实现对目标信号的有效积累,可提高信杂比。仿真实验和实测数据验证表明,该方法在非高斯相关杂波背景下的检测性能明显优于MTD方法的性能。     

基于分数低阶本征滤波的雷达目标检测方法

在非高斯相关杂波背景下,通常杂波分布的概率密度函数结构复杂甚至无闭式表达,难以建立统计检测模型。针对此问题,以α稳定分布为背景,基于分数低阶统计量和最佳滤波器理论,以滤波器输出分数低阶信杂比最大为准则,给出了一种分数低阶本征滤波(FLOEF)模型。该模型利用杂波的分数低阶协方差矩阵对非高斯相关杂波进行白化,可显著改善信杂比,实现非高斯相关杂波背景下雷达目标的有效检测。通过仿真和实测数据给出了FLOEF在不同条件下的检测性能,并同传统基于二阶统计量的本征滤波进行了比较,结果验证了FLOEF的优越性。     

非高斯背景下基于Sigmoid函数的信号检测

针对非高斯背景下的弱信号检测问题,该文提出一种基于Sigmoid函数的信号检测(SFD)方法。首先依据混合高斯模型对非高斯背景建模,在此基础上系统研究了参数k与SFD的检测性能以及检测特性的关系,确定了k的最佳的取值,并指出SFD在检测性能达到最优的同时也具有恒虚警特性。其次通过固定k值得到了一种新的非参量检测方法,较传统的匹配滤波性能有明显提升。最后进行仿真分析验证了SFD的有效性和优越性。     

一种局部最优的非高斯接收机设计

在超低频通信中,由于非高斯噪声的严重干扰,传统的基于高斯噪声的接收机的性能恶化.本文提出了一种在非高斯噪声环境下局部最优的接收机设计,具体推导了局部最优检测在非高斯噪声模型下的FFT快速实现算法,该算法充分利用了FFT算法在运算速度上的优势,大大降低了运算复杂度.非高斯噪声模型采用广泛应用的Class B统计物理模型.仿真实验表明,局部最优接收机性能明显优于传统的匹配滤波检测器,运算速度快且易于硬件设计实现,有很高的实用价值.     

有色非高斯背景下微弱信号的Rao有效绩检验

混合高斯Rao有效绩检验是实现有色非高斯背景下微弱信号检测的渐近最佳检测器,预白化和高斯化技术的应用使得它的检测性优于传统的匹配滤波器.在使用混合高斯自回归模型描述检测问题之后,基于功率谱密度和概率密度参数估计,引入预白化和高斯化滤波器,构建起模块化的、实用的混合高斯Rao有效绩检测器.然后对其检测性能进行了深入分析,揭示了预白化和高斯化技术改善检测性能的机理所在.最后给出了一组湖试数据检测实例.     

复杂海杂波背景下分数低阶匹配滤波检测方法

针对在复杂海杂波背景下,雷达目标检测中动目标检测(Moving Target Detection,MTD)技术的检测性能显著下降的问题,以及局部最优检测器(Locally Optimum Detector,LOD)仅适用于低信杂比背景下弱目标检测的问题,基于分数低阶统计量理论,提出了一种分数低阶匹配滤波检测方法.该方法通过幂变换抑制杂波的非高斯特性,通过应用杂波分数低阶协方差矩阵特征值分解的方法白化相关杂波,在此基础上应用匹配滤波进行目标积累,以提高信杂比.通过仿真和实测数据,对所提出方法的检测性能进行了验证,并且与MTD和LOD进行了比较.结果表明,本文所提出方法能较好地解决非高斯相关杂波背景下的目标检测问题,检测性能明显优于MTD和LOD方法.     

非高斯杂波下雷达目标跟踪算法改进研究

针对杂波干扰环境中的非高斯特性,发现海杂波噪声、闪烁噪声等具有显著尖峰的非高斯噪声可以采用α稳定分布来描述,用α稳定分布可以建立更符合实际的噪声模型。根据统计信号处理最新理论和技术,利用p阶分数相关和分数低阶协方差替代传统相关和协方差来改进Kalman滤波器,优化获得改进的基于分数低阶统计量Kalman滤波交互多模型算法(Based FLOS-Kalman-IMM),仿真验证了Based FLOS-Kalman-IMM滤波跟踪新算法可以更好地适应非高斯复杂环境,得到稳健的雷达跟踪效果。     

复合高斯杂波下抑制失配信号的自适应检测器

随着雷达分辨率的提高及擦地角的减小,海杂波幅度分布明显偏离瑞利分布,表现出很强的非高斯特性,复合高斯模型得到广泛应用。因此该文以复合高斯杂波为背景,研究当信号发生失配时的雷达目标检测问题。该文基于两步广义似然比(GLRT)检验,设计了复合高斯杂波下对失配信号具有选择性的自适应检测器。为了设计选择性检测器,在零假设下引入虚假干扰以修正原始二元假设,并假设该虚假干扰与实际目标信号在白化空间正交。该文提出的检测器对海杂波纹理分量及协方差矩阵恒虚警(CFAR)。最后利用仿真及实测海杂波数据,通过蒙特卡洛实验验证该检测器的有效性。实验表明,该文所提检测器有效提高了对失配信号的选择性,同时对距离扩展目标匹配信号的检测性能也有1～3 dB的提升。      

一种基于归一化峰度的抗非高斯噪声语音VAD检测方法

在多媒体会议房间中,鼓掌、咳嗽等非高斯干扰噪声常会严重影响语音处理系统的性能.为了有效地抑制非高斯干扰噪声,本文提出了一种基于线性预测残差域高阶统计量的语音VAD检测方法.该方法利用语音信号线性预测残差的归一化峰度表征语音和非语音信号在谐波数量上的差异,构造判别准则进行VAD检测,并通过预估高斯背景噪声的能量,削弱了背景噪声对VAD算法性能的影响.仿真实验结果表明,该方法能够有效地区分高斯背景噪声下的语音和非高斯噪声.     

一种MIMO雷达非高斯杂波中的信号分离方法

针对传统匹配滤波方法对非高斯相关杂波下MIMO雷达信号分离性能下降的问题,本文基于线性约束最小功率波束形成器原理,给出了一种适用于非高斯相关杂波条件下的MIMO雷达自适应信号分离算法。该方法将需要分离的发射信号以外的信号和相关杂波作为干扰,利用接收信号的分数低阶统计量在线性约束最小功率准则下导出了滤波器权系数,实现了非高斯相关杂波中对信号实时有效的分离。最后采用本文方法在SG-Alpha稳定分布杂波背景下进行了仿真实验,结果表明,本文方法可有效实现非高斯相关杂波中的MIMO雷达信号分离。      

SAR图像的Gamma混合分布建模方法

随机信号的混合概率模型比单一概率模型具有更多的灵活性,更适合复杂的分布建模。当前主要的混合概率模型有高斯混合模型、α分布混合模型和Gamma混合模型等。但高斯混合模型更适合随机变量对称分布的分布建模,而α混合模型参数多、算法复杂。SAR图像的像素值为非负值,且多为斜峰分布,更适合用Gamma混合模型建模。仿真分析及数据测试都表明,本文提出的gamma混合分布建模方法对SAR图像的像素统计分布具有更高的运算效率。      

Matched median filter for detecting QAM signals

In this paper, the complex matched median filter (MMF) is developed for QAM signal detection. It is shown that the MMF is robust against impulsive type noise. By combining the MMF and the linear matched filter (LMF), an extended class of matched filters is introduced. These filters combine the desirable properties of MMF and LMF and behave well in varying noise environments. Computer simulations demonstrate that the proposed detectors give a much smaller symbol error probability than the LMF when the noise has an impulsive component and produces only a slight performance degradation in the case of pure Gaussian noise     

Partial update and sparse adaptive filters

There has been increasing research interest in developing adaptive filters with partial update (PU) and adaptive filters for sparse impulse responses. On the basis of maximum a posteriori (MAP) estimation, new adaptive filters are developed by determining the update when a new set of training data is received. The MAP estimation formulation permits the study of a number of different prior distributions which naturally incorporate the sparse property of the filter coefficients. First, the Gaussian prior is studied, and a new adaptive filter with PU is proposed. A theoretical basis for an existing PU adaptive filter is also studied. Then new adaptive filters that directly exploit the sparsity of the filter are developed by using the scale mixture Gaussian distribution as the prior. Two new algorithms based on the Student's-t and power-exponential distributions are presented. The minorisation-maximisation algorithm is employed as an optimisation tool. Simulation results show that the learning performance of the proposed algorithms is better than or similar to that of some recently published algorithms     

Optimum radar signal processing in clutter

This paper considers the joint optimization of a class of radar signals and filters in a number of clutter-pins-noise environments. The radar signal processor in this case will be optimum in the sense that its output at the time of target detection yields the maximum ratio of peak signal power to total interference power. If the interference at the input to this signal processor is a Gaussian random process, this processor also yields the maximum probability of detection for a given value of false-alarm probability. The signals used are pulse trains and the filters are tapped delay lines. The purpose of signal design is to determine the optimum complex weighting for each pulse of the pulse train. Filter design yields the optimum complex weighting for the output taps of the delay line. Filter design for a specified signal is considered first. This is followed by combined signal and filter design and matched filter design. Constrained signal and filter design is investigated last. It should be emphasized that the optimizations require a knowledge of the clutter time-frequency distribution. For practical situations, when the clutter distribution is unknown, an adaptive filter is proposed that automatically provides the optimum filter weights for a given transmitted signal. When the clutter has a range-time extent less than the equivalent range-time extent of the signal, filter design alone yields nearly optimum performance. As the clutter becomes extended in range-time, it is necessary to consider jointly the design of signal and filter to obtain an optimum radar signal processor. In this report it is suggested that the signal be designed under the assumption of the clutter being extended over a broad range of Dopplers and that the signal processor consist of a bank of adaptive filters. Then each filter output yields the maximum ratio of peak signal to total interference power for this signal design.     

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

Gaussian particle filtering

Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter. It is based on the particle filtering concept, and it approximates the posterior distributions by single Gaussians, similar to Gaussian filters like the extended Kalman filter and its variants. It is shown that under the Gaussianity assumption, the Gaussian particle filter is asymptotically optimal in the number of particles and, hence, has much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Simulation results are presented to demonstrate the versatility and improved performance of the Gaussian particle filter over conventional Gaussian filters and the lower complexity than known particle filters.     

Statistical properties of local extrema in two-dimensional Gaussianrandom fields

This paper is concerned with the statistical properties of the local extrema and local maxima of two-dimensional (2D) Gaussian random fields (GRFs). A GRF may be represented by a linear filtering operation on a white noise field; the spatial properties of the GRF are then determined by the shape of the filter kernel function. New expressions are derived for the mean spatial density of local extrema and for the distribution of local extrema in a 2-D random field. The work is motivated by the problem of detecting known structures in images using 2D matched filters. The new results enable accurate performance predictions to be made of the reliability of such filters in the presence of noise. Case studies are presented for several well-known 2-D filter kernel functions     

A Parametric Approach to Suboptimal Signal Detection in α-Stable Noise

This paper deals with the detection of a known deterministic signal embedded in alpha-stable noise. The implementation of the optimal receiver requires the explicit expression of the probability density function (pdf) of the noise. Unfortunately, since there exists no closed-form for the pdf of alpha-stable distributed random variables, numerical integrations are required. To avoid such numerical approximations, we suggest a low-complexity parametric suboptimal detector well matched to essential properties of alpha-stable noises. This receiver does not require the explicit expression of the noise pdf. In addition, parameter optimization is fast for several optimization criteria and the selected receiver allows retrieval of the optimal Gaussian detector (matched filter) as well as the locally optimal detector in the Cauchy context. The performance of the detector is studied and a comparison with the optimal solution along with a variety of classical detectors is given. The robustness of the detector against the signal amplitude and the stability index alpha of the noise is discussed     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

一种基于分数低阶协方差矩阵的波束形成新方法

利用稳定分布对具有脉冲特性的噪声进行建模，提出了一种新的分数低阶协方差概念，推导了一种基于分数低阶协方差矩阵的波束形成方法，并分析了其旁瓣特性。模拟表明新方法具有更高的信号干扰噪声比及更强的波束形成与旁瓣抑制能力。新算法在高斯和分数低阶稳定分布环境下比传统的算法具有更好的韧性。