高频雷达海杂波混沌特性的实验研究

利用高频地波雷达实测数据 ,从实验角度应用动力学方法对高频海杂波的动力特性作出分析 ,首次得到了高频海杂波呈现混沌特性的结论。这一结论对高频雷达目标探测和海态遥感以及海洋气象的研究都具有重要意义。     

多目标环境下跑道异物监测雷达的CFAR检测技术

强地杂波下的弱固定目标检测是机场跑道异物监测雷达需解决的关键问题之一,传统的恒虚警检测算法在多目标环境下会出现“自屏蔽”效应,不能有效地检测到目标。本文提出了一种剔除平均杂波图恒虚警检测算法,在分析系统特性和跑道环境的基础上,结合调频连续波雷达体制建立了多目标回波信号模型。在剔除若干个样本值后,由余下的样本值估计背景杂波强度,对多次连续扫描测量值作递归滤波处理,获得了平稳的检测门限,有效地检测到了目标。最后,进一步讨论了相关参数对检测性能的影响。     

利用数字电视信号的外辐射源雷达性能分析

本文对数字电视信号的特性、对消性能进行了分析,并通过实际采集的数据分析其在杂波背景下目标检测性能,且与调频广播信号进行了对比,最后指出数字电视信号能够作为无源雷达检测目标的种优秀的外辐射源。     

雷达信号处理MTI/MID性能分析与功能测试

动目标显示和动目标检测是现代雷达重要的频域信号处理措施.介绍了雷达信号处理中动目标显示和动目标检测的基本原理,对其性能进行了分析,给出了其性能评价指标,提出了对其性能进行检测的方法.     

自然纹理背景的目标提取

针对自然纹理背景,提出一种基于多尺度小波特征融合的人造目标检测方法。在小波变换域,纹理背景和目标区域的多级小波系数具有不同的能量分布,能量特征可作为简单、有效的空间特征来检测目标。由于小波函数具有良好的局域性特点,不同尺度下用它检测出的边缘特征点移位不会超过1个像素。融合边缘特征和能量特征进行人造目标检测,可有效地保证目标边界的定位精度,达到较好的鲁棒性和准确性。实验结果证明,该方法对纹理背景下人造目标面积探测的误差率小于5％,目标探测概率大于94．1％。     

汽车防撞雷达多目标识别测量算法研究

针对目前汽车防撞系统中常用的调频连续波测距雷达对多目标识别的不足,基于频移键控雷达的联合作用,提出一种新型的测量方法,该算法采用一种类梯形调频波形,利用调制频率的周期性变化特征分别对单目标和多目标实现有效检测,使雷达不仅可测量单目标在不同运动状态下的距离和速度,同时也可识别多目标,并对其完成测距与测速。利用Matlab对所提出的算法进行了仿真与验证,表明该算法不仅能准确捕捉所探测目标,并且在测量中有着较高的精度,适用于毫米波汽车防撞雷达在行驶中的目标测量。     

一种新的声呐图像处理算法

针对高频声呐图像的目标检测问题,在分析声呐灰度图像像素点的灰度分布模型的基础上,提出了一种新的快速图像处理算法。该算法利用图像中各像素点处滑动窗内像素点的灰度分布模型的参数以及灰度分布模型和归一化灰度直方图的拟合误差为特征量,构造声呐图像的特征图,并采用自适应阈值算法进行图像处理。仿真结果表明该算法具有实时性好、准确度高的特点,并且可有效地克服背景干扰的影响,有利于人造目标的准确提取。     

基于数字电视信号的无源雷达性能仿真分析

对数字电视信号的特性、对消性能进行分析,并通过实际采集的数据分析其在杂波背景下目标检测性能,且与调频广播信号进行对比,最后指出数字电视信号能够作为无源雷达检测目标的一种优秀的外辐射源。     

雷达信号处理MTI／MTD性能分析与功能测试

动目标显示和动目标检测是现代雷达重要的频域信号处理措施。介绍了雷达信号处理中动目标显示和动目标检测的基本原理。对其性能进行了分析。给出了其性能评价指标。提出了对其性能进行检测的方法。     

复杂动态背景下的红外运动多目标跟踪与检测

提出了一种采用小波变换对复杂动态背景变化程度的检测方法.对红外序列图像进行小波变换预处理后,可以提取出若干个特征区域,通过检测这些特征区域的差异来判断背景的变化程度,实现背景抑制.在抑制动态背景的基础之上,运用基于动态先验知识的区域主动轮廓模型的水平集方法来实现红外多目标的数据关联和跟踪,动态先验知识包括形状描述因子、灰度特征和运动特征等.同时,在多目标跟踪中存在目标"合并和分裂"的现象,运用"记忆和填充"方法来实现对多目标的稳定跟踪.通过对实际复杂动态背景条件下的红外序列图像进行多目标跟踪和检测实验,验证了所提方法的可行性和有效性.     

Performance of an optimum receiver designed for pattern recognition with nonoverlapping target and scene noise

The design of an optimum receiver for pattern recognition is based on multiple-alternative hypothesis testing with unknown parameters for detecting and locating a noisy target or a noise-free target in scene noise that is spatially nonoverlapping with this target. The optimum receiver designed for a noise-free target has the interesting property of detecting, without error, a noise-free target that has unknown illumination by using operations that are independent of the scene-noise statistics. We investigate the performance of the optimum receiver designed for nonoverlapping target and scene noise in terms of rotation and scale sensitivity of the input targets and discrimination against similar objects. Because it is not possible in practical systems to have a completely noise-free target, we examine how the performance of the optimum receiver designed for a noise-free target is affected when there is some overlapping noise on the target. The application of the optimum receiver to binary character recognition is described. Computer simulation results are provided.     

Optimum nonlinear composite filter for distortion-tolerant pattern recognition

We describe a nonlinear distortion-tolerant filter for pattern recognition that is optimum in terms of tolerance to input noise and discrimination capability. This filter was derived by minimization of the output energy that is due to the overlapping additive noise and the input scene, and the output of the filter meets the design constraints obtained from the training data set. The performance of this filter was tested with an input scene containing one of the training data sets, a nontraining true target, and a false object in the presence of overlapping additive noise and nonoverlapping background noise. We carried out Monte Carlo runs to measure the statistical performance of the filter and obtained receiver operating characteristics curves to show the detection capabilities of the filter.     

Minimum-mean-square-error filters for detecting a noisy target in background noise

A minimum-mean-square-error filter is proposed to detect a noisy target in spatially nonoverlapping background noise. In this model, both the background noise that is spatially nonoverlapping with the target and the noise that is additive to the target and the input image are considered. The criterion used to design the filter is to minimize the mean-square-error between the filter output and a delta function located at the target position in the presence of the noise. Computer-simulation results for a number of noisy input images are presented, and the performance of the filter is determined. We also test the filter discrimination against undesired objects and tolerance to target distortions, such as rotation and scaling.     

Performance of minimum-mean-square-error filters for spatially nonoverlapping target and input-scene noise

Using computer simulations, we investigate the performance of a minimum-mean-square-error filter for input-scene noise that is spatially nonoverlapping (disjoint) with a target for a limited set of images. Different input-scene-noise statistics are used to test the filter performance. We show that in the presence of spatially nonoverlapping target and input-scene noise, the output of the minimummean- square-error filter has a well-defined correlation peak, small sidelobes, and a high peak-to-correlationenergy ratio compared with other widely used filters such as the classical matched filter, the phase-only filter, and the inverse filter. We also test the robustness of the minimum-mean-square-error filter to errors in noise statistics used in the filter design. We show that, for the images tested here, the performance of the minimum-mean-square-error filter is not sensitive to errors in noise statistics and the filter can detect the target even if a considerable error exists. The discrimination capability and the illumination sensitivity of the minimum-mean-square-error filter are also tested.     

Nonlinear equalization for holographic data storage systems

Despite the fact that the channel in a holographic data-storage system is nonlinear, most of the existing approaches use linear equalization for data recovery. We present a novel and simple to implement nonlinear equalization approach based on a minimum mean-square-error criterion. We use a quadratic equalizer whose complexity is comparable to that of a linear equalizer. We also explore the effectiveness of a nonlinear equalization target as compared with the conventional linear target. Bit-error-rate (BER) performance is studied for channels having electronics noise, optical noise, and a different span of intersymbol interference. With a linear target, whereas the linear equalizer exhibits an error floor in the BER performance, the quadratic equalizer significantly improves the performance with no sign of error floor even up to 10(-7). With a nonlinear target, whereas the quadratic equalizer provides an additional performance gain of 1-2 dB, the error-floor problem of the linear equalizer has been considerably alleviated, thereby significantly improving the latter's performance. A theoretical performance analysis of the nonlinear receiver with non-Gaussian noise is also presented. A simplified approach is developed to compute the underlying probability density functions, optimum detector threshold, and BER using the theoretical analysis. Numerical results show that the theoretical predictions agree well with simulations.     

Pattern recognition using binary masks based on the fractional Fourier transform

This paper presents two new methodologies for pattern recognition invariant to position and rotation. The two methods use the correlation of signatures obtained using a binary rings mask created from the fractional Fourier transform (FRFT). An analysis of fragmented information was performed with 21 images of fossil diatom species. The methodologies were applied to 30 species of phytoplankton to test the performance when rotation distortion is present in a target. Also, a noise analysis was performed. Higher signature correlation values are achieved by working outside the Fourier plane by changing the order of the FRFT.     

改进的动态图模型点模式匹配

为了提高概率图模型点模式匹配的精度,本文提出了改进的动态图模型点模式匹配算法。首先,在动态图模型点模式匹配的相似性度量中应用混合高斯分布,以提高模型利用多特征的能力,使匹配方法对噪声更加稳健。其次,在目标点集中引入了虚拟的哑点并给出了包含哑点的相似性度量。当模板中的点和哑点相匹配的相似性度量更大时模板点将和哑点匹配,以减少由异常点所导致的误配。实验结果表明所提出的匹配方法对噪声和异常点更加稳健,匹配的精度也优于传统方法。     

Distortion tolerant image recognition receiver by use of a multiple-hypothesis method

A multiple-hypothesis method is used to detect a target or a reference signal in the presence of additive noise with unknown statistics. The receiver is designed to detect the target and to be tolerant of the variations in rotation and illumination of the target. A multiple-hypothesis test with unknown-noise parameters is used to locate the target position. The proposed method does not use any specific distortion-invariant-filtering technique, but it relies on a multiple-hypothesis approach. Maximum-likelihood estimates of the illumination constant and the unknown noise parameters are obtained. Computer simulations are presented to evaluate the performance of the receiver for various distorted noisy true-class targets with varying illumination and false-class objects.     

Camera focusing based on fringe pattern matching

A method of camera focusing based on fringe pattern matching is presented. In the method, the variance of the mismatch function is used as a focus measure. The focus measure was examined in a fringe analysis system, and the results from the three well-known focus measures-gray-level variance, image gradient, and Laplacian-were also obtained in the experiment as a comparison. The experimental results show that the focus measure based on fringe pattern matching performs well in peak sharpness, monotonicity, and noise sensitivity and that it has a better performance in noise sensitivity owing to its advantage of averaging noise.     

Modified fringe-adjusted joint transform correlation to accommodate noise in the input scene

A modified fringe-adjusted joint transform correlator is proposed that is able to accommodate noise in the input scene. The effect of noise in the input scene on the performance of the joint transform correlator is analyzed and quantified. When the target is embeddedin aseverely noise-corrupted input scene, it is shown that the proposed modified fringe-adjusted filter joint transform correlator delivers a better correlation performance and the capacity to accommodate this noise than does the fringe-adjusted filter-based correlator. When the power spectra of the input image and the reference image are subtracted from the power spectrum of the joint-input image, it is found that the noise effect on the output plane is independent of the objects in the input scene and originates from the convolution of the reference image and noise in the input scene.