共查询到18条相似文献，搜索用时 296 毫秒
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文章采用SIRP方法实现了基于K分布的高分辨率雷达海杂波模型的仿真,K分布不仅能在很宽的条件范围内与海杂波的幅度分布很好的匹配,而且还可以正确的模拟海杂波回波脉冲间的相关特性.仿真结果证明了该方法的准确、可行. 相似文献
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一种基于局部K-分布的新的SAR图像舰船检测算法 总被引：1，自引：0，他引：1
提出了一种基于局部K-分布的新的SAR图像舰船检测算法.取目标窗口和背景窗口,通过把泄露到背景窗口中的舰船部分去除,对背景窗口中的剩余部分统计均值和方差,最终得到杂波分布概率模型进行恒虚警检测.相对于K-分布CFAR检测算法和基于局部窗口的K-分布CFAR检测算法,该算法能够适应杂波的局部变化, 对距离很近的舰船不会产生漏检.仿真结果表明了方法的有效性. 相似文献
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分析了在海杂波背景下检测目标的特点,并针对不同类型的雷这回波,给出了几种常用的海杂波幅度统计模型及参数估计方法,为雷达滤波嚣的设计提供了理论参考. 相似文献
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分布式星载雷达因其有较长的基线,可以实现对地面低速动目标的检测,然而分布式星载雷达通常存在距离和多普勒模糊,以及由栅瓣引入的角度模糊。文章研究了模糊对分布式星载雷达地杂波的影响。理论分析和仿真结果表明:距离模糊会引起杂波谱的展宽,多普勒模糊会增加杂波自由度,使杂波谱分散开,角度模糊会产生与阵列方向图相应的栅瓣杂波。 相似文献
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网络病毒的入侵和传播通常伴随着强杂波信息的掩盖而提高其生存能力,研究一种有效的免疫算法是对抗病毒传播的关键。传统的抵御网络病毒传播的免疫方法采用人工免疫算法,该法基于熵权重属性分类时不能有效抵抗强杂波干扰的影响,抗病毒感染的免疫能力不高。本文提出一种基于自适应实时繁殖策略的灰阶菌群免疫算法,采用分段匹配白化滤波方法滤除杂波信息,构建强杂波环境下病毒传播数学演化模型,以信息熵作为病毒传播免疫分类属性,提出采用灰阶菌群聚类方法进行病毒特征免疫。进行仿真实验表明该算法能有效滤除病毒潜藏的杂波干扰,病毒信息特征菌群得到准确聚类,降低了计算机用户的感染率,实时性和稳定性好。 相似文献
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文章针对单参数恒虚警(CFAR)检测器在weibull杂波模型下CFAR性能的不足,提出采用最优线性无偏估计(BLUE)算法设计局部检测器,并在全局最优条件对局部检测器的双门限参数以及判决融合规则进行优化.实验仿真表明,该方法能有效改善分布式CFAR检测系统的性能. 相似文献
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《Journal of The Franklin Institute》2022,359(9):4449-4467
A novel ground-moving target detection method is introduced using a distinguishing target, and clutter feature for airborne radar. The clutter proximity feature is extracted based on the Euclidean distance between a signal pixel and the expected clutter ridge in the angle-Doppler domain. Subsequently, target and clutter pixels are classified based on the extracted features for target detection without actually removing clutters or clutter estimation. The proposed technique is especially suitable for effective airborne radar target detection in the unknown ground clutter. The experimental results have validated the effectiveness of the new approach, which enables ground moving target detection in inhomogeneous clutter. 相似文献
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多通道合成孔径雷达（SAR）空时自适应处理是抑制地面杂波的有效方法,而空时二维滤波器的权值求取离不开杂波协方差矩阵的估计。从多通道SAR回波模型出发,提出了一种基于二维FFT的多通道SAR原始数据频域模拟方法,并根据该方法模拟出的多通道数据对空时二维协方差矩阵进行了估计。仿真结果显示,该方法在有效降低运算量的情况下保持了数据的真实性,良好地反映了多通道SAR的空时二维杂波特性。 相似文献
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《Journal of The Franklin Institute》1998,335(1):71-87
The performance of adaptive radar in range-heterogeneous clutter is a topic of practical interest, since radars are often trained over nonhomogeneous stretches of clutter. In this paper, the performance of eigenanalysis-based Doppler radar is investigated in a nonhomogeneous clutter environment, and is compared to the sample matrix inversion (SMI) and the generalized likelihood ratio (GLR) methods. Two clutter models are considered: edge transition and Weibull range-dependent. It is shown that in the asymptotic case (when the number of samples used to estimate the covariance matrix is large) the location of the clutter edge affects SMI and GLR, but not the eigencanceler. It is further shown that the effect of the nonhomogeneity in the clutter-edge model is to reduce the adaptive processor's training sample support, making the eigencanceler the preferred processor choice. In the Weibull range-dependent model, all processing methods perform poorly when the noise power variance is high. As the variance is reduced, the eigencanceler outperforms SMI and GLR for small training sets, similar to the homogeneous case. 相似文献
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Mohamed B. El Mashade 《Journal of The Franklin Institute》2005,342(5):521-550
The radar signals returning from the targets being illuminated are usually accompanied by thermal noise and clutter. Constant false alarm rate (CFAR) processors are useful for detecting these targets in a background for which the parameters of the statistical distribution are not known and may be nonstationary. The ordered-statistics (OS) CFAR technique has been proven to work satisfactorily in both multiple-target and nonuniform clutter cases. Unfortunately, the large processing time taken by this scheme limits its practical uses. The modified versions of the OS processor have been proposed to replace it in these applications. They can reduce the processing time of the single-window OS detector in half without changing its useful properties. Our goal in this paper is to provide a complete detection analysis for the OS processor along with ordered-statistic greatest-of (OSGO) and ordered-statistic smallest-of (OSSO) modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple-target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges worsens with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells. 相似文献
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分析了中高分辨率SAR海洋图像的目标和海杂波特点. 利用舰船目标的灰度相关性和形状特性与背景杂波的差异,提出了一种基于独立联合K-分布CFAR的舰船检测算法. 算法建立了海杂波的二维独立联合K-分布概率模型,通过给定的虚警率得到检测阈值以对图像进行检测. 该算法能够极大地抑制斑点噪声和背景局部不均匀对检测带来的影响,有效地降低了虚警数,检测效果得到了明显改善. 相似文献
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Two auxiliary model based recursive identification algorithms, a generalized extended stochastic gradient algorithm and a recursive generalized extended least squares algorithm, are developed for multivariable Box–Jenkins systems. The basic idea is to use the auxiliary models to estimate the unknown noise-free outputs of the system and to replace the unmeasurable terms in the information vectors with their estimates. We prove that the estimation errors given by the proposed algorithms converge to zero under the persistent excitation condition. Finally, an example is provided to show the effectiveness of the proposed algorithms. 相似文献
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《Information processing & management》2016,52(6):1161-1177
Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far.The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features.We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (Ωλ-MART), an algorithm that induces forests of oblivious regression trees.We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget. 相似文献
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文章回顾了中国气象卫星的发展历史、所提供的服务和应用。中国气象卫星是在全球气象卫星发展的浪潮中发展起来的。经过40多年的发展,极轨和静止两个系列的气象卫星已经业务运行。中国的气象卫星工程由卫星、运载火箭、发射、测控和地面应用5大系统组成。地面系统作为气象卫星工程大系统中的重要组成部分,对卫星应用效益起重要的作用,在卫星发射后对卫星有效荷载进行运行管理,实施从卫星下传数据的接收、处理、分发、应用和服务。中国气象卫星投入业务运行以后,为改善天气预报、环境监测和短期气候预测提供了有用的信息。在天气预报领域,为台风、暴雨、冰雹 、暴雪、沙尘暴、龙卷风等灾害性天气的预报提供了更有力的手段;在短期气候预测领域,提供了更多有用的参数,如海表水温、雪盖、植被指数等;在环境监测领域,在洪涝、森林草原火情、沙尘暴、雪灾和海冰等监测中发挥了重要作用。现阶段,中国气象卫星面临的最大机遇和挑战在于提高产品的精度,为定量应用服务。其内容涵盖气象卫星的空间段和地面段,并且与数值天气预报本身也有密切的关系。气象卫星为数值天气预报提供全球资料,数值预报模式为气象卫星提供定量产品推导的背景场。气象卫星与数值天气预报协同工作,才能促进气象卫星应用水平的提高。 相似文献