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通过压缩感知(Compressed Sensing, CS)算法可以实现对目标的稀疏成像,并获取其空间散射结构用于目标鉴别和识别。该文针对穿透地表成像的前视超宽带虚拟孔径雷达(Forward Looking Virtual Aperture Radar, FLVAR)实测数据,以CS理论为基础对地雷目标进行稀疏成像,利用地雷目标电磁散射的稀疏性实现其散射结构的提取,将目标散射特性转化为与物理结构相关的几何特征,并基于该特征进行目标的分类鉴别。新方法不仅拓展了地雷鉴别的新思路,而且也为压缩感知在目标散射结构提取和目标鉴别上的应用进行了初步有效的尝试。 相似文献
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基于压缩感知(CS)的合成孔径雷达成像方法可以显著减少数据采样时间、数据量以及节省信号带宽。然而,基于CS的方法对噪声和杂波相当敏感,在信噪比较低的时候,成像质量较差。该文结合CS理论提出了合成孔径雷达中的随机孔径贝叶斯压缩感知(BCS)高分辨2维成像方法。在距离向应用CS减少采样数据的同时,在方位向随机抽取部分孔径位置发射和接收信号,以少量的测量孔径和测量数据获得重建目标空间的足够信息。基于贝叶斯的分析方法由于考虑了成像场景中的杂波以及压缩采样过程中的加性噪声,因而能够更好地重建目标空间。仿真结果表明,基于贝叶斯方法得到的图像比基于FFT方法得到的图像更加尖锐,比基于CS方法得到的图像更加稀疏,因而具有更高的分辨率。 相似文献
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目前,超宽带(UWB)技术是受到高度重视的一种短距离高速率的无线通信技术,在军事、雷达定位、灾害救援、测距及通信等领域均所应用。然而,由于频谱资源更加珍贵的条件下,使得超宽带无线通信技术的地位越来越重要。脉冲超宽带是其最为经典的实现方式,将压缩感知技术应用到脉冲超宽带中,可有效降低接收机的采样率,具有重要实践意义。 相似文献
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针对压缩感知雷达(Compressive Sensing Radar, CSR)面临测量噪声、信道干扰及系统精度误差等扰动时,非自适应随机测量值和感知矩阵失配导致传统CSR目标参数提取性能下降的问题,该文提出一种基于贝叶斯压缩感知(Bayesian Compressive Sensing, BCS)的噪声MIMO雷达稳健目标参数提取方法。文中首先建立了噪声MIMO雷达的稀疏感知模型,推导了基于目标参数稀疏贝叶斯模型的联合概率密度函数,随后将BCS方法与LASSO (Least-Absolute Shrinkage and Selection Operator)算法相结合对联合概率密度函数进行优化求解。与传统CSR算法相比,该方法能够在CSR系统模型存在失配误差时对目标参数进行有效估计,降低了目标参数估计误差,改善了CSR目标参数提取的准确性和鲁棒性。计算机仿真验证了该方法的有效性。 相似文献
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超宽带信道建模中基于压缩感知的解卷积算法 总被引:1,自引:0,他引:1
针对频域测量方式下的超宽带(UWB)信道测量数据后处理,该文提出了用具有高斯滚降特性过渡带的类高斯窗,提取符合中国UWB频谱规范的信道测量数据,并将类高斯窗对应的时域脉冲作为先验信息,使用基于压缩感知(CS)的算法对时域信道测量信号解卷积,使得解卷积后的信道冲激响应具有高分辨率特性。利用频域加窗补零,以及改变解卷积算法中参数化波形字典原子的步长,可以得到不同分辨率的解卷积结果。采用匹配追踪(Matching Pursuit,MP)算法作为CS的重构算法。针对一间办公室的视距(LOS)与非视距(NLOS)信道测量数据处理结果表明,基于压缩感知的解卷积算法可以用较少的观测值获得和CLEAN算法相近的解卷积性能。 相似文献
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基于方位不变特征的地雷检测方法 总被引:1,自引:0,他引:1
车载前视超宽带地表穿透雷达在地雷探测中遇到的困难是虚警率过高,地雷与杂波在全孔径图像中很难准确区分。为降低地雷探测过程中的虚警率,该文提出一种基于目标子孔径图像方位不变性的检测方法。该方法利用分裂发射虚拟孔径成像模型,将全孔径图像分解为左右两个子孔径图像,并根据子孔径图像中的目标一维距离剖线建立双峰特征模型。在此模型基础上提取具有方位不变性的若干特征,进而得到左右子孔径图像中目标的一致性度量,并将该一致性度量作为最终的特征向量送入鉴别器加以判别。经实测数据验证,该算法能有效剔除原先在全孔径图像中无法剔除的杂波,从而降低前视地表穿透合成孔径雷达中地雷探测的虚警率。 相似文献
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压缩感知在超宽带雷达成像中的应用 总被引:1,自引:0,他引:1
利用信号的先验稀疏性,通过压缩感知(Compressive Sensing,CS)方法可以实现从少量的非适应性随机测量数据重建原始信号。将压缩感知理论应用到超宽带雷达高分辨率成像中,提出基于CS理论的二维方位-距离向成像算法,可以显著减少数据采集时间、数据量、处理时间以及节省信号带宽,并利用矢量网络分析仪(Vector Network Analyzer,VNA)测量的实验数据验证了采用时间和空间减采样数据的CS算法可以实现与传统的延迟-求和波束形成方法(Delay-Sum Beamform-ing,DSBF)相当的成像质量和分辨率。 相似文献
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Compressed sensing (CS) has been widely concerned and sparsity of a signal plays a crucial role in CS to exactly recover signals. Contourlet transform provides sparse representations for images, so an algorithm of CS reconstruction based on contourlet is considered. Meanwhile, taking into account the computation and the storage of large random measurement matrices in the CS framework, we are trying to introduce the wavelet transform into the contourlet domain to reduce the size of random measurement matrices. Several numerical experiments demonstrate that this idea is feasible. The proposed algorithm possesses the following advantages: reduced size of random measurement matrix and improved recovered performance. 相似文献
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Network coding (NC) provides an elegant solution for improving capacity and robustness in computer networks. Different to traditional “store-and-forward” transmission paradigm, each intermediate node linearly combines received data packets, and the original files can be decoded at the sink nodes in NC settings. This brand-new paradigm is vulnerable to pollution attack, which means that some malicious nodes inject fake data packets into the network and this will lead to incorrect decoding. There are some information-theoretical solutions and cryptographic solutions for solving this security issue, and most existing schemes can thwart data pollution attacks. However, the privacy of the original files are vital to some application environments (e.g. military network). To the best of our knowledge, there is not a secure scheme which can thwart pollution attack and can protect the privacy of transmitted data simultaneously. In this paper, we present an efficient privacy-preserving scheme for secure network coding based on compressed sensing (CS), which has attracted considerable research interest in the signal processing community. Specifically, we embed CS into the general NC framework, i.e., the source node needs to compress each original data packet using the sensing matrix before creating the augmented vector and the sink nodes require to perform an additional CS reconstruction algorithm for reconstructing the original file. In addition, we construct a simple key distribution protocol and each intermediate node just needs two secret keys for verifying the integrity of received data packets. Such novel hybrid construction enables the privacy-preserving guarantee, and the performance comparison shows the high-efficiency of our scheme in terms of the computational complexity and communication overhead. 相似文献
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An image encryption scheme based on a four-wing hyperchaotic system combined with compressed sensing and DNA coding is proposed. The scheme uses compressed sensing (CS) to reduce the image according to a certain scale in the encryption process. The measurement matrix is constructed by combining the Kronecker product (KP) and chaotic system. KP is used to extend the low-dimensional seed matrix to the high-dimensional measurement matrix. The dimensional seed matrix is generated by a four-wing chaotic system. At the same time, the chaotic sequence generated by the chaotic system dynamically controls the DNA coding and then performs the XOR operation. Simulation experiments and performance analysis show that the algorithm has good performance and security. 相似文献
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利用机载或车载超宽带合成孔径雷达(UWB SAR)可以实现大区域地雷和雷场远距离快速探测,是探雷的发展趋势.为了降低地雷检测中的虚警,需要提取有效的地雷特征区分地雷和杂波.本文提出了一种基于高维时频分布(HDTFD)的地雷散射方位不变特征提取方法,具体实现可采用高维Wigner-Ville分布(HDWVD)和高维Choi-Williams分布(HDCWD)等.基于HDTFD特征提取方法能够在保持高空间分辨的同时,有效提取散射函数中关于频率和方位角信息.利用Rail-GPSAR系统实测数据比较了基于HDWVD和基于HDCWD特征提取方法在分辨率、交叉项抑制和方位不变特征提取等方面的性能.实测数据处理结果说明基于HDCWD特征提取方法更适合实际数据处理. 相似文献
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Haixiao Liu Bin Song Hao Qin Zhiliang Qiu 《Journal of Visual Communication and Image Representation》2013,24(8):1232-1242
Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel “undersampled” correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1–3.5 dB improvements in the average PSNR, and a 2–9 dB gain for non-key frames when key frames are subsampled at an increased rate. 相似文献
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针对位置指纹定位算法在训练阶段信号数据采集量大和定位精度不高的问题,提出一种压缩感知(CS,Compressed Sensing)与K均值改进支持向量机(SVM,Support Vector Machine)相结合的定位算法模型(CS-KSVM)。CS算法在训练阶段利用已采集到的部分参考点wifi信号强度数据对整个指纹信号库进行重构以降低信号采集工作量,再用K均值改进SVM算法来实现测试点的准确分类。实验仿真结果表明,CS-KSVM算法在相同采样点条件下的定位精度明显要高于传统定位算法,同时在相同定位精度条件下大大减少了定位需要的采样点数。CS-KSVM算法在3米之内的定位准确度可以达到93.2%。 相似文献
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Wenbo Xu Jiaru LinKai Niu Zhiqiang He 《AEUE-International Journal of Electronics and Communications》2012,66(4):294-296
Compressed sensing is a new framework to capture sparse signals at sub-Nyquist rate. To guarantee reliable recovery from compressed measurements, the exact reconstruction of the support is necessary. In this letter, we study the probability of exact support reconstruction for maximum likelihood decoding algorithm. The recovery problem is first casted as the one of finding K one-dimensional subspaces containing the maximum energy of the received signal, where K is the sparsity level. Then, the asymptotic probability is developed based on the derived probability density function of the normalized distances between the received signal and all possible subspaces. 相似文献
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The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the joint correlations of video signals in space and time, and their reconstruction quality and speed need further improvement. To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves a peak signal-to-noise ratio gain of 1–3 dB and a multi-scale structural similarity gain of 0.01–0.03 at a low time complexity, and the reconstructed frames not only have good edge contours but also retain textural details. 相似文献