基于压缩感知的LFM雷达信号回波盲稀疏度估计

由于压缩感知理论用于LFM雷达中要预先给出信号稀疏度,提出了自适应正交匹配追踪算法(AOMP),该方法可用于处理LFM雷达回波信号。在稀疏度即目标数目未知时,由不同发射信号通过延时进而相加来构造冗余字典。AOMP算法是依据残差之差的相对能量小于设定的停止门限来自适应终止稀疏分解过程。理论分析和仿真结果表明,存在噪声时,AOMP算法优于OMP算法,明显提高重构算法的重建概率。当回波信号的距离分辨率匹配字典的距离分辨率,冗余字典结合AOMP算法可有效处理LFM雷达回波信号,具有广泛的应用价值。     

盲源分离算法应用于抗雷达多假目标干扰

多假目标干扰是与目标信号强相关的欺骗干扰信号,其对准雷达天线主瓣方向,会严重影响雷达的性能,通常的旁瓣抗干扰技术和一些主瓣抗压制干扰技术难以奏效.针对目标和干扰信号的相对独立性以及在空域上的差异性,提出利用盲源分离(Blind Source Separation,BSS)算法抗脉冲压缩雷达多假目标干扰的方法.首先给出了假目标干扰原理和主瓣干扰信号模型;其次利用经典的BSS算法分离接收到的主瓣干扰混合信号,并脉压找出目标信号达到抗干扰的目的;最后分析了目标与多假目标干扰混合信号的可分离性.仿真实验表明,该方法对多假目标干扰有良好的抗干扰性能.     

LFM脉冲压缩雷达的随机移频多假目标干扰技术研究

针对LFM脉冲压缩雷达回波信号距离和多普勒频率存在耦合这一特点, 在阶梯波移频干扰的基础上, 提出了随机移频的干扰方法。在雷达回波信号中心频率正负二分之一带宽内附加随机的多普勒移频分量, 可产生分布在真实目标周围的随机假目标。随机移频干扰技术产生的假目标随机性强, 使得雷达无法通过假目标频率补偿得出真实目标的位置, 并且随机移频产生的假目标相对于雷达是部分适配的, 因此假目标在一定程度上会展宽, 当附加随机移频点接近于雷达中心频率时, 产生的假目标甚至可以覆盖真实目标, 从而达到较好的干扰效果。     

基于一维距离像稀疏表达的无源假目标识别

为了解决敌方释放箔条干扰我方防空反击任务或者敌方舰船设置角反射器阵列干扰我方对海目标打击任务中的雷达抗干扰问题,提出了一种基于雷达一维距离像的稀疏表达的无源假目标识别的方法。首先,分别利用大量关注目标和无源欺骗干扰的雷达一维距离像数据进行稀疏字典学习,分别得到目标和干扰的稀疏字典;然后利用两种稀疏字典分别对未知的雷达一维距离像信号进行稀疏表达;最后分别计算两种稀疏字典对未知信号稀疏表达的重构误差,利用重构误差比值识别目标和干扰类别。仿真结果表明,在目标与无源假目标干扰的回波不混叠、目标与干扰噪声比3 dB条件下,识别无源假目标欺骗干扰的准确率超过90%,证明了该方法抗无源假目标干扰的有效性。     

基于压缩感知的雷达前视向稀疏目标分辨

主动雷达使用多普勒波束锐化(DBS)和合成孔径雷达技术只能提高在斜视和侧视向的横向距离分辨,而对前视向波束内相同距离但不同方位-俯仰角度上的多个目标难以分辨。该文提出一种基于压缩感知理论(CS)的单接收通道结构雷达前视向稀疏目标分辨方法。多个天线子阵接收信号经随机加权求和后通过单个接收通道输出,同一距离单元上不同脉冲重复周期的接收机输出建模为对同一稀疏信号场景的多次观测,根据观测进行压缩感知信号重构得到稀疏目标场景估计。仿真表明,该方法能够实现雷达对前视向波束内的稀疏目标分辨。     

冗余字典在压缩感知中的应用研究

信号的稀疏表示是压缩感知理论中的关键问题,一般选择正交基作为压缩感知中的稀疏变换基。因为冗余字典能更有效的表示信号的特征,使得信号能用字典中的少量原子线性表示,因此本文对冗余字典在压缩感知理论中的应用进行了研究。设计了由不同正交基与单位矩阵组成的3种简洁冗余字典,作为压缩感知的稀疏变换基。以一维信号作为测试信号,研究了冗余字典的稀疏表示和算法重构的性能。实验结果证明了冗余字典在压缩感知理论中应用的有效性。     

基于迭代自适应稀疏分解的雷达信号去噪

稀疏分解能有效分离信号和噪声,因此适用于信号去噪.文中构造了雷达回波稀疏表示的冗余字典,字典原子与目标回波波形匹配,基于该字典的雷达回波信号稀疏度就是目标数.针对稀疏度自适应匹配追踪算法进行低信噪比信号稀疏分解时的不足,提出了一种迭代自适应匹配追踪算法,采用规范化的残差之差作为迭代终止条件,使得稀疏分解过程能依据噪声水平自适应终止,以逐次逼近方式估计信号稀疏度,改善了稀疏分解的精度.仿真实验结果表明,该算法在低信噪比以及稀疏度未知的条件下,实现了雷达回波信号的准确稀疏分解,极大地提高了信噪比.     

强地杂波下基于压缩感知的稀疏子脉冲高分辨雷达成像方法

针对稀疏雷达孔径数据处理与成像问题,本文提出了一种强地杂波背景下基于压缩感知（CS）的线性调频步进信号（SFCS）稀疏子脉冲高分辨雷达成像方法。在对稀疏回波数据解线调时,采用填零一次相消技术剔除地杂波,对粗分辨距离像序列二次采样后获得高信杂比的目标高分辨回波信号;再利用该信号的频域稀疏特性,结合各脉冲簇中随机丢失不同子脉冲的情况,构造相应的部分傅里叶基矩阵实现雷达数据的稀疏化表征,然后利用正交匹配追踪（OMP）算法对目标高分辨距离像（HRRP）进行重构处理,实现对目标的高分辨成像。仿真结果验证了本文方法的有效性。      

一种ISAR二维压缩感知成像的运动补偿方法

 通过发射一组具有随机脉冲重复间隔(PRI)的线性调频信号,并对经过去斜率(Deramp)处理的回波信号随机下采样,可以得到目标的二维随机观测回波数据.本文通过分析该类回波信号的模型和信号的稀疏性,针对ISAR(Inverse Synthetic Aperture Radar)二维压缩感知成像技术提出了一种运动补偿算法.实测数据处理结果表明,本文提出的算法可以有效地针对ISAR二维随机观测回波数据实现运动补偿.     

基于随机调频步进信号的高分辨ISAR成像方法

为充分利用随机调频步进逆合成孔径雷达回波所具有的联合稀疏特征,提高成像性能,该文提出一种基于分布式压缩感知理论的随机调频步进逆合成孔径雷达高分辨成像方法。首先构建随机调频步进信号回波的联合稀疏表示模型,并完成子脉冲的脉冲压缩处理;其次,基于每组子脉冲的随机方式(组与组之间的随机方式不同),构建相应的随机量测矩阵,获取回波的压缩感知信号模型,并利用分布式压缩感知理论实现距离向联合高分辨重构;最后结合回波在方位向的稀疏性,采用快速稀疏重构算法实现方位向高分辨成像。理论分析和仿真结果表明由于充分利用了随机调频步进信号回波的随机性与联合稀疏特征,所提出方法具有重构精度高、距离向采样率低、抗噪性能强等特点。     

Compressed Sensing and Redundant Dictionaries

This paper extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for compressed sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.     

Joint FrFT‐FFT basis compressed sensing and adaptive iterative optimization for countering suppressive jamming

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.     

SOJ与虚假目标复合干扰下目标跟踪技术

在远距离支援干扰下,由于目标检测概率降低,雷达容易出现检测不到目标真实回波的现象,如果此时再施放虚假目标干扰,由于虚假目标回波比目标回波幅度强,雷达更容易误将虚假目标干扰回波当成真实目标的回波。针对上述特点,提出了采用卡方检验,SNR检验鉴别虚假目标,采用集中式雷达网数据处理方法对目标进行跟踪。仿真实验表明,此种方法降低了虚假目标的误鉴别概率,同时也可以较好地实现了干扰下目标的跟踪,具有一定的实际工程意义。     

Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization

Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.     

基于数学形态学的密集转发干扰抑制算法

基于数字射频存储器(Digital Radio Frequency Memory,DRFM)的干扰机在一个脉冲重复周期内通过重复转发截获的雷达发射信号形成密集假目标干扰,严重影响雷达对真实目标的检测和跟踪.针对这一问题,提出了一种基于数学形态学的密集假目标干扰抑制算法.该方法先用Otsu算法对脉压后的回波二值化处理,利...     

基于压缩感知的超宽带信道估计方法的研究

压缩感知(Compressed Sensing, CS)理论可以从较少的观测样本中恢复稀疏信号。针对超宽带(Ultra- WideBand, UWB)信道的稀疏特性,将压缩感知理论应用于UWB系统的信道估计中,能够有效地降低系统的采样速率。该文针对UWB信道的特点对过完备字典库和观测矩阵进行设计,提出了一种滤波矩阵估计算法。然后,分别利用丹茨格选择器(Dantzig Selector, DS),基追踪降噪(Basis Pursuit De-Noising, BPDN)算法和正交匹配跟踪(Orthogonal Matching Pursuit, OMP)算法实现信号检测,进一步给出UWB信道估计中CS重建算法的选择建议。基于IEEE 802.15.4a信道模型的仿真结果表明,该算法同随机观测算法的检测结果相比,能够在较低的采样速率下获得更好的误码率性能。     

微机程控通用雷达目标模拟器

雷达目标模拟器是一种用来产生模拟的目标回波信号的装置,在雷达设备的研究、维护和使用等领域广泛的用途。本文介绍一种通用雷达目标模拟器,这种目标模拟器由微机进行控制,可以用程序设定信号的特性,从而可以产生具有复杂特性的目标回波信号,例如具有多个运动目标、有一定的杂波背景等等。而且,这种目标模拟器可以与不同型号雷达的方位和定时脉冲实现同步, 生相应的雷达回波信号,因而具有广泛的用途。     

Nonparametric Bayesian dictionary learning algorithm based on structural similarity

Though nonparametric Bayesian methods possesses significant superiority with respect to traditional comprehensive dictionary learning methods,there is room for improvement of this method as it needs more consideration over the structural similarity and variability of images.To solve this problem,a nonparametric Bayesian dictionary learning algorithm based on structural similarity was proposed.The algorithm improved the structural representing ability of dictionaries by clustering images according to their non-local structural similarity and introducing block structure into sparse representing of images.Denoising and compressed sensing experiments showed that the proposed algorithm performs better than several current popular unsupervised dictionary learning algorithms.