一种进动锥体目标的EM参数估计方法

为分离进动锥体散射点回波、反演锥体的尺寸和进动角等参数,提出了一种EM算法分离散射点微多普勒并进行微动参数估计的方法.该方法由进动锥体回波的类正弦调频模型,经过时频平面检测后,采用EM算法迭代地估计多分量正弦信号的参数和分离散射点的时频观测值,在此基础上再进行锥底散射点多普勒半周期叠加、估计锥体目标尺寸和微动参数.仿真结果表明,在弱散射点信噪比大于2 dB时,可估计出目标的尺寸和进动角等参数,从而验证了该方法的有效性.     

一种最大密度检测欠定混合矩阵估计算法

针对源数未知条件下欠定盲源分离混合矩阵估计问题,提出了最大密度检测混合矩阵估计算法。在观测信号稀疏表示的基础上,首先对观测信号进行预处理;然后寻找观测信号的最大密度点;接着在此基础上确定有效样本点集合,再聚类得到辐射源数和混合矩阵。为验证算法的有效性,在时频单源点检测法和小波变换法下开展了仿真实验。结果表明,所提出算法的源数和混合矩阵估计效果优于参考算法,计算复杂度远低于参考算法。进一步实验表明,所提出算法对于正定、超定和欠定盲源分离混合矩阵的估计都具有较好的适用性。     

基于时频子空间方法的线性调频信号参量估计

针对加性色噪声背景下的多分量线性调频信号参量估计问题,提出了基于相位差分的时频子空间方法.该方法将时频子空间的方法引入线性调频信号参量估计中.首先时多分量线性调频信号做相位差分,然后在时域内利用子空问方法对差分后的信号进行滤波,最后用基于矩阵束的方法进行频率估计.本方法消除了使用相位差分而带来的交叉项的干扰,同时能够很好的消除加性色噪声对多分量线性调频信号的参量估计的影响.仿真实验证明了本方法的有效性.     

非半周期采样信号相位差估计的相位校正相关法

针对非半周期采样信号的相位差估计问题,提出一种非半周期采样信号相位差估计的相位校正相关法。首先,利用频率估计的相位匹配方法对采样信号进行频率估计,得到采样信号的频率估计值;其次,对采样信号进行互相关,得到互相关信号,并通过采样信号的信号时移和互相关构造一路新的互相关信号,使两路互相关信号具有相同的误差项;然后,利用两路互相关信号得到相位校正互相关信号;最后,利用相位校正互相关信号和频率估计值获得采样信号的相位差估计值。理论分析表明,该方法消除了采样信号非半周期采样对相位差估计的影响,无需预知信号幅值即可实现采样信号相位差的无偏估计。为验证所提方法的有效性和优越性,在不同相位差、信噪比和信号长度条件下对本文方法进行了仿真实验,并利用科氏流量计进行了应用实验。仿真实验结果表明,本文方法有效提高了非半周期采样信号的相位差估计精度,其相位差估计精度优于互相关方法、数据延拓式相关方法和正交时延估计方法的相位差估计精度,更接近克拉美罗下限。科氏流量计流量测量实验验证了本文方法在实际应用中的有效性。     

非均匀噪声环境下基于矩阵补全的DOA估计方法

针对传统的信号波达方向（DOA）估计算法无法适用于实际应用中非均匀噪声、数据不完整等情况的问题,提出了一种结合矩阵补全理论和最大似然交替投影算法的DOA估计方法。在背景噪声为非均匀噪声的情况下,该方法通过对只有部分元素已知的阵列协方差矩阵进行矩阵补全,将稀疏矩阵重构为无噪声协方差矩阵,然后利用最大似然交替投影算法实现对DOA的估计。实验仿真表明:该DOA估计方法能够有效恢复不完整数据并抑制非均匀噪声的影响,而且在低信噪比条件下,仍具有较好的DOA估计性能。     

New method for estimating 2-D DOA in the coherent source environment based on data matrix reconstruction

The performance of classical two dimensional (2-D) Direction-Of-Arrival (DOA) estimation algorithms degrade substantially in the presence of coherent environment. A new DOA matrix method——DOA matrix method based on data matrix reconstruction (DMR-DOAM) is proposed for 2-D DOA estimation in the coherent source environment. The proposed algorithm reconstructs two Toeplitz equivalent covariance matrices by using cross-correlation information among receiving data from arrays. Decorrelation and 2-D DOA estimation can be realized via the eigen-decomposition of the new DOA matrix. The algorithm can retain the advantages of the traditional DOA matrix method, such as automatical parameter alignment and no need of 2-D search spectrum peak. The equivalent covariance matrices only use the middle column of classical covariance matrices, so the calculation amount is reduced, and the algorithm can be realized easily. Furthermore, the paper analyzes the estimation performance and influencing factors of the proposed algorithm. Theoretical analyses and simulation results both show that the proposed algorithm is effective.     

Micro-Doppler Parameter Estimation Method Based on Compressed Sensing

A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper. The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this paper. Relatively ideal micro-Doppler parameter estimation results were obtained. The proposed micro-Doppler parameter estimation was compared with the traditional micro-Doppler parameter estimation algorithm. Requirements for return signal length were analyzed with this new algorithm and its performance was also analyzed in various environments with different SNR.     

一种混合网台跳频参数盲估计算法

针对低信噪比（SNR）和复杂电磁环境条件下跳频参数估计精度低及算法复杂度高的问题,提出了一种短时傅里叶变换（STFT）和平滑伪魏格纳分布（SPWVD）的组合时频分析方法.该算法首先利用STFT将天线接收信号变换到时频域,并对时频信号进行自适应降噪处理;通过自适应聚类算法进行频率的精估计;提取时频信息并剔除各类干扰,再通过网台分选后得到各类网台跳时粗估计;最后采用SPWVD及修正后的截断门限进行跳变时刻的精估计.仿真结果表明,该算法在混合网台和低SNR条件下,跳频参数估计精度较高,算法复杂度较低,有效解决了实际跳频通信系统存在频率转换时间条件下的参数估计问题.     

直接序列扩频信号PN序列盲估计方法

在非协作信息侦测情况下,提出了一种实用的直接序列扩频(DS-SS)通信信号PN码序列的快速盲估计方法。该方法根据DS-SS基带信号的特点,首先采用二阶循环统计量估计PN码周期,然后利用分段互相关平均估计PN码同步始点;并在准确预估计这2个参数的基础上,将接收信号以PN码周期分成多个时段,利用多重互相关方法估计PN码序列。理论分析与仿真表明,该方法在截获信号信噪比很低的情况下有很好的估计性能,且无需任何先验知识,对PN码码型也没有任何限制,是一种有效的快速盲估计方法。     

基于加权1范数稀疏信号重建的DoA估计

针对l₁范数下奇异值分解的l₁-SVD稀疏信号重建的波达方向估计方法存在求解量的稀疏性较差且空间谱中存在较多的伪峰,不能准确估计波达方向的问题,对接收信号矩阵进行预处理,并使用信号子空间设计权值矢量得到更好的稀疏性和更好地逼近l₀范数,利用得到的权值矢量对l₁-SVD算法中解矢量的各个元素进行加权,以得到的加权l₁范数作为最小化的目标函数进行优化.仿真结果表明,提出的算法在快拍数、正则化参数和信噪比等条件改变的情况下能有效抑制伪峰,并准确稳定地估计出波达方向.     

跳频信号2D-DOA与极化参数的欠定估计

为在欠定条件下估计跳频(frequency hopping,FH)信号二维波达方向(two dimensional direction of arrival,2D-DOA)和极化参数,从而有效辅助FH网台分选和信号识别、跟踪等,提出基于空间极化时频分析的联合估计算法.在建立FH信号极化敏感阵列快拍数据模型基础上,推导空间极化时频分布(spatial polarimetric time frequency distributions,SPTFD)的线性时频扩展形式SPSTFT,同时给出一种组合时频分布方法定位各跳(hop)信号在时频面上的自项区域,据此构造各hop的SPTFD和SPSTFT矩阵.利用SPSTFT/SPTFD矩阵中蕴含的信源极化-空域特征信息采取两种不同方法估计2D-DOA和极化参数.新算法无需多维参数寻优和配对,计算量小.仿真结果表明,本算法能在欠定条件下有效估计FH信号2D-DOA和极化参数,SPTFD矩阵法估计精度高,并能处理发生频率碰撞的hop.     

Remote sensing image fusion based on sparse non-negative matrix factorization

In order to reduce the spectral and spatial distortions, a novel method based on sparse non-negative matrix factorization (SNMF) is proposed for multispectral and panchromatic images fusion. Firstly, the high spatial resolution and low spatial resolution dictionaries are learned from panchromatic. Then we construct a sparse non-negative matrix factorization model of the multispectral image. Thus, the coefficients matrix with spectral information can be obtained. The high spatial resolution multispectral image is produced by the multiplication high spatial resolution dictionary and the coefficients matrix. By introducing the sparse regularization, the instability of the standard non-negative matrix factorization is conquered and the fused image can preserve the high spectral and spatial information. Some experiments are made on QuickBird and Geoeye satellite datasets, and experimental results show that our proposed method can reduce distortions in both the spectral and spatial domains, and outperform some related pan-sharpening approaches in visual results and numerical guidelines.     

Fast space-time adaptive processing method by using the sparse representation

One of the key problems of space-time adaptive processing (STAP) is how to estimate the clutter covariance matrix (CCM) accurately with a small number of samples when the clutter environment is heterogeneous. The CCM estimation methods based on sparse representation (CCM-SR) can achieve a good estimation performance with only one or a few samples, which significantly improves the convergence rate of the STAP. By using the sparsity characteristic of the clutter spectrum, the CCM-SR method estimates the clutter spectrum and yields a good estimation of the CCM. However, there are often many pseudo-peaks in the clutter spectrum estimated by the sparse representation (SR), which will cause a CCM estimation error. By exploiting the special relationship of the clutter ridge curve between space domain and Doppler domain, we can eliminate the pseudo-peaks in the clutter spectrum effectively via fitting the curve of the clutter ridge and improve the estimation accuracy of the CCM. In addition, a byproduct of our method is the estimation of the flying parameters (the velocity of the radar platform, the crab angle and so on). Experimental results show that the proposed method can improve the performance of conventional STAP based on sparse representation (STAP-SR) and obtain a good estimation of the flight parameters.     

带L1规划的广义线性模型中的一种系数估计法

介绍了L1规划广义线性模型（GLM）的一种系数估计法,估计系数的同时进行变量选择,从而确立模型。事实上,此算法用来选择变量更有用,L1规划法按照对系数一范数的惩罚来选择变量,是向前选择变量法的一种改进,运用凸优化的预测—修正法,GLM系数估计法可有效地算出系数,其中规划参数的步长对控制系数精确性至关重要,本文对三种步长选择法作了相应的比较和分析,接着给出算法的推广。最后总结算法并且对这种估计法的应用范围进行了展望。     

基于S变换的混合DS/FH扩频信号参数估计

针对混合二相编码调制的混合扩频信号的参数估计问题,提出了一种基于S变换的跳频参数估计方法．该方法利用非线性变换获得携带跳频信息的参考信号,并利用循环谱估计该信号的频率集作为修正信息; 在S变换中引入高斯窗长控制参数以实现其时频分辨率的可控性．在此基础上,提取修正S变换时频脊线作为对参考信号瞬时频率的估计; 利用修正信息降低脊线高频区域的误差,同时抑制噪声对参数估计精度的影响．仿真实验表明了该算法对跳频参数估计的有效性．算法无需信号的先验知识,并易于通信侦测接收机的实现．     

跳频信号的时频分析新方法

根据跳频信号的特点,提出了一种基于信号分解的时频分析方法。该方法先将多分量跳频信号通过带通滤波变成多个单分量信号,再将每个分量的WVD线性叠加得到新的时频分布。理论分析和仿真结果表明,这种新的时频分析方法能够有效抑制跳频信号的交叉项干扰,与现有的方法相比,具有更高的时频分辨率,更适合于跳频信号的时频分析和参数估计。     

MIMO radar waveform design method via low sidelobe beampattern modification

In the design of the multiple-input multiple-output(MIMO) radar waveform, decreasing transmit beampattern sidelobes could reduce the energy of clutters and false targets from sidelobes and increase the received signal to noise ratio (SNR) so as to improve the angle estimation property. Motivated by this idea, a transmit waveform design method based on low sidelobe beampattern modification is proposed for MIMO radar. First, the transmit waveform cross-correlation matrix can be obtained by the semidefinite relaxation (SDR) technique. Then the essence of our method is to establish an optimization modification model to reduce the beampattern sidelobe by changing the non-diagonal elements of the waveform cross-correlation matrix. Finally, the corresponding transmit beamforming weight matrix is obtained by this modified matrix, and the ESPRIT algorithm is used for the direction of arrival (DOA) estimation. Meanwhile, the feasibility of our method is demonstrated. Simulation results show the superiorities of our method in sidelobe suppression, DOA estimation accuracy and angle resolution.     

利用稀疏贝叶斯理论的跳时估计方法

当跳频信号的频率不在预设的频率集中时,为了提高跳时估计的正确率,提出了一种基于稀疏贝叶斯理论的跳时估计方法。该方法首先在信号模型中设置频率偏差参数;其次利用狄利克雷过程以及稀疏贝叶斯理论,设计接收信号模型中各个参数的迭代规则,并在每次迭代中利用频率偏差参数修正频率字典矩阵;最后,算法收敛时可得到用于计算谱图的稀疏矩阵,进而可以得到跳时的估计值。仿真结果表明,该算法估计的跳时正确率高于其他方法,并且计算的谱图的真实性也高于其他方法。     

基于压缩感知的码片内多径时延估计

针对码片内多径时延难以估计的问题,结合PN序列的伪随机特性和一般信道冲激响应的稀疏特性,提出了一种基于压缩感知的时延估计算法,将时延估计问题转化为稀疏向量的估计问题。利用压缩感知方法估计稀疏向量,对向量只有稀疏度要求,而对向量中非零值的位置没有任何要求,所以本文提出的算法对码片内多径具有分辨能力。通过仿真实验验证了文中提出的算法在低信噪比下对码片内多径时延估计具有鲁棒性,并且得出了算法运算量与稀疏向量维数之间的关系。     

一种联合跳频信号参数估计方法

提出一种新的跳频信号参数联合估计算法,即对跳频信号作较短窗长STFT,然后提取其时频脊线得到跳频周期、跳变时刻的精确估计和跳频频率的粗估计,最后对跳频频率的粗估计值所在频域区间进行Zoom-FFT细化分析,得到跳频频率的准确估计。仿真结果表明,与基于SPWVD和基于小波变换的参数估计方法相比,该方法具有更好的抗噪性能和更小的计算量。