基于三维压缩感知的MIMO雷达角度估计算法

基于张量模型的参数估计是雷达信号处理的一个发展趋势,然而现有张量算法无法在估计精度和计算复杂度方面达到良好的折衷。为解决上述问题,提出一种三维压缩感知（Three-way compressive sensing,TWCS）的多输入多输出雷达角度估计算法。利用匹配滤波后的信号内部隐含的多维结构,将接收数据堆叠成一个三阶张量模型。为降低高维张量在存储和计算方面的复杂性,利用高阶奇异值分解对高维张量数据进行压缩。其次将压缩后的张量与三线性模型相联系,获取压缩的方向矩阵。利用目标角度在所处背景的稀疏性,设计两个过完备字典,采用优化的方法获取目标角度。由于利用了接收数据的多维结构,TWCS中参数估计的精度要优于传统的子空间算法。此外所提TWCS算法不需要额外配对计算,且能进一步获取目标的多普勒信息。最后,利用仿真实验验证TWCS算法的估计效果。     

Sparsity estimation based adaptive matching pursuit algorithm

Compared with convex optimization algorithms and combination algorithms, greedy pursuit algorithms can balance operational efficiency and reconstruction precision, so they are widely used in the signal reconstruction step of compressed sensing. However, most existing greedy pursuit algorithms only work well if the signal sparsity is known, and their reconstruction performance is influenced by signal sparsity. To more accurately match the sparsity and obtain better reconstruction performance, we propose a greedy pursuit algorithm, the sparsity estimation based adaptive matching pursuit algorithm, which achieves image reconstruction using a signal sparsity estimation based on the Restricted Isometry Property (RIP) criterion and a flexible step size. Experimental results demonstrate that this algorithm provides better reconstruction performance and lower computation time, using different measurement matrices, when the sparsity is estimated in advance.     

Review of user parameter-free robust adaptive beamforming algorithms

This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms. We present the ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, and the convex combination (CC) as well as the general linear combination (GLC) approaches. The purpose of these methods is to mitigate the effect of small sample size and steering vector errors on the standard Capon beamformer (SCB). We also present sparsity based iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (referred to as IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning), which exploit sparsity to estimate the signal parameters. We provide a thorough evaluation of these beamforming methods in terms of power and spatial spectrum estimation accuracies, output signal-to-interference-plus-noise ratio (SINR) and resolution under various scenarios including coherent, non-coherent and distributed sources, steering vector mismatches, snapshot limitations and low signal-to-noise ratio (SNR) levels. Furthermore, we discuss the computational complexities of the algorithms and provide insights into which algorithm is the best choice under which circumstances.     

结合最小均方误差的改进球形译码检测算法

在多输入多输出(MIMO)系统的信号检测算法中,球形译码算法的检测性能最接近最大似然算法,但传统球形译码算法运算复杂度较高。为降低球形译码算法复杂度,提出一种新型的球形译码检测算法。新算法由改进的快速球形译码算法与最小均方误差算法相结合而成。改进的快速球形译码算法通过在球形半径收缩时乘上一个常量参数来提高半径收缩速度,减少算法搜索的信号点数,从而达到降低复杂度的目的。最小均方误差算法则能够通过减小噪声对接收信号的干扰来降低因搜索噪声点而产生的复杂度。将最小均方误差算法的信道矩阵应用在改进的快速球形译码算法中,将两种算法有效地结合,能够进一步降低算法复杂度。仿真结果表明,当信噪比(SNR)低于10 dB时,新算法相比于原始球形译码算法,检测性能平均提高了9%左右。     

宽带大规模MIMO-OFDM系统自适应稀疏信道估计方案

大规模MIMO-OFDM系统下行链路利用压缩感知算法获得信道状态信息需要已知信号的稀疏度作为先验条件,然而实际环境中,无线信道的稀疏度是未知的。利用大规模MIMO信道的空时共同稀疏性的特点与不同SNR下设置不同停止迭代阈值的思想改进压缩感知重构算法,目的在于使所提算法不仅提升估计性能,还可以准确获得信道的动态稀疏度。通过实验可知,相比传统的CoSaMP算法和S-CoSaMP算法,SSA-CoSaMP算法在同等信噪比下具有更良好的信道估计性能,并且可以自适应地获取稀疏度。更适合实际工程中应用。     

基于改进MUSIC算法的宽带信号DOA估计

现有的波达方向（DOA）估计算法在估计被动探测系统中的宽带信号方位时,存在DOA估计结果偏差大、运算复杂度高等问题,难以满足信号实时处理的要求。为提高多源信号DOA估计的空间分辨率,提出一种基于S变换且不需要预估信号源个数的多重信号分类改进算法。根据宽带信号的频域特征,利用S变换处理阵列接收信号,得到多分辨的时频谱矩阵,同时构建时频域的阵列信号数据模型,结合信号功率谱矩阵呈联合对角化结构的特点,设计基于S变换的子空间谱估计公式。在此基础上,通过谱峰搜索进行DOA估计,实现多源宽带信号的声源定位。仿真结果表明,在信噪比范围为-15～10 dB的条件下,该算法的估计成功率始终保持在90%以上,相比TCT、CS＿TCT、CWT＿MUSIC算法,其具有较优的估计性能,并且无需预估信号源数。     

SOSP: a stepwise optimal sparsity pursuit algorithm for practical compressed sensing

Compressed Sensing (CS), as a promising paradigm for acquiring signals, is playing an increasing important role in many real-world applications. One of the major components of CS is sparse signal recovery in which greedy algorithm is well-known for its speed and performance. Unfortunately, in many classic greedy algorithms, such as OMP and CoSaMP, the real sparsity is a key prior information, but it is blind. In another words, the true sparsity is not available for many practical applications. Due to this disadvantage, the performance of these algorithms are significantly reduced. In order to avoid too much dependence of classic greedy algorithms on the true sparsity, this paper proposed an efficient reconstruction greedy algorithm for practical Compressed Sensing, termed stepwise optimal sparsity pursuit (SOSP). Differs from the existing algorithms, the unique feature of SOSP algorithm is that the assumption of sparsity is needed instead of the true sparsity. Hence, the limitations of sparsity in practical application can be tackled. Based on an arbitrary initial sparsity satisfying certain conditions, the SOSP algorithm employs two variable step sizes to hunt for the optimal sparsity step by step by comparing the final reconstruction residues. Since the proposed SOSP algorithm preserves the ideas of original algorithms and innovates the prior information of sparsity, thus it is applicable to any effective algorithm requiring known sparsity. Extensive experiments are conducted in order to demonstrate that the SOSP algorithm offers a superior reconstruction performance in terms of discarding the true sparsity.

     

保留边缘与细节的压缩采样视频复原算法*

已有的压缩感知视频复原算法因过平滑效应难以保留视频帧的边缘与细节信息,对此提出一种基于混合稀疏性测量的压缩采样视频复原算法。编码端将视频序列分为关键帧与非关键帧,并使用相同的感知矩阵对帧的每块进行采样。解码端则设计了考虑局部稀疏性与全局稀疏性的混合稀疏性测量方案,并将其作为压缩感知视频复原问题的正则项;然后,通过分裂Bregman迭代算法对关键帧进行解码,并考虑视频帧间的时间相关性对非关键帧进行细化处理。基于多组仿真实验的结果表明,本算法获得了较好的视频复原精度,并具有理想的计算时间性能。     

Parameter estimation for control systems based on impulse responses

The impulse signal is an instant change signal in very short time. It is widely used in signal processing, electronic technique, communication and system identification. This paper considers the parameter estimation problems for dynamical systems by means of the impulse response measurement data. Since the cost function is highly nonlinear, the nonlinear optimization methods are adopted to derive the parameter estimation algorithms to enhance the estimation accuracy. By using the iterative scheme, the Newton iterative algorithm and the gradient iterative algorithm are proposed for estimating the parameters of dynamical systems. Also, a damping factor is introduced to improve the algorithm stability. Finally, using simulation examples, this paper analyzes and compares the merit and weakness of the proposed algorithms.     

基于部分支撑集的L1范数稀疏重构算法

针对压缩感知理论中,现有的优化L1范数稀疏重构算法在重构源信号时,当且仅当稀疏度小于等于观测信号长度一半时才能够正确重构源信号的问题,提出了部分支撑集的L1范数稀疏重构算法。改进算法采用线性规划方法最小化源信号"尾部"支撑集的L1范数,能够在稀疏度大于观测信号长度一半时正确重构出源信号。仿真结果表明,在不同信噪比和稀疏度条件下,所提算法的重构精度优于现有的优化L1范数的稀疏重构算法和正交匹配追踪的稀疏重构算法。     

对人体姿态估计热图误差的再思考

近年来,基于热图的算法一直占据人体姿态估计算法的主导地位。热图解码（即将热图转换为人体关节点坐标）算法是这类算法的基本环节。而当前的热图解码算法并没有考虑系统误差的影响,因此,提出一种基于误差补偿的人体姿态估计热图解码算法。首先在训练过程中评估模型的误差补偿因子,然后在推理阶段用误差补偿因子补偿人体关节点的预测误差,这些误差同时包括系统误差和随机误差。在不同的网络架构、输入分辨率、评估指标和数据集上进行的大量实验的结果表明与目前最佳的热图解码算法相比,所提算法获得了显著的精度增益。具体来说,所提算法使HRNet-W48-256×192模型在COCO（Common Objects in Context）数据集上的平均精度（AP）提升了2.86个百分点,使ResNet-152-256×256模型的相对于头部的正确点百分比指标在MPII（Max Planck Institute for Informatics）数据集上提升了7.8个百分点。此外,由于所提算法不像现存算法需要采用高斯平滑预处理和求导操作,因此速度约为当前最佳算法的2倍。可见,所提算法对于开展高精度、高速度的人体姿态估计具有实际的应用价值。     

The study of selective encryption of motion vector based on the S-Box for the security improvement in the process of video

The Selective Encryption method encrypts the important and requisite parts of data. Since the method does not encrypt the whole of data, the amount of computation is small,which makes it faster and the resources can be used efficiently. The existing selective algorithms have vulnerabilities to the plain text attack and the image restoration attack using the motion vector. They are also vulnerable to the attack in storing and transmitting of the random table data using in encrypt and scramble. In this paper, we propose the selective encryption algorithm of motion vector based on S-Box to remove the vulnerabilities of the existing selective algorithms. The motion vectors generated by the end of motion estimation function of video encoding/decoding xored with S-Box table, are replaced to certain location by using mapping table. The S-Box and mapping table are generated by the secret key through the Rivest Cipher 4 (RC4) encryption algorithm. The proposed algorithm enhances the resistance against attacks through the reinforcement of video security, and thus, reduces the vulnerabilities of the existing algorithms such as I-Frame selective encryption and MVEA. Even though the level of security of the proposed algorithm is higher than the bit scrambling algorithms, it has much better security and higher processing rate than others selective algorithms.     

一种基于稀疏度估计的自适应压缩频谱感知算法

针对现有的频谱感知存在信号稀疏度估计所需压缩观测值不能满足信号稀疏度变化时实时跟踪的问题,研究一种基于稀疏系数信息估计的自适应宽带频谱压缩感知方法,在流信号进行稀疏度未知的压缩时,先采集由先验信息得到的观测值数目.在采集到的观测值数目上自适应调整,得到信号稀疏度估计所需的观测值数目,并精确估计信号的稀疏度.仿真结果表明,SCI-CSS算法对流信号频谱能够保持良好的收敛性和较快的跟踪速度,且能有效地确定使信号稀疏度估计所需压缩观测值数目并随信号稀疏度自适应调整,实现对信号稀疏度变化的实时跟踪.     

一种融合聚类与用户兴趣偏好的协同过滤推荐算法

协同过滤推荐算法可以根据已知用户的偏好预测其可能感兴趣的项目,是现今最为成功、应用最广泛的推荐技术。然而,传统的协同过滤推荐算法受限于数据稀疏性问题,推荐结果较差。目前的协同过滤推荐算法大多只针对用户-项目评分矩阵进行数据分析,忽视了项目属性特征及用户对项目属性特征的偏好。针对上述问题,提出了一种融合聚类和用户兴趣偏好的协同过滤推荐算法。首先根据用户评分矩阵与项目类型信息,构建用户针对项目类型的用户兴趣偏好矩阵;然后利用K-Means算法对项目集进行聚类,并基于用户兴趣偏好矩阵查找待估值项所对应的近邻用户;在此基础上,通过结合项目相似度的加权Slope One算法在每一个项目类簇中对稀疏矩阵进行填充,以缓解数据稀疏性问题;进而基于用户兴趣偏好矩阵对用户进行聚类;最后,面向填充后的评分矩阵,在每一个用户类簇中使用基于用户的协同过滤算法对项目评分进行预测。实验结果表明,所提算法能够有效缓解原始评分矩阵的稀疏性问题,提升算法的推荐质量。     

融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法

针对现有大多数兴趣点推荐算法都存在签到数据稀疏、社交关系难以获取、用户个性难以考虑等问题,文中提出融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法.首先考虑用户签到种类信息,同时分解用户签到地点矩阵和用户签到种类矩阵,减小签到数据稀疏带来的影响.再在显式社交关系的基础上,使用信息熵的方法度量用户的隐式社交关系,缓解...     

融合上下文信息与核密度估计的协同过滤推荐

随着互联网信息技术的迅速发展,网络数据量快速增长,如何在海量数据中找到用户感兴趣的信息并实现个性化推荐是目前重要的研究方向。协同过滤算法作为推荐系统中的经典方法被广泛应用于不同场景,但是仍然存在数据稀疏,以及在计算相似度时不能考虑到所有数据的问题,只能够利用具有共同评分的数据,严重影响了推荐的精确度。针对上述存在的问题,提出了一种融合上下文信息与核密度估计的协同过滤个性化推荐算法。该算法通过对用户和项目各自的上下文信息和已经存在的用户评分数据进行处理,通过核密度估计构建用户和项目的兴趣模型,充分挖掘了用户和项目的兴趣分布,以获得更准确的用户和项目兴趣相似度,降低预测评分误差。在公开的数据集上验证表明,将该算法对比传统的协同过滤算法,有效提高了推荐的精确度。     

基于稀疏优化lp正则化的光滑化拟牛顿算法

压缩感知被广泛应用于信号恢复和图像重构与去噪,重构算法是压缩感知的关键部分之一。当采样率很低时,重建原始信号是个困难的问题。对此,现有算法普遍表现不佳。采用[p(0相似文献   

多普勒信号的Burg算法优化研究

随着高速列车的发展,测速精度高的多普勒测速方式被广泛应用。其将多普勒信号看作广义平稳随机信号,利用功率谱估计方法进行信号处理。针对现代谱估计中Burg算法基本原理分析其误差来源。分析了两种优化算法,一种是基于窗函数的优化算法,另一种针对改进的协方差法实现了基于预测误差功率最小意义的优化算法。通过仿真对比原算法与两种优化算法的频谱估计性能,分析算法复杂度,选择模型最优参数,并验证了在列车测速范围内不同频段的适用性。结果表明,优化算法在不增加运行时间的基础上可降低频谱偏移程度、改善频谱分辨率,可识别出列车测速范围内各个频段频率,并且谱估计频率误差小于1%。     

基于MUSIC谱估计的探地雷达回波处理方法

为了克服现有信号处理算法对探地雷达噪声及杂波滤除的不足,提出了一种基于MUSIC谱估计时域与频域联合滤波方法。利用常用的去直流波和均值滤波抑制方法进行雷达信号预处理,在此基础上,引入谱分析中信号子空间与噪声子空间的概念,将MUSIC算法与最小二乘法相结合来进行探地雷达杂波及噪声处理。仿真及实验结果表明,提出的回波处理方法可以有效地抑制雷达信号中的噪声和杂波,能提高测量数据的精度,具有较高的实用性和可用性。     

长期演进系统中一种低复杂度球形译码算法

在长期演进(LTE)系统中,球形译码算法拥有接近于最大似然(ML)的误码率(BER)性能。针对在16QAM和64QAM等高阶调制情况下球形译码算法计算复杂度和所需硬件资源的急剧增加,提出了一种调整符号搜索策略的改进型球形译码算法。该算法在不同的检测层采用特定的符号搜索方案,并结合一种基于信噪比的动态调整半径方法。在无线瑞利信道环境下,对各种球形译码算法进行了仿真。仿真结果表明,提出的改进型算法基本保持传统球形译码算法较低的BER性能,同时还有效地降低了计算复杂度和硬件实现复杂度。