Compressed Sensing Trilinear Model-Based Blind Carrier Frequency Offset Estimation for OFDM System with Multiple Antennas

This paper addresses the problem of carrier frequency offset (CFO) estimation for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas, and links CFO estimation problem in OFDM systems to the compressed sensing trilinear model. Exploiting this link, we derives a compressed sensing trilinear model-based CFO estimation algorithm. The signal model can be expressed as a trilinear model, and there exists three compressed matrices which can compress the trilinear model to be a lower dimensional one. After that, trilinear decomposition is performed for the compressed model to get the estimations of compressed data matrices, and then the CFO can be estimated through solving the sparse recovery problem via \(\ell _0\) -norm constraint. The proposed algorithm has much better CFO estimation performance than ESPRIT method, and performs very close to the trilinear decomposition-based approach which has heavier computational burden and requires much greater storage capacity. Furthermore, our proposed algorithm can even work in condition of no virtual carrier. We derive the Cramer-Rao bound of CFO estimation for multiple antenna OFDM system. Simulation results illustrate performance of our algorithm.     

Angle Estimation Using Quaternion-ESPRIT in Bistatic MIMO-Radar

In this letter; we present a novel two-dimensional angle estimation for bistatic multiple-input multiple-output (MIMO) radar. We reconstruct the received signal of MIMO radar to model with quaternion theory, and then angle estimate using quaternion estimation of signal parameters via rotational invariance technique for MIMO radar is proposed. The proposed algorithm can obtain automatically paired two-dimensional angle estimation in MIMO-radar. The proposed algorithm has much better angle estimation performance than the Wang’s quaternion algorithm, which has a much heavier computational load than the proposed algorithm. Simulation results verify the usefulness of our algorithm.     

基于空域平滑稀疏重构的DOA估计算法

该文提出一种基于空域平滑稀疏重构的DOA估计算法,利用空域平滑理论对协方差矩阵进行处理,然后通过KR积变换改变其结构,并对变换后的矩阵进行稀疏重构获得角度估计。此外,该文还给出了两种不同的目标函数误差求解方法。从仿真实验可以看出,该算法与传统的基于压缩感知理论的DOA估计算法对比,明显降低了运算量,且对于相干和非相干信号的处理性能均有所提高,在低角度间隔、低信噪比和低采样数条件下优势更为突出。     

Joint sparse model based data reconstruction algorithm for wireless sensor network

The data of wireless sensor network has strong joint sparse characteristics,by utilizing compressed sensing theory,compressed data by joint encoding,and then reconstructed the data by joint decoding,the sensed data can be gathered with low computational cost.A synchronous subspace pursuit algorithm based on joint sparse model and com-pressed sensing theory was proposed.By utilizing the sparsity of the sensed data,it selected the correct joint subspace and reconstruct the original signal group accurately with fewer observations in a backtracking iterative manner.Com-pared with SCoSaMP algorithm and SP algorithm,the proposed algorithm presents better data reconstruction perform-ance under the conditions of different sparsity and sampling rate.     

Computationally efficient DOD and DOA estimation for bistatic MIMO radar with propagator method

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Cramér–Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm.     

帧间自适应语音信号压缩感知

近年来提出的压缩感知是一种以低于传统奈奎斯特速率对信号采样可得到精确恢复的理论。该理论很快应用于简化传统的采样硬件、缩短采样时间、以及减少数据的存储空间。针对语音信号的传输问题,本文提出一种帧间自适应语音信号压缩感知的方法。在离散余弦变换域的语音信号具有稀疏性的前提下,以大量语音信号帧的分析统计为依据,提出一种基于语音帧能量分级和帧间位置惯性的语音信号自适应压缩感知算法。实验结果表明,能量自适应可以显著地提高语音信号的恢复质量,而位置自适应可以明显地减少语音信号的恢复时间,从而本文提出的算法可以用较少的恢复时间获得较好的恢复效果。      

基于压缩感知的分布式协同估计算法

为了降低分布式协同估计算法的计算量并改善其收敛性能,提出了基于压缩感知(CS)和递归最小二乘(RLS)的分布式协同估计算法.该算法在传统RLS分布式协同估计算法的基础上引入压缩感知技术,首先在压缩域中进行递归最小二乘运算,然后利用压缩感知重构算法得到未知参数向量的估计值.提出的算法能够在增量式策略和两种模式的扩散式策略下实现对未知向量的有效估计.理论分析和仿真结果表明,该算法一方面降低了RLS分布式协同估计算法的计算量,另一方面保持较快的收敛速度与良好的均方误差性能.     

基于四元数的Root-MUSIC的双基地MIMO雷达中角度估计算法

该文将四元数理论应用到双基地集中式多输入多输出(MIMO)雷达的角度估计中。文中通过传统数据模型构造四元数矩阵,提出了基于四元数的求根-多重信号分类(Root MUltiple SIgnal Classification, Root-MUSIC)的MIMO雷达中角度估计算法,该算法通过奇异值分解和Root-MUSIC来估计出发射角(Direction Of Departure, DOD)和接收角(Direction Of Arrival, DOA)。该算法的角度估计性能远优于现有文献的方法,并且无需谱峰搜索,复杂度大大降低。仿真结果验证了算法的有效性。     

Channel estimation for multi-panel millimeter waveMIMO based on joint compressed sensing

Channel state information (CSI) is essential for downlink transmission in millimeter wave( mmWave) multipleinput multiple output (MIMO) systems. Multi-panel antenna array is exploited in mmWave MIMO system due to itssuperior performance. Two channel estimation algorithms are proposed in this paper, named as generalized jointorthogonal matching pursuit (G-JOMP) and optimized joint orthogonal matching pursuit (O-JOMP) for multi-panelmmWave MIMO system based on the compressed sensing (CS) theory. G-JOMP exploits common sparsity structureamong channel response between antenna panels of base station ( BS) and users to reduce the computationalcomplexity in channel estimation. O-JOMP algorithm is then developed to further improve the accuracy of channelestimation by optimal panel selection based on the power of the received signal. Simulation results show that theperformance of the proposed algorithms is better than that of the conventional orthogonal matching pursuit (OMP)based algorithm in multi-panel mmWave MIMO system.     

A RD-ESPRIT algorithm for coherent DOA estimation in monostatic MIMO radar using a single pulse

This paper discusses the problem of coherent direction of arrival (DOA) estimation in a monostatic multi-input multi-output (MIMO) radar using a single pulse, and proposes a reduced dimension (RD)-estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. We reconstruct the received data and then utilise it to construct a set of Toeplitz matrices. After that, we use RD-ESPRIT to obtain the DOAs of the sources. The proposed algorithm is effective for coherent angle estimation based on a single pulse, and it has much better angle estimation performance than the forward backward spatial smoothing (FBSS)-ESPRIT algorithm and the ESPRIT-like of Li, as well as very close angle estimation performance to the ESPRIT-like of Han. For complexity comparison, our algorithm has very close complexity to the FBSS-ESPRIT algorithm, and lower complexity than the ESPRIT-like of Han and the ESPRIT-like of Li. Simulation results present the effectiveness and improvement of our approach.     

稀疏L型阵中基于压缩感知的角度估计方法

利用二级Nested阵来构建稀疏L型阵列,针对此阵列,提出了基于压缩感知的角度估计方法。该方法通过计算接收数据的自相关协方差矩阵并向量化,然后进行重排序和去冗余,得到虚拟阵列的入射角信息。该虚拟阵列的长度远远大于实际物理阵列的长度,因而相比同物理阵元的均匀L型阵,阵列孔径和自由度明显增大。最后利用正交匹配追踪技术对虚拟阵列的l1范数约束问题进行求解,并完成二维角度的配对。计算机仿真表明,所提算法具有更高的信源分辨力,并且在高信噪比、高快拍数、大角度间隔条件下,具有更好的估计性能。     

2D-DOA estimation of noncircular signals for uniform rectangular array via NC-PARAFAC method

In this paper, we propose a two-dimensional direction of arrival (2D-DOA) estimation algorithm for uniform rectangular array via noncircular-parallel factor (NC-PARAFAC) method. Compared to the conventional parallel factor (PARAFAC) algorithm, the proposed algorithm exploits the property of noncircular signals to double the array aperture. Therefore, the angle estimation performance of the proposed algorithm is better than the conventional PARAFAC method. The proposed algorithm achieves automatically paired two-dimensional angle estimates, and has better 2D-DOA estimation performance than some conventional algorithms, which include estimation of signal parameters via rotational invariance technique (ESPRIT), propagator method (PM), PARAFAC algorithm, noncircular-ESPRIT (NC-ESPRIT) and noncircular-PM (NC-PM). We also derive the Cramér–Rao bound for the 2D-DOA estimation of noncircular signals with uniform rectangular array. Simulation results verify the effectiveness and improvement of the proposed algorithm.     

基于平行因子四线性分解的二维角度和频率联合估计

该文提出了一种新的2维角度和频率联合估计方法.首先给出双平行线阵结构,将阵列天线输出的信号进行建模分析,表明此信号具有平行因子四线性模型特征,分析了该模型低秩分解的唯一性,从分解得到的矩阵中联合估计出信源的频率和到达角.该方法无需谱峰搜索,可实现参数的同时估计与配对,并与现有的算法进行了比较,具有更高的估计精度,而且在...     

小波模极大值点的信号稀疏表示及重建

作为压缩感知理论的前提,稀疏表示要求信号本身是稀疏的或者在某种正交基下可以稀疏表示。本文针对信号本身及小波变换后均不够稀疏的情况,提出一种基于模极大值点的信号稀疏表示算法。该算法在小波变换的基础上,利用小波分解的结构,对各层高频小波系数通过寻找其模极大值点的方法进行稀疏化,然后通过测量矩阵得到它的测量值,对测量点数进行熵编码以实现数据压缩传输。解码时,采用正交匹配追踪算法得到模极大值点的估计值,最后通过交替投影法重构出原信号。仿真结果表明,与经典压缩感知算法相比,该算法恢复信号的质量有较大提高,且由于稀疏度增大,所以信号具有更好的可压缩性,实验表明本文算法对复杂信号效果更明显。      

基于有限新息率的正交偶极子阵列信号参数估计算法

为解决极化敏感阵列波达方向(DOA)估计中压缩感知类算法的网格失配问题,该文提出一种基于有限新息率(FRI)的正交偶极子阵列无网格信号参数估计算法。首先,利用均匀正交偶极子线阵中不同极化指向天线的两个子阵,求取其自相关矩阵之和,并通过协方差拟合准则恢复出满足Toeplitz结构的协方差矩阵。然后,利用该协方差矩阵构建FRI信号重构模型,求解以重构结果为系数的多项式的零点,就可以得到入射信号DOA参数的估计结果。最后,根据已估计出的DOA参数以及两个子阵的自相关矩阵和互相关矩阵,利用最小二乘法计算得到入射信号的极化参数估计结果。仿真实验表明,该算法与子空间类和压缩感知类算法相比,具有更高的估计精度及更好的角度分辨力。     

采用残差变化控制的自适应稀疏信道估计

针对传统压缩感知信道估计对稀疏度信息依赖和稀疏度自适应信道估计在低信噪比时抗噪能力较差的问题,提出了一种采用残差变化控制的稀疏度自适应的压缩感知信道估计算法。该算法在传统的压缩感知信道估计的基础上引入残差变化控制,通过比较每次迭代下的残差变化的幅度来控制信道估计的迭代次数,提高信道估计的自适应性和鲁棒性。同时,为解决传统稀疏度自适应压缩感知信道估计抗噪能力较差的问题,利用正交匹配追踪提高算法的抗噪声性能。相比于传统的稀疏度自适应匹配追踪(Sparsity Adaptive Matching Pursuit, SAMP)算法,所提算法约有4 dB的性能优势,且算法复杂度更低。     

Compressed Blind Signal Reconstruction Model and Algorithm

The model of inherent connection between underdetermined blind signal separation and compressed sensing (CS) is analyzed first; then, the mathematical model of underdetermined blind signal reconstruction is built using CS. More specifically, the mixing matrix is estimated by exploiting the wavelet packet transform and k-means clustering methods up to permutation and scaling indeterminacy, and then, the measurement matrix and the measurement equation are obtained. To reconstruct the underdetermined sparse source signals, the proposed semi-blind compressed reconstruction algorithm is derived based on the blind signal reconstruction model and compressive sampling matching pursuit (CoSaMP) method. Our simulation results demonstrate that the proposed scheme is effective, irrespective of artificial data or real data. Moreover, the proposed scheme can be adjusted for different applications by modifying the mixing matrix estimation method and CoSaMP method with respect to the correspondence conditions.     

基于分布式压缩感知的改进SOMP信道估计算法

针对多径信道联合稀疏模型,基于分布式压缩感知理论提出了一种适用于正交频分复用（Orthogonal Frequency Division Multiplexing,OFDM）通信系统的改进同时正交匹配追踪（Simultaneous Orthogonal Matching Pursuit,SOMP）信道估计算法。该算法首先联合多个符号利用比较残差和的方式,在每次迭代中估计各符号信道响应公共支撑集与相应元素直到公共支撑集估计结束,然后对各符号信道响应非公共支撑集单独进行估计,最终得到多个符号的信道响应估计值。仿真结果表明,改进的SOMP算法在JSM-2模型下性能与传统的SOMP算法相近,在JSM-1模型下性能优于传统的SOMP算法与OMP算法。     

基于MIMO雷达的极化平滑降维酉ESPRIT算法

相干目标的波达方向估计一直是雷达信号处理中的一个难题。为了获得更好的相干信号角度估计精度,并提高算法可实现性,在多输入、多输出雷达的基础上,提出一种极化平滑降维酉旋转不变性参数估计算法。首先通过降维矩阵对接收信号数据进行降维处理,然后利用降维后的接收数据构造中心复共轭对称矩阵,再构建适当的酉矩阵对其进行实值处理,然后对其进行极化平滑解相干处理,最后构造出实值旋转不变性方程估计目标的波达方向。相比于常规的极化平滑旋转不变性参数估计算法,本文所提极化平滑降维酉旋转不变性参数估计算法的相干信号角度估计精度更高、更利于工程实现。第五节通过仿真实验证明了该算法的有效性与真实性。      

适用于压缩感知估计角度的测量矩阵研究

针对接收数据压缩投影后导致到达角 (Direction-Of-Arrival, DOA)估计精度不高的问题,提出一种高精度的全局信息压缩投影到达角估计算法。该算法首先提出更适应角度估计的空域稀疏化范德蒙矩阵作为测量矩阵,然后对由其组成的Gram矩阵的非对角元素进行压缩处理得到目标矩阵,接着利用步长符合沃尔夫条件的梯度下降法优化Gram矩阵,得到当Gram矩阵与目标矩阵最接近时所对应的可以保留更多全局信息的测量矩阵,最后利用此矩阵压缩接收数据,将接收数据投影到测量矩阵空间,进行稀疏重构得到角度估计结果。仿真实验表明,所提算法角度估计精度远优于同等条件下辐射源信号直接重构的角度估计结果,且在信噪比大于-6dB时数据压缩投影后角度估计的成功率达到100%,性能优越。