一种对称α稳定噪声中的DOA估计新方法

本文研究了对称α稳定(SαS)噪声环境中窄带信号源的DOA估计问题。SαS过程能很好地描述许多具有冲激特性的信号和噪声,但其二阶和高阶统计量不存在。利用SαS过程的性质,提出了一种基于数据矩阵加权的子空间类DOA估计新方法。该方法与分数低阶矩法作了比较,仿真结果显示了所给算法的优越性。     

脉冲噪声环境下基于宽带模糊函数的双基地MIMO雷达目标参数估计新方法

以Alpha稳定分布作为噪声模型,研究了脉冲噪声环境下宽带双基地MIMO雷达系统中参数估计问题.针对在脉冲噪声环境中,基于传统的信号模型和算法效果显著退化的问题,本文提出了基于分数低阶统计量的宽带模糊函数算法.首先根据分数低阶宽带模糊函数的峰值点实现对多普勒频率尺度因子和时延的联合估计.接下来基于分数低阶宽带模糊函数构造两个子阵.通过采用改进的MUSIC算法和ESPRIT算法实现了收发角的联合估计.仿真实验表明本文算法具有很好的性能.     

基于Screened Ratio原理的冲击噪声环境下DOA估计算法

该文提出了一种冲击噪声环境中DOA估计的算法。算法首先根据Screened ratio原理构造阵列信号的相关矩阵,然后利用MUSIC算法实现DOA估计。与基于分数低阶矩(FLOM)的算法相比,该文算法不需要选择FLOM参数p。计算机仿真表明该文算法在冲击噪声环境下具有更佳的稳定性和估计精度。     

基于矩阵补全的二阶统计量重构DOA估计方法

该文针对传统波达方向角(DOA)估计算法在非均匀噪声下角度估计精度差及分辨率低的问题,基于矩阵补全理论,提出一种二阶统计量域下加权L1(MC-WLOSRSS)稀疏重构DOA估计算法。首先,基于矩阵补全方法,引入弹性正则化因子将接收信号协方差矩阵重构为无噪声协方差矩阵;而后在二阶统计量域下通过矩阵求和平均将无噪声协方差矩阵多矢量问题转化为单矢量问题;最后利用稀疏重构加权L1范数实现DOA参数估计。数值仿真表明,与传统MUSIC, IL1-SRACV, L1-SVD子空间算法及稀疏重构加权L1算法相比,所提算法能显著抑制非均匀噪声影响,具有较好DOA估计性能,且在低信噪比条件下,亦具有较高估计精度和分辨力。     

单快拍数据的分布式阵列DOA估计

该文针对分布式阵列相干信号单次快拍波达方向估计问题,提出一种基于状态空间平衡法的1维波达角估计算法。该算法首先直接利用单快拍数据以分布式阵列每个子阵单元进行Hankle矩阵构造,然后采用状态空间平衡法,分别获得低精度无模糊的子阵单元内DOA估计和高精度有模糊的子阵单元间DOA估计,最后结合配对和解模糊算法获得高精度无模糊DOA估计。该算法不受信号形式限制,可同时对相干信号和非相干信号进行处理,能充分利用分布式阵列扩展阵列物理孔径特性,获得较高的DOA估计精度。计算机仿真结果验证了所提算法的有效性。     

单快拍数据的分布式二维阵列测角方法研究

该文针对2维阵列波达方向估计问题,提出一种基于单快拍数据的分布式2维DOA估计算法。该算法首先利用每个子阵单元的单快拍数据进行2维Hankle矩阵构造;然后基于2维状态空间平衡法分别获得方位角和俯仰角子阵单元内DOA估计与子阵单元间DOA估计;最后通过解模糊算法获得方位角和俯仰角高精度无模糊DOA估计。该算法较好地解决了子阵单元内DOA估计和子阵单元间DOA估计之间的配对问题以及俯仰角和方位角之间配对问题,充分利用分布式阵列扩展阵列物理孔径特性;同时该算法可直接对相干信号和非相干信号进行处理。计算机仿真结果验证了所提算法的有效性。     

基于传播算子的声学矢量传感器阵列 扩展孔径二维DOA估计算法

 本文提出一种基于传播算子的声学矢量传感器阵列扩展孔径二维DOA估计算法.首先,利用传播算子方法得到一组高精度模糊的DOA估计值;然后,利用声学矢量传感器的特点得到另一组低精度无模糊的DOA估计值;最后,利用无模糊估计值对模糊估计值进行解模糊处理,得到高精度无模糊的DOA估计值.提出的算法无需进行特征值分解或奇异值分解进行信号子空间/噪声子空间的估计.与基于ESPRIT的算法相比,提出的算法的计算量约为信号个数与声学矢量传感器个数的四倍之比.计算机仿真结果表明在信噪比不是很低时,提出的算法与基于ESPRIT的算法具有相当的估计性能.     

Alpha稳定分布噪声环境下类M估计相关的DOA估计新算法

提出了一类适用于Alpha稳定分布随机变量的统计量—类M估计相关（MELC）,通过构造阵列输出的类M估计相关矩阵,提出了适用于Alpha稳定分布噪声环境下的波达方向（DOA）估计新算法,即MELC-MUSIC算法。仿真实验表明,在Alpha稳定分布噪声环境下,MELC-MUSIC算法在抗噪声特性、多源信号分辨性以及对不同形式信号（圆对称信号或非圆对称信号）的适应性方面获得比基于分数低阶统计量（FLOS）的MUSIC方法更好的估计性能。     

脉冲噪声下基于Renyi熵的分数低阶双模盲均衡算法

针对脉冲噪声下盲均衡器难以快速收敛并有效抑制噪声的问题,该文提出一种基于Renyi熵的分数低阶双模盲均衡算法。该算法将Renyi熵与分数低阶统计量相结合并用作代价函数来更新盲均衡器权向量,利用Renyi熵提高算法的收敛速度,利用分数低阶统计量增强算法对脉冲噪声的抑制能力。为了提升系统稳健性,该文进一步提出双阈值加权判决法,通过设置双阈值并引入非线性加权函数,使得两种代价函数之间的切换更为平滑。在不同脉冲性噪声、不同信道环境下进行仿真实验,结果表明,该文算法既能有效抑制脉冲噪声,又具有较快的收敛速度。     

冲击噪声背景下相干信源DOA估计方法研究

研究了冲击噪声环境下相干信源波达方向(DOA)估计问题.在对称α稳定分布冲击噪声假设下,基于共变和分数低阶矩的MUSIC(即ROC-MUSIC和FLOM-MUSIC)方法不能用于相干信源DOA估计.本文首次将空间平滑思想应用于共变系数矩阵和分数低阶矩矩阵中,定义了新的前后向平滑共变系数矩阵和前后向平滑分数低阶矩矩阵,提出了两种新的适用于冲击噪声环境的相干信源DOA估计方法:基于前后向平滑共变系数矩阵的空间平滑(ROC-SS)算法和基于前后向平滑分数低阶矩矩阵的空间平滑(FLOM-SS)算法.理论分析表明,可以通过前后向平滑共变系数矩阵和前后向平滑分数低阶矩矩阵的特征分解来估计噪声子空间,从而实现对相干信源的DOA估计.论文还对提出的ROC-SS算法和FLOM-SS算法进行了性能对比分析.计算机仿真结果证明了ROC-SS算法和FLOM-SS算法的有效性和正确性.     

Efficient 2D DOA estimation of coherent signals in spatially correlated noise using electromagnetic vector sensors

This paper considers a new azimuth-elevation DOA estimation algorithm for multiple signals using electromagnetic vector sensor array. We firstly exploit the planar-plus-an-isolated sensor array geometry (Li et al. in IEE Proc Radar Sonar Navig 143(5):295–299, 1996) to define a full rank cross-covariance matrix. Then we develop an efficient ESPRIT-like algorithm using the so-called propagator to estimate the steering vectors of electromagnetic vector sensor, without performing eigen-decomposition into signal subspaces. Finally, we compute the vector cross product to obtain the closed-form azimuth-elevation angle estimates. The new algorithm does not require 2D iterative searching, and is applicable to coherent (fully correlated) signals and spatially correlated noise. In addition, the proposed algorithm offers enhanced estimation precision by sparse array aperture extension, but suffers no DOA cyclical ambiguity. Monte-Carlo simulations are presented to verify the effectiveness of the proposed algorithm.     

循环平稳信号的联合稀疏表示DOA估计

基于联合稀疏表示的思想,本文提出了一种利用二阶循环统计量的循环平稳信号波达方向（DOA）估计算法。首先,对传统的谱相关信号子空间拟合算法进行分析研究;然后,通过在循环域构造过完备的阵列方向矩阵字典,建立了联合稀疏表示模型,从而将循环平稳信号的DOA估计问题转化为联合稀疏矩阵的恢复问题;最后,利用联合Ι_2,0范数逼近法求出联合稀疏矩阵的优化解,并根据优化矩阵中非零行的位置估计出循环平稳信号的DOA。与传统的SCSSF算法相比,所提算法具有更高的DOA估计精度,同时也适用于信号个数多于阵元个数的场合。理论分析和仿真实验结果都表明了算法的有效性。      

基于MI-MUSIC的分布式阵列波达方向估计方法

针对分布式阵列的波达方向估计,提出了一种基于多尺度旋转不变(MI)MUSIC的波达方向估计方法。首先对分布式阵列进行子阵划分,构造具有多尺度旋转不变性的子阵;然后利用酉ESPRIT算法得到精度低但无模糊的粗估计,采用MI鄄MUSIC算法得到一组精度高但包含模糊的精估计;最后以粗估计为参考解精估计的模糊,从而得到高精度无模糊的波达方向估计。所提方法具有分布式阵列及MI-MUSIC的优点,对子阵内部阵元位置误差不敏感,且精度较双尺度ESPRIT高。计算机仿真结果验证了该方法的有效性,也验证了分布式阵列DOA估计中存在基线模糊门限与信噪比门限。     

DOA estimation of noncircular signals for coprime linear array via locally reduced-dimensional Capon

We investigate the issue of direction of arrival (DOA) estimation of noncircular signals for coprime linear array (CLA). The noncircular property enhances the degree of freedom and improves angle estimation performance, but it leads to a more complex angle ambiguity problem. To eliminate ambiguity, we theoretically prove that the actual DOAs of noncircular signals can be uniquely estimated by finding the coincide results from the two decomposed subarrays based on the coprimeness. We propose a locally reduced-dimensional (RD) Capon algorithm for DOA estimation of noncircular signals for CLA. The RD processing is used in the proposed algorithm to avoid two dimensional (2D) spectral peak search, and coprimeness is employed to avoid the global spectral peak search. The proposed algorithm requires one-dimensional locally spectral peak search, and it has very low computational complexity. Furthermore, the proposed algorithm needs no prior knowledge of the number of sources. We also derive the Crámer-Rao bound of DOA estimation of noncircular signals in CLA. Numerical simulation results demonstrate the effectiveness and superiority of the algorithm.     

Direction of Arrival Estimation of Multiple UWB Signals

This paper deals with the problem of estimation of direction of arrivals (DOA) of a multiple ultra-wideband (UWB) pulse postion modulation signals incident on a smart antenna in the presence of white Gaussian noise. We transform the received signal into frequency domain in order to split the array output into multiple frequency channels. Corresponding frequency channels data of the array is arranged into a model similar to narrowband DOA estimation. Iterative quadratic maximum likelihood algorithm is applied to yield DOA estimates. These separate estimates at different frequencies are combined into a single estimate of DOA for each source in an appropriate manner. The performance of the proposed method is studied via extensive computer simulations. It is seen that the technique can successfully resolve the DOA of the closely-spaced UWB signals.     

Unfolded coprime L-shaped arrays for two-dimensional direction of arrival estimation

Generally, a coprime L-shaped array (CLsA) is composed of two uniform L-shaped subarrays with larger spacing among inter-element to accomplish the improved direction of arrival (DOA) estimation performance. In this paper, the two subarrays are unfolded to extend the array aperture and the performance of the unfolded CLsA (UCLsA) for two-dimensional (2D) DOA estimation is investigated. In addition, an all array multiple signals classification (AA-MUSIC) algorithm is proposed for the UCLsA. By stacking the received signals of the two subarrays, the ambiguity problem can be avoided on the basis of the coprime property. Simultaneously, due to the combination of the cross-correlation and auto-correlation, the proposed AA-MUSIC algorithm can achieve the full degrees of freedom (DOFs) and obtain more accurate DOA estimates, nevertheless, the expensive total spectral search is entailed. Consequently, a reduced complexity MUSIC (RC-MUSIC) algorithm is proposed to relieve the computational burden. The Cramer-Rao Bounds (CRBs) are utilised as a theoretical benchmark for the lower bound of unbiased estimate. Furthermore, numerical simulations verify the effectiveness and superiority of the AA-MUSIC algorithm and RC-MUSIC method for the UCLsA.     

Nested planar array: configuration design,optimal array and DOA estimation

Nested array enables to enhance localisation resolution and achieve under-determined direction of arrival (DOA) estimation. In this paper, we improve the traditional nested planar array to achieve more degrees of freedom (DOFs) and better angle estimation performance. The closed-form expressions for sensor positions of the improved array are given and the optimal array configuration for largest available DOFs is derived. Meanwhile, a computationally efficient DOA estimation algorithm is proposed. Specifically, we utilise two dimensional Discrete Fourier Transform (2D DFT) method to obtain the coarse DOA estimates; Subsequently, we achieve the fine DOA estimates by 2D spatial smoothing multiple signals classification (SS-MUSIC) algorithm. The proposed algorithm enjoys the same estimation accuracy as SS-MUSIC algorithm but with lower complexity because the coarse DOA estimates enable to shrink the range of spectral search. In addition, estimation of the number of signals is not required by 2D DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm.     

空间任意阵的多宽带信源定位算法性能研究

针对宽带信号非平稳特性,以及常用宽带DOA估计算法要求信源个数是已知的情况,提出一种新的基于空间任意阵的非平稳信号DOA估计算法。首先,通过短时傅里叶变换将信号转为频域表示,然后构造阵列频域数据模型,最后利用短时功率谱矩阵的联合对角化特性实现宽带信号的波达方向估计。本文对提出的算法进行了理论分析,并在常见阵型（如线阵、十字阵）上进行了仿真和性能分析,仿真结果表明该算法可高分辨率地估计出DOA,并且估计误差小。相对于已有的MUSIC谱估计方法,该算法无需进行信源个数估计,更具实用性。      

Linear Chirp Signal DOA Estimation Using Sparse Time–Frequency Dictionary

This paper is concerned with the estimation of the directions-of-arrival (DOA) of multiple linear chirp signals. We construct a novel time-frequency dictionary based on the properties of chirp signals in the fractional Fourier domain, and a sparse reconstruction algorithm is proposed to achieve high performance. Then, the errors resulting from the off-grid model mismatch is considered, and the dictionary matrix is reformulated into a multiplication of a fixed matrix and a sparse matrix. Further, an iterative alternating approach is proposed to improve the accuracy of the DOA estimates. The proposed algorithm provides better estimation, anti-correlation performances and increased resolution than Multiple Signal Classification (MUSIC) and the time–frequency MUSIC (TF-MUSIC) based on the spatial time–frequency distributions. Simulation results demonstrate the effectiveness of the proposed approach.

     

复合式均匀圆阵提高相关信号DOA估计性能

为使均匀圆阵能进行相关信号的全方位DOA估计及提高系统性能,论文利用基于自适应天线 理论方向图综合技术的子圆阵构成复合式测向均匀圆阵,再运用混合DOA估计方法进行DOA估计。仿 真结果证明复合式测向均匀圆阵在一定程度上使分布区域较广的相关信号的DOA估计成为可能;增加了 可检测的信源数目;提高了系统抗干扰能力及分辨率、使系统判模糊能力增强,稳健性能增加。