非圆信号多级维纳滤波MUSIC测向算法

为降低非圆信号的MUSIC(记为NC-MUSIC)测向算法的计算量,提出了基于多级维纳滤波的NC-MUSIC算法.首先,该算法将非圆信号特性用于多级维纳滤波算法,构造出扩展阵列输出矩阵,利用多级维纳滤波的递推特性求出信号子空间,而不需要估计样本协方差矩阵和对其特征值分解;其次,为了进一步降低算法的计算量,推导出信号子空间的谱峰一维搜索公式进行非圆信号谱峰搜索的计算,快速估算出目标的方位值.仿真结果和计算复杂度分析表明,新算法不但在均方根误差性能上与其他快速算法相似,均接近于NC-MUSIC算法,具有良好的估计性能,而且降低了NC-MUSIC算法的计算最,使其计算复杂度小于非圆信号扩展传播算子快速测向算法的计算复杂度.证实了新算法快速有效的估计性能.     

一种自动匹配的分布式非圆信号二维DOA快速估计方法

在相干分布式非圆信号2维波达方向(DOA)估计中,针对利用非圆特性后维数扩展带来的较大复杂度问题,且现有的低复杂度算法均需要额外的参数匹配,该文提出一种基于互相关传播算子的自动匹配2维DOA快速估计算法。该算法考虑L型阵列,在建立相干分布式非圆信号扩展阵列模型的基础上,首先证明了L阵中两个子阵的广义方向矢量(GSV)均具有近似旋转不变特性,然后通过阵列输出信号的互相关运算消除了额外噪声,最终利用子阵GSV的近似旋转不变关系通过传播算子方法得到中心方位角与俯仰角估计。理论分析和仿真实验表明,所提算法无须谱峰搜索和协方差矩阵特征分解运算,具有较低的计算复杂度,并且能够实现2维DOA估计的自动匹配;同时,相比于现有的相干分布式非圆信号传播算子算法,所提算法以较小的复杂度代价获得了性能的较大提升。     

基于实值矩阵的宽带信号DOA估计方法

针对传统宽带信号DOA估计算法的高计算复杂度问题,提出使用实值矩阵改进的算法。与传统算法在复数协方差矩阵的基础上进行DOA估计相比,本算法仅使用复数协方差矩阵的虚部构造出一个实值矩阵,并在此实值矩阵的基础上,使用子空间算法进行DOA估计。实值矩阵不仅能够降低特征值分解的计算量,而且得到的噪声子空间与真实DOA和镜像DOA均正交,因此在DOA迭代估计时,仅需要对一半的角度进行谱峰搜索,而对于搜索时出现的角度模糊问题,可通过MUSIC算法去模糊,从而达到正确估计效果。相比于传统宽带信号DOA估计算法,本算法在保持DOA估计精度几乎不变的基础上,有效降低了算法计算量,计算机仿真结果验证了本文算法的有效性。     

基于互相关抽样分解的分布式非圆信号DOA快速估计

在非相干分布式非圆信号波达方向(DOA)估计中,针对利用信号非圆特性后输出矩阵维数扩展带来的较大运算量问题,该文提出一种基于互相关抽样分解的DOA快速估计算法。该算法仅需要从子阵间的扩展互相关矩阵中抽样出少量行元素和列元素,构成两个低维子矩阵,进而通过低秩近似分解便可快速地同时求出左右奇异矢量,即分别对应两个子阵的信号子空间,避免了计算整个互相关矩阵及其奇异值分解运算;最后利用两个子阵信号子空间的旋转不变性通过最小二乘得到DOA估计。仿真分析表明,当行列抽样数大于信源数的两倍时,所提算法与直接基于互相关矩阵奇异值分解的非相干分布式非圆信号DOA估计算法性能相近,但复杂度得到了大幅度降低;而相比于传统的低复杂度非相干分布源DOA估计算法,所提算法利用信号非圆特性具有更高的估计性能。     

非均匀线阵的快速求根MUSIC方法

相对于均匀阵列,非均匀阵列在增大天线孔径、避免栅瓣方面具有明显优势。针对非均匀线阵DOA估计带来的高计算量问题,提出了一种快速的实多项式求根MUSIC方法。该方法利用小角度范围内的导向矢量可以利用低阶多项式很好近似的特性,将非均匀线阵DOA估计问题转化为多组低阶实多项式求根问题。由于低阶实多项式具有计算量小以及数值稳定性好等优点,而且多组多项式求根可以并行实现,该方法能够快速地实现非均匀线阵DOA估计,非常适合于工程应用。     

智能射频通信中的快速虚拟DOA算法

在智能射频通信中,通常需要利用信源到达角(DOA)信息来实现对期望信号的有效波束形成,然而这些DOA信息一般是根据相应算法估计得到.当入射信号较多时,基于信号非圆特性的稳健性算法由于对阵列孔径进行了虚拟扩展,故能得到良好的DOA估计.尽管如此.非圆算法需要进行计算量较大的复数特征分解,不利于实现DOA的快速有效估计.为此文中提出了一种计算量远小于现有算法的快速虚拟算法(FVA).FVA可以通过虚拟协方差矩阵的数据来直接构造信号子空间,因此它不需要进行特征分解,就能快速有效地对信号的DOA信息进行估计.理论分析与仿真试验结果表明,在同等条件下与现有非圆算法相比,新算法能以更小的计算量来获得良好的DOA估计性能,故更具应用价值.     

改进嵌套稀疏圆阵下基于OGSBL的DOA估计方法

针对现有基于嵌套稀疏圆阵DOA估计方法计算复杂度高、超参数无法快速选取问题,提出了一种基于改进嵌套稀疏圆阵的离格稀疏贝叶斯学习(OGSBL)方法.该方法首先将改进嵌套稀疏圆阵接收信号的协方差矩阵进行向量化处理,然后构造扩展的观测矩阵,进而结合离格模型与稀疏贝叶斯学习算法实现欠定的DOA估计.仿真实验结果表明,所提算法降...     

内插阵列变换扩展传播算子算法

扩展传播算子(EPM)算法是首先对数据进行扩展,再利用传播算子(PM)方法进行测向的一种算法,该算法充分利用了非圆信号的特点,分辨力和估计精度优于未充分利用非圆信号信息的经典高分辨算法。但是在实际信号测向中,由于阵元位置误差的存在,算法的估计性能会受到一定的影响。因此提出一种基于内插阵列变换的扩展传播算子(VIA-EPM)算法,该算法利用真实阵列流型与虚拟阵列流型之间的变换矩阵,将真实协方差矩阵变换为虚拟协方差矩阵,再对虚拟协方差矩阵进行分块并得出扩展传播算子,进而得出算法的空间谱函数。仿真实验表明:在存在阵元位置误差的情况下,新算法通过对阵元位置校准数据进行内插阵列变换(VIA),取得与阵元位置校准的EPM算法相当的估计性能,保持了阵列扩展能力以及高估计精度,在低信噪比情况下,基于扩展协方差矩阵的VIA-EPM算法的分辨力以及估计精度均要优于基于扩展数据矩阵的VIA-EPM算法。     

基于空时频协方差矩阵重构的高效跳频信号DOA估计

为了充分利用跳频信号的空域信息来进行信号的DOA估计,在信号空时频分析的基础上,本文提出了一种基于协方差矩阵重构的高效跳频信号DOA估计方法。首先将接收信号的均匀线阵（uniform linear array, ULA）平均划分成2个子阵,分别对每个子阵接收到的信号进行时频分析,在时频域选择有效跳,构造每跳的空时频矩阵（spatial time-frequency distribution, STFD）,然后求得2个子阵的互协方差矩阵。将2个子阵的互协方差矩阵进行重构运算得到等效的信号子空间,最后构造空间谱多项式求根估计出信号的DOA。仿真结果表明该方法相比于以往改进类子空间算法能够有效提高估计精度和降低算法复杂度。      

非均匀噪声背景下混合信号DOA估计算法

针对非均匀噪声背景下非相关信源与相干信源并存时波达方向(DOA)估计问题，提出了基于迭代最小二乘和空间差分平滑的混合信号DOA估计算法。首先，该算法利用迭代最小二乘方法得到噪声协方差矩阵估计，然后对数据协方差矩阵进行“去噪”处理，利用子空间旋转不变技术实现非相关信源DOA估计；其次，基于空间差分法消除非相关信号并构造新矩阵进行前后向空间平滑，利用求根MUSIC算法估计相干信源DOA。相比于传统算法，该算法能估计更多的信源数，在低信噪比情况下DOA估计性能更优越。仿真实验结果验证了该算法的有效性。      

FFT-based fast polynomial rooting

A fast root finding algorithm based on an FFT implementation is proposed, thus avoiding the need for a computationally heavy polynomial rooting technique that estimates the eigenvalues of a companion matrix. The minimum-phase polynomial factorisation proposed by Oppenheim and Schafer (1989) is first extended to an arbitrary radius factorisation, then used to extract the roots in an iterative manner     

一种波达方向、频率联合估计快速算法

首先提出了基于PM(propagator method)方法的波达方向(DOA)、频率联合估计快速算法，给出了PM算子的一个估计，由PM算子构造出一特殊的低维矩阵，其特征值给出频率的估计，进而由估计的频率和相应的特征矢量得到DOA的估计。该算法具有参数自动配对，计算量小的优点，易于在工程应用中实时处理。计算机仿真结果证实了算法的有效性。     

Two-dimensional DOA estimation of coherent signals using acoustic vector sensor array

In this paper, we present two new methods for estimating two-dimensional (2-D) direction-of-arrival (DOA) of narrowband coherent (or highly correlated) signals using an L-shaped array of acoustic vector sensors. We decorrelate the coherency of the signals and reconstruct the signal subspace using cross-correlation matrix, and then the ESPRIT and propagator methods are applied to estimate the azimuth and elevation angles. The ESPRIT technique is based on the shift invariance property of array geometry and the propagator method is based on partitioning of the cross-correlation matrix. The propagator method is computationally efficient and requires only linear operations. Moreover, it does not require any eigendecomposition or singular-value decomposition as for the ESPRIT method. These two techniques are direct methods which do not require any 2-D iterative search for estimating the azimuth and the elevation angles. Simulation results are presented to demonstrate the performance of the proposed methods.     

Efficient 2-D DOA estimation for coherent sources with a sparse acoustic vector-sensor array

This paper proposes a computationally efficient two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm based extended-aperture for acoustic coherent signals impinging on a sparse acoustic vector-sensor array. The coherency of incident signals is decorrelated through matrix averaging and the signal/noise subspaces are reconstructed through a linear operation of a matrix formed from the cross-correlations between some sensor data, where the effect of additive noise is eliminated. Consequently, DOAs can be estimated without performing eigen-decomposition (into signal/noise subspaces), and there is no need to evaluate all correlations of the array data. The derived estimates are automatically matched by translating eigenvalues into real-valued ones, furthermore, the proposed method can achieve the unambiguous direction estimates with enhanced accuracy by setting the vector sensors to space much farther apart than a half-wavelength, and it is also suitable for the case of spatially nonuniform noise, which may be more realistic scenario for the sparsely placed sensors. The performance of the proposed method is demonstrated through numerical examples.     

Direction finding for diversely polarized signals using polynomialrooting

A direction finding algorithm for diversely polarized arrays that is based on polynomial rooting is presented. Using polynomial rooting instead of search reduces significantly the computational requirements of the algorithm and enhances resolution. The basic method is limited to linear uniformly spaced arrays. However, using an interpolation technique, the algorithm is extended to a larger class of arrays. The performance of the proposed algorithm is analyzed. Computer simulations are used to demonstrate the performance of the new technique and to verify the performance analysis     

一种快速实值分解Root-MUSIC方位估计算法

针对基阵输出信号的协方差矩阵具有中心-厄尔米特特性,提出了一种基于实值分解技术的Root-MUSIC算法,并给出了一种降阶快速求根方法,这种降阶算法可以降低求解多项武的次数.实值分解技术极大降低了特征分解时的计算量,可分辨多个高度相关或相干信号源.仿真实验结果证明,与传统算法相比,快速实值分解Root-MUSIC算法具有可以分辨相干信号、计算量小和性能好等优点.     

多线性调频信号瞬时频率估计快速算法

对多线性调频信号的瞬时频率估计问题提出一种快速算法,该算法以特征子空间跟踪算法为基础,结合矩阵线性变换和多项式方程求根得到参数估计。该算法的优点是计算量小,其计算量仅与短时傅里叶变换相当;频率分辨力较高;多信号情况下不存在交叉项问题;当多个分量的功率差异达到14 d B时仍能有效估计瞬时频率。但是,由于采用了矩阵求逆的步骤,该算法在低信噪比环境下性能较差。仿真实验显示在信噪比不低于6 d B时,算法具有明显的优越性。     

基于传播算子的Root-MUSIC算法

传播算子（PM）法不需要进行复杂的特征值分解,减小了计算量,但仅在信噪比较高的情况下才有较好的波达方向估计性能,且谱峰搜索仍需要较大计算量。在此基础上提出了一种改进的算法PM-Root-MUSIC,它不需要特征值分解,同时用多项式求根代替谱峰搜索,大大减少了计算量。理论分析和计算机仿真结果表明此方法是有效的。     

Source localization in a waveguide using polynomial rooting

Source localization in acoustic waveguides involves a multidimensional search procedure. We propose a new algorithm in which the search in the depth direction is replaced by polynomial rooting. Using the proposed algorithm, range and depth estimation by a vertical array requires a 1-D search procedure. For a 3-D localization problem (i.e., range, depth, and direction-of-arrival (DOA) estimation), the algorithm involves a 2-D search procedure. Consequently, the proposed algorithm requires significantly less computation than other methods that are based on a brute-force search procedure over the source location parameters. In order to evaluate the performance of the algorithm, an error analysis is carried out, and Monte-Carlo simulations are performed. The results are compared with the Cramer-Rao bound (CRB) and to the maximum likelihood (ML) simulation performance. The algorithm is shown to be efficient, while being computationally simpler than the ML or the Bartlett processors. The disadvantage of the algorithm is that its SNR threshold occurs in lower SNR than in the ML algorithm     

An efficient code-timing estimator for receiver diversity DS-CDMAsystems

We propose an efficient algorithm for estimating the code timing of direct-sequence code-division multiple-access (DS-CDMA) systems that consist of an arbitrary antenna array at the receiver and work in a flat-fading and near-far environment. The algorithm is an asymptotic (for large number of data samples) maximum-likelihood (ML) estimator that is derived by modeling the known training sequence as the desired signal and all other signals including the interfering signals and the additive noise as unknown colored Gaussian noise. The algorithm does not require the search over a parameter space and the code timing is obtained by rooting a second-order polynomial, which is computationally very efficient. Simulation results show that the algorithm is quite robust against the near-far problem and channel fading. It requires a shorter training sequence than the single-antenna-based estimators