基于虚拟旋转不变技术的宽带信号二维到达角估计

在相关信号子空间方法的基础上,本文提出了一种阵列宽带信号二维角度的估计方法。该方法首先利用虚拟互相关计算方法得到阵列输出的协方差矩阵,并构造出两个子阵(实际子阵和虚拟子阵);然后采用投影算子来形成聚焦矩阵,最后对聚焦后的协方差矩阵采用ESPRIT方法估计出宽带信号的二维到达角。这种方法能抑制非高斯噪声对算法的影响,并能扩展阵列孔径,且不需要进行角度预估计;估计出的二维角度能自动配对,提高了算法的实现速度。计算机仿真试验证实了该算法的有效性。     

基于累积量的互耦误差下相干信号DOA估计

在高斯色噪声和阵列互耦误差背景下,针对相干信号源和非相干信号源并存的问题,提出了一种准确估计信号源到达角(DOA)的算法。首先,采用辅助阵元法将互耦误差从阵列流形中分离;然后结合空间平滑技术和四阶累积量构建平滑矩阵,实现对高斯噪声的抑制和对信号的解相干;最后使用ESPRIT算法获得信号源的来波方向。仿真结果表明,文中算法有效解决了阵列互耦和信源相干的影响,在高斯白噪声和高斯色噪声环境下均能精确地估计DOA。     

ICA-based direction-of-arrival estimation of uncorrelated and coherent signals with uniform linear array

A novel approach for direction-of-arrival (DOA) estimation of uncorrelated and coherent signals with uniform linear array is proposed in this paper. First, the mixing matrix, which contains all azimuth information of signal sources, is estimated by independent component analysis. Afterward, several parameter equations are established upon the new mixing matrix. Finally, all DOAs of coherent and uncorrelated signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Moreover, the signal number resolved by our approach can exceed the number of array elements. Simulation results have demonstrated the efficiency of the proposed method.     

Two-dimensional noisy autoregressive estimation with application to joint frequency and direction of arrival estimation

The present study addresses the problem of two-dimensional autoregressive estimation in the presence of additive white noise. The estimation method is based on combining the low-order and high-order Yule-Walker equations. The noise-compensated YW equations are solved using an iterative algorithm. The proposed method is also applied to joint frequency and direction of arrival estimation in uniform linear arrays. Using simulation study, the performance of the proposed algorithm is evaluated and compared with other methods.     

相干信源波达方向估计的加权空间平滑算法

提出了一种用于空间相干源DOA估计的加权空间平滑算法(WSS，weighted spatial smoothing)。常规的空间平滑算法没有利用子阵输出的互相关信息，而且对相干信源的分辨力较差。WSS算法充分利用了子阵输出的自相关信息和互相关信息，将主阵协方差矩阵的所有子阵阵元数阶的子矩阵进行加权平均，以期提高常规空间平滑算法的分辨性能。文中以加权平滑后等效的信源协方差矩阵的对角化为约束条件，推导了加权矩阵的理论表达式。计算机仿真结果表明，WSS算法与常规空间平滑算法相比具有更高的分辨性能和更低的信噪比门限。特别是在于阵划分较多时其优越性更加明显。     

Adaptive beamspace focusing for direction of arrival estimation of wideband signals

In this paper, we present an adaptive beamspace focusing technique for the direction of arrival (DOA) estimation of wideband signals. The proposed focusing scheme can perform coherent signal subspace transformation in the beamspace domain without preliminary DOA estimation or iteration. It can maintain low focusing error over a predefined sector-of-interest in the field-of-view (FOV) of the array while adaptively suppressing out-of-sector sources. The beamspace gain outside the sector-of-interest is controlled via additional constraints that provide robustness against moving or suddenly appearing out-of-sector sources. We formulate the adaptive beamspace design problem as a second-order cone program (SOCP) that can be solved efficiently using interior point methods. Numerical simulations are presented showing the superior performance of our approach compared to classical non-adaptive beamspace focusing techniques.     

A subspace method for direction of arrival estimation ofuncorrelated emitter signals

A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results     

HOS-based direction of arrival estimation

A new technique for estimating the direction of arrival of multiple non-Gaussian signals using higher order statistics is reported. A set of new algorithms based on a new class of performance criteria related to the bispectral power of the array data is presented. Computer simulations show that the proposed algorithms outperform conventional array processing techniques     

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.     

Two-dimensional direction finding of coherent signals with a linear array of vector hydrophones

In this paper, we propose a parallel factor (PARAFAC) analysis based two dimensional direction finding algorithm for coherent signals using a uniformly linear array of vector hydrophones. By forming a PARAFAC model using spatial signature of vector hydrophone array, the new algorithm requires neither spatial smoothing nor vector-field smoothing to decorrelate the signal coherency. We also establish that the azimuth-elevation angles of K coherent signals can be uniquely determined by PARAFAC analysis, as long as the number of hydrophones \(L \ge 2K - 1\). In addition, because the vector hydrophone array manifold contains no phase information, this new algorithm can offer high azimuth-elevation estimation accuracy by setting vector hydrophones to space much farther apart than a half-wavelength. Simulation results are finally presented to verify the efficacy of the proposed algorithm.     

波达方向估计中的宽带信号个数检测新方法

在进行波达方向估计时,往往要对信号个数进行判断,传统的宽带信号个数检测方法需要在每个频点上进行多次采样,否则无法正确完成测向.针对此问题,提出了一种基于Bootstrap准则实现的宽带信号个数检测新方法.将宽带信号划分为若干个互不重叠的子带部分,之后对每个子带信号进行特征分解,利用特征值对相应的特征向量进行加权,再通过Bootstrap准则构造新的信号重采样矩阵,用聚类的方法判断出该子带上的信号个数,最后将各个子带上的结果进行综合判断出宽带信号个数.所提方法回避了聚焦的过程,且在非高斯噪声背景下也有较高的估计成功概率,尤其是在小快拍数下有着较好的估计性能,仿真结果证明了该方法的性能.     

L型阵列互耦条件下的二维波达方向估计

针对L型阵列,提出一种去互耦算法.该算法在L型阵列的两均匀线阵上分别取受互耦影响一致的阵元,则其理想导向向量可与互耦参数剥离,用其中一组阵元输出的协方差阵和两组阵元输出的互协方差阵构建矩阵,根据其传播算子构成的信号子空间和阵元导向向量张成同一空间以及均匀线阵的旋转不变特性得到两个与方向角和俯仰角相关的信息参量.在这两参量配对时,只需对包含信息参量的其中一个矩阵进行一次特征值分解以及简单的除法运算即可实现.理论和仿真表明,该算法无需谱峰搜索,只需一次特征分解,有效抑制了互耦影响,测量精度高.     

Spectrum blind reconstruction and direction of arrival estimation of multi-band signals at sub-Nyquist sampling rates

In this paper we consider the problem of spectrum blind reconstruction (SBR) and direction of arrival (DOA) estimation of constituent sources of a disjoint multi-band signal (MBS) at sub-Nyquist sampling rates. Transformation of the problem into frequency domain indicates that the steering vector is a function of both the carrier frequency and its corresponding DOA. Employing the existing two dimensional frequency-DOA search algorithms suffers from the drawbacks of increased computational complexity and ambiguity issues. To overcome these drawbacks, in this paper we propose a simple modification to the receiver architecture by introducing an additional delay channel at every sensor. Estimation algorithms based on ESPRIT is then employed to estimate the carrier frequencies, while MUSIC algorithm is employed to estimate their corresponding DOAs. Using the knowledge of both these parameters, the MBS spectrum is then reconstructed. A two-dimensional iterative grid refinement algorithm is also described to further improve the estimation accuracy in the presence of noise. Identifiability issues are addressed and the conditions for unique identifiability are discussed. Furthermore, by assuming a two dimensional uniform array the advantages of the proposed approach in terms of identifiability is also provided. We further show that an \(M \ge N+1\) sensors and an overall sampling rate of at least \(2(N+1)B\) would be sufficient to achieve SBR and DOA estimation of an MBS comprising of N disjoint bands each of maximal bandwidth B. Numerical simulations are finally presented which verifies the validity of the proposed approach and compares the performance against appropriate bounds.     

MUSIC-like estimation of direction of arrival for noncircular sources

This paper examines the asymptotic performance of MUSIC-like algorithms for estimating directions of arrival (DOA) of narrowband complex noncircular sources. Using closed-form expressions of the covariance of the asymptotic distribution of different projection matrices, it provides a unifying framework for investigating the asymptotic performance of arbitrary subspace-based algorithms valid for Gaussian or non-Gaussian and complex circular or noncircular sources. We also derive different robustness properties from the asymptotic covariance of the estimated DOA given by such algorithms. These results are successively applied to four algorithms: to two attractive MUSIC-like algorithms previously introduced in the literature, to an extension of these algorithms, and to an optimally weighted MUSIC algorithm proposed in this paper. Numerical examples illustrate the performance of the studied algorithms compared to the asymptotically minimum variance (AMV) algorithms introduced as benchmarks.     

High-precision estimation of the time of arrival of radio signals

An interpolation algorithm of high-precision estimation of the time of arrival of radio signals has been proposed. Analytical expressions were obtained for specifications of the algorithm that were corroborated by the results of computer simulation. It was shown that with due regard for the complexity adequate to a receiver of the maximum likelihood estimation of the temporal position with quantized output, the proposed interpolation algorithm ensures the noise immunity close to potentially attainable. The performed analysis makes it possible to select parameters of the estimation algorithm in a reasonable way depending on the requirements to its accuracy and complexity of realization.     

Time delays and angles of arrival estimation using known signals

Channel estimation in a multipath mobile communication system is addressed in this paper, and a novel approach based on the linear prediction in frequency domain and the singular value decomposition technique is presented for joint estimation of the angles of arrival and the time delays of multiple reflections of a known signal. Simulation results illustrating the performance of the proposed algorithm are included, and the results show that the proposed method is close in accuracy when compared to the iterative maximum-likelihood method. However, when the two methods are compared in computational complexity, it is demonstrated that the proposed method reduces the complexity to nearly half of that of the maximum-likelihood method. The Cramer–Rao bounds are computed for comparison.     

Design of uncorrelated signals

By considering the identification of a multi-input single-output process, it is shown that it is not possible to design uncorrelated sequences (of the same period) with autocorrelation functions of delta-function form This result is of importance not only in process identification, but also in the field of communication, where much effort has gone into trying to design uncorrelated sequences.     

一种分布式目标波达方向估计方法

本文提出一种在多径情况下,利用广义阵列流形的特点,通过求解矩阵的广义特征值分解,不用进行谱峰搜索或多维参数搜索,直接由广义特征值得到分布式目标波达方向（DOA）估计的方法。该方法的估计性能对信号源的分布特性优于MUSIC方法。     

UN-MUSIC and UN-CLE: an application of generalized correlationanalysis to the estimation of the direction of arrival of signals inunknown correlated noise

A new approach is proposed for the consistent estimation of the directions of arrival (DOA) of signals in an unknown spatially-correlated noise environment. The signal and noise model used is based on the assumption that the data are received by two arrays well separated so that their noise outputs are uncorrelated. The generalized correlation decomposition of the cross-correlation matrix between the two arrays is then introduced. Of particular interest is the canonical correlation decomposition. The analysis of the generalized correlation leads to various interesting geometric and asymptotic properties of the eigenspace structure. Two algorithms, UN-MUSIC and UN-CLE, are developed to estimate the DOA of signals in unknown spatially correlated noise based on the utilization of these properties. Computer simulations show that these methods are superior in performance compared to conventional methods. Furthermore, it is demonstrated that the new methods are equally effective even when only one sensor array is employed     

Two-dimensional angle-of-arrival estimation in the presence offinite distance sources

When sources are a finite distance away from an array system, most high-resolution bearing estimation techniques exhibit unsatisfactory performance due to the invalidity of the planar wavefront assumption. This problem has been addressed by a far-field approximation (FFA) method based on a preprocessing scheme. By exploiting favorable characteristics of a uniform linear array, the method constructs a FFA covariance matrix, which is Toeplitz and approximates to the far-field data covariance matrix, from the observed data covariance matrix. Then eigenstructure methods can be applied to perform bearing estimation without revising the planar wavefront assumption. This method is extended to the problem of two-dimensional angle-of-arrival (AOA) estimation using a uniform planar array in the presence of finite distance sources. A procedure is derived for reconstructing the 2-D FFA covariance matrix with block-Toeplitz structure. Using the 2-D FFA covariance matrix, eigenstructure methods can be applied in conjunction with a 2-D AOA search to solve the problem. Simulation results confirm the theoretical work