Low-Complexity Estimation of the Nominal Azimuth and Elevation for Incoherently Distributed Sources

This paper develops a new technique for estimating the two-dimensional direction-of-arrivals (DOAs) of incoherently distributed (ID) sources, which can estimate effectively the nominal azimuth and nominal elevation of multiple ID sources at the cost of less computational complexity. Using a pair of parallel uniform linear arrays (ULAs), a new approach for 2D DOA estimation of multiple ID sources is proposed. The proposed method firstly estimates the nominal elevation by the modified TLS-ESPRIT method, which is based on the approximate rotational invariance property with respect to the nominal elevation between two closely parallel ULAs. And then with the help of the nominal elevation estimates, the nominal azimuth is estimated by one-dimensional searching. Without multi-dimensional searching, the proposed method has significantly reduced the computational cost compared with the existing methods. Simulation results indicate that the proposed method can exhibit a good performance and be applied to the multisource scenario where different sources may have different angular distribution shapes.     

相干分布源DOA与角度扩展估计求根算法

论文提出了一种具有低复杂度的相干分布源波达方向和角度扩展估计算法。该算法将点源模型中的求根MUSIC算法推广应用至分布源模型。利用空间频率下的相干分布源广义方向矢量可以表示成参数去耦形式的结构特点,并根据相干分布源的角信号密度函数,构造参数估计的多项式求根形式,然后通过交替迭代的求根方法得到分布源的中心波达方向和角度扩展的估计值。该算法参数估计性能与DSPE算法相当,其计算复杂度要远小于DSPE算法,并且适用于不同分布类型的相干分布源同时存在的情况。计算机仿真验证了算法的性能。      

Simplified Estimation of 2D DOA for Coherently Distributed Sources

In mobile communications, local scattering in the vicinity of the mobile results in angular spreading as seen from a base station antenna array. In this paper, we consider the problem of estimating the two-dimensional (azimuth and elevation) direction-of-arrival (DOA) parameters of spatially distributed sources. Based on double parallel uniform linear arrays (ULAs), a simplified method without spectrum-peak searching is proposed for the 2D DOA estimation of multiple coherently distributed (CD) sources. The proposed method firstly obtains two approximate rotational invariance relations with respect to the nominal DOAs of CD sources by using one-order Taylor approximation to the generalized steering vectors (GSVs) of two pairs of shifted subarrays. And then a new ESPRIT-based method is utilized to estimate the nominal azimuth DOA and nominal elevation DOA. In addition, a simple parameter matching approach is also given. Compared with the conventional methods, our method has significantly reduced the computational cost and can sustain the estimation performance within a tolerable level. Moreover, our method is a blind estimator without any prior knowledge about angular distribution shape. Numerical examples illustrate the performance of the method.     

A Fast Decoupled Nominal 2-D Direction-of-Arrival Estimation for Coherently Distributed Source

In this paper, we consider the problem of the nominal 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation for coherently distributed source. This new approach is based on the rotation matrices of three parallel uniform linear arrays as deduced, which has decoupled the nominal 2-D DOA from those of angular spreads. The estimator makes use of the eigenvalue decomposition to beamspace data to estimate the nominal elevation DOA. And then using a new cross-correlation matrix, the nominal azimuth DOA estimates are decoupled from the elevation estimates and can be obtained with no searching. The proposed algorithm has lower computational complexity particularly when the radio of array size to the number of source is large, at the expense of negligible performance loss. Simulation results verify the effectiveness of the proposed method.     

基于稀疏表示的相干分布式非圆信号的参数估计

基于稀疏表示技术,该文提出一种相干分布式非圆信号的参数估计新方法。该方法将信号的非圆特性引入分布式信源模型,充分利用非圆信号的特性,联合阵列输出协方差矩阵和椭圆协方差矩阵,并将其矢量化之后表示在受制于稀疏限制的过完备字典上;然后将DOA估计转化为一个稀疏重构问题,能够一次性求解出中心DOA和角度扩展。仿真结果表明,该方法适用于各种非圆率的非圆信号,具有较好的信噪比性能和分辨力,所提出的方法还能对圆和非圆信号同时存在的情况进行有效估计。     

Decoupled estimation of DOA and angular spread for a spatiallydistributed source

In mobile communications, local scattering in the vicinity of the mobile results in angular spreading as seen from a base station antenna array. In this paper, we consider the problem of estimating the parameters [direction-of-arrival (DOA) and angular spread] of a spatially distributed source, using a uniform linear array (ULA). A two-step procedure enabling decoupling the estimation of DOA from that of the angular spread is proposed. This method combines a covariance matching algorithm with the use of the extended invariance principle (EXIP). More exactly, the first step makes use of an unstructured model for the part of the covariance matrix that depends on the angular spread. Then, the solution is refined by invoking EXIP. Instead of a 2-D search, the proposed scheme requires two successive 1-D searches. Additionally, the DOA estimate is robust to mismodeling the spatial distribution of the scatterers. A statistical analysis is carried out, and a formula for the asymptotic variance of the estimates is derived. Numerical examples illustrate the performance of the method     

A Method for the Direction-of-Arrival Estimation of Incoherently Distributed Sources

We consider the problem of estimating the nominal direction of arrival (DOA) of an incoherently distributed source. This problem is encountered due to the presence of local scatterers in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the spatial covariance matrix has full rank for an incoherently distributed source, the performance of most high-resolution DOA estimation algorithms conceived under coherently distributed sources, as well as point source models, degrades when scattering is present. In addition, several DOA estimation techniques devised under a distributed source model require a high-dimensional nonlinear optimization problem. In this paper, we propose a novel method based on the conventional beamforming approach, which estimates the nominal DOA from a spatial maximum peak of the output power. The proposed method is computationally more attractive than the maximum likelihood (ML) estimator, although the performance degrades in comparison with the ML estimator, whose asymptotic performance is equivalent to the Cramer–Rao bound (CRB). We derive and compare the asymptotic performances of the proposed method and the redundancy-averaged covariance matching (RACM) method in the single-source case. The simulation results illustrate that the asymptotic performance of the proposed method is better than that of the RACM method.      

Computationally efficient DOA estimation for coprime linear array: a successive signal subspace fitting algorithm

ABSTRACT

In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm.     

利用盲源分离算法实现DOA估计

提出一种基于快速盲源分离算法实现波达方向（DOA）估计的方法。构造了具有对角化结构的相关矩阵组,引入解盲源分离问题的联合对角化代价函数,采用一种快速的复数域乘性迭代算法求解代价函数,得到混迭矩阵逆的估计,进而实现DOA估计。与同类算法相比,该算法具有更广的适用性和更精确的DOA估计性能。仿真实验结果验证了算法的快速收敛性和优越的估计性能。     

DOA Tracking for Coherently Distributed Sources with Particle Filter

In this work, an efficient direction of arrival (DOA) tracking method for coherently distributed sources is proposed. The central DOA and angle spread are estimated by the proposed method with a particle filter at each snapshot. The spectrum calculated by the distributed source parameter estimator (DSPE) is used as a pseudo-likelihood function for particle updating, which enables the particle filter to process sensor signals directly without estimating the source amplitude. The proposed method is compared with the total least square estimating signal parameter via rotational invariance technique and the fast implementation of a power iteration subspace updating (FAPI-TLS-ESPRIT). The proposed method is verified by Monte Carlo simulations, and simulation results show that the proposed method can achieve an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method. In addition, the proposed method can simultaneously estimate the central DOA and angle spread.

     

共形阵列天线信源方位与极化状态的联合估计算法

 针对共形天线阵列流形的特点,提出了共形阵列天线信源方位与极化状态的联合估计算法.算法的方位估计不需要信源极化状态的任何信息,估计精度高、分辨力强,在完成信源方位估计的同时,还可以精确的估计出信源的极化状态,不需要参数配对,实现了共形阵列天线信源方位与极化状态的联合估计.算法仅需要二维参数搜索,计算量比已有的联合估计算法小,且适用于任意共形载体,具有广泛的应用范围.对算法参数估计的理论性能进行了分析推导,给出了参数估计的CRB(Cramer-Rao Bound),并通过Monte-Carlo仿真实验验证了理论分析的正确性与算法的有效性.     

脉冲噪声下基于相关熵的相干分布源DOA估计新方法

针对复杂电磁环境下被动无线监测定位问题,该文提出广义相关熵的概念,推导了广义相关熵的性质,用以抑制阵列输出信号中的脉冲噪声。为了实现脉冲噪声环境下相干分布源中心DOA和扩散角的联合估计,提出基于广义相关熵的DOA估计新方法,并证明了该方法的有界性。为进一步提升算法的鲁棒性,推导了一种仅依赖阵列输出信号的自适应核函数。仿真结果表明,该算法能够实现脉冲噪声环境下相干分布源参数的联合估计,相比已有算法,具有更高的估计精度和鲁棒性。     

非相干分布源DOA和角度扩展去耦估计方法

该文提出了一种新的非相干分布源的DOA和角度扩展估计算法。根据空间频率模型下的非相干分布源协方差矩阵的结构特点,可将协方差矩阵分离成两个分别由相位信息和幅度信息重建的矩阵。对矩阵的各主次对角线元素均进行平滑,可得到包含相位信息和幅度信息的平滑向量。利用最小均方拟合方法,可从相位信息中估计得到方位角;估计得到的方位角信息代入到幅度信息中即可获得角度扩展信息的估计,实现非相干分布源的DOA和角度扩展去耦估计。计算机仿真验证了算法的性能。     

用菲涅尔区板天线实现来波方向的鲁棒性估计

针对蜂窝移动通信局域散射电波传播的空间分布源模型,研究了应用菲涅尔区相位修正平面聚焦结构和馈源阵列的组合形成的一种新型多波束自适应天线．基于广义MUSIC算法及极大似然估计算法．分别实现相干及非相干的空间分布式源信号来波方向及其角度扩展参数的鲁棒性估计问题．数值仿真计算结果表明．在这一无线传播环境中,基于菲涅尔区相位修正平面聚焦结构在减小相关参数估计的RMS误差等方面,其性能优于传统的均匀直线阵列模型．因此基于该新型自适应天线．可以实现来波方向的鲁棒性估计。     

多径条件下均匀线阵DOA估计及互耦误差自校正

针对多径信道环境下存在互耦误差的均匀线阵,提出了一种联合波达方向估计及互耦 误差自校正算法。在不改变阵列互耦误差的条件下,首先利用虚拟阵列平移预处理方法,将 相干信源协方差矩阵恢复到满秩。进而利用互耦误差的对称Toeplitz特性,基于子空间原理 构造一代阶函数,采用秩损的方法得到互耦误差条件下的DOA估计及阵列互耦误差。数值仿 真结果表明,该算法具有良好的DOA估计性能与互耦误差自校正性能。     

基于协方差矩阵重构的离网格DOA估计方法

针对稀疏表示模型中网格失配导致波达方向角(DOA)估计存在较大估计误差的问题,该文提出一种基于协方差矩阵重构的离网格(Off-Grid)DOA估计方法(OGCMR)。首先,将DOA与网格点之间偏移量包含进所构建接收数据空域离散稀疏表示模型;而后基于重构信号协方差矩阵建立关于DOA估计的稀疏表示凸优化问题;再构建采样协方差矩阵估计误差凸模型,并将此凸集显式包含进稀疏表示模型以改善稀疏信号重构性能;最后采用交替迭代方法求解所得联合优化问题以获得网格偏移参数及离网格DOA估计。数值仿真表明,与传统多重信号分类(MUSIC)、L1-SVD及基于稀疏和低秩恢复的稳健MVDR (SLRD-RMVDR)等估计算法相比,所提算法具有较好的角度分辨力以及较高的DOA估计精度。     

利用矩阵滤波技术估计信号二维波达方向

现有的基于子空间的波达方向估计算法都要求不相关信号源的数目小于阵元数目,但当信号源数目很多时,算法失效.为此,提出了一种具有过载估计能力的二维波达方向估计方法,先利用矩阵滤波技术接收数据进行预处理,使信号子空间限定在一个较小的区域内,把区域外的信号抑制掉,从而减小了有效的信号源数目,然后可以采用现有算法进一步估计二维波达方向.矩阵滤波器的设计过程也可以归结为一个最小二乘问题,效率很高.计算机仿真验证了算法的有效性.     

三正交分离式极化敏感阵列的波达方向估计

该文研究了分离式极化敏感阵列(SS-PSA)的多目标波达方向(DOA)估计问题。首先建立三正交分离式极化敏感均匀稀疏阵列的信号模型;接着利用阵列的旋转不变性采用ESPRIT算法计算出多值模糊DOA估计;利用该模糊DOA估计值对分离式极化天线进行空间相移的补偿,然后利用共点式极化天线之间的多维角度结构求出对应的虚拟DOA估计;最后通过提取多值模糊估计值和对应虚拟估计值之差的最小范数得到多目标的DOA估计。该文所提阵列采用分离式极化天线代替传统共点式极化天线,降低了阵列的互耦影响;采用稀疏阵列结构有效地扩展了阵列的物理孔径,大大提高了DOA估计精度。计算机仿真结果证明了该算法对分离式极化天线DOA估计的有效性和高精度性。     

A Simplified Estimator for Tridimensional Localization of Single Incoherently Distributed Source

In this paper, a simplified parametric estimator for tridimensional localization of single incoherently distributed (ID) source with small angular spread is proposed. The proposed estimator firstly obtains two sample covariance matrices using the observation data of a L-shape array. And then the secondary diagonal elements of the sample covariance matrices are used to estimate the nominal azimuth and elevation of single ID source. Our technique does not involve any spectrum searching and the eigen-decomposition of the sample covariance matrix, and thus the computational burden has been significantly alleviated. Moreover, it is also a blind estimator which doesn’t require any prior knowledge about the angular power density of the ID source. Numerical examples illustrate the performance of the proposed estimator.     

Acoustic vector-sensor array processing

A method is presented for localizing acoustic sources using an array of sensors, the output of each being a vector consisting of the acoustic pressure and acoustic particle velocity. The authors derive a compact expression for the Cramer-Rao bound (CRB) on the estimation errors of the source direction-of-arrival (DOA) parameters in the multi-source multi-vector-sensor model. An explicit expression is found for the mean-square angular error (MSAE) bound for source localization with a single vector sensor. The authors present two simple algorithms for estimating the source DOA with this sensor, along with their statistical performance analyses