Direction of arrival estimation using adaptive directional time-frequency distributions

Time-frequency distributions (TFDs) allow direction of arrival (DOA) estimation algorithms to be used in scenarios when the total number of sources are more than the number of sensors. The performance of such time–frequency (t–f) based DOA estimation algorithms depends on the resolution of the underlying TFD as a higher resolution TFD leads to better separation of sources in the t–f domain. This paper presents a novel DOA estimation algorithm that uses the adaptive directional t–f distribution (ADTFD) for the analysis of close signal components. The ADTFD optimizes the direction of kernel at each point in the t–f domain to obtain a clear t–f representation, which is then exploited for DOA estimation. Moreover, the proposed methodology can also be applied for DOA estimation of sparse signals. Experimental results indicate that the proposed DOA algorithm based on the ADTFD outperforms other fixed and adaptive kernel based DOA algorithms.     

多径传播条件下宽带线性调频信号波达方向估计方法

针对信号出现多径传播情况时现有宽带信号波达方向（direction of arrival, DOA）估计方法性能下降的问题,提出了一种多径传播条件下宽带线性调频（chirp）信号波达方向估计方法,该方法将导向有效投影（steered effective projection, STEP）技术与宽带线性调频信号的时频特性相结合,对具有不同时频特性的信号分量进行分离,逐个处理,并以时频分布矩阵代替传统的协方差矩阵,从而构造有效噪声子空间,实现时域角度估计。本方法无需进行信号聚焦操作,因此理论上不受聚焦误差的影响。仿真结果验证了所提方法的有效性。      

相干分布式目标一维波达方向估计方法

在相干分布式目标波达方向估计研究中,角信号分布函数一般具有共轭对称性.在角分布函数的数学形式未知,或角分布函数形式不同的信号源同时存在的情况下,本文提出了搜索极小最小特征值(噪声子空间)或极大最大特征值(信号子空间)的分布式目标一维波达方向估计方法,并分析了分布式目标波达方向估计的模糊性问题.     

时频干涉仪到达角估计性能分析

传统干涉仪测向是对单个脉冲信号测向的，对于多信号没有分辨能力，对于线性调频等时变频率信号也不能直接应用。本文提出了一种时频干涉仪算法以实现对宽带线性调频信号的到达角（DOA）估计；同时该算法可实现多信号分辨；讨论了通道误差对算法性能的影响；分析表明，通道增益不一致不会造成DOA估计错误，而通道时延的不一致将造成DOA估计错误；给出了通道时延误差校正算法，通过校正可实现DOA的正确估计；计算机仿真结果证实了分析的正确性。     

A CLOSED-FORM WIDEBAND DIRECTION-OF-ARRIVAL ESTIMATION WITH CHIRPLET-BASED ADAPTIVE SIGNAL DECOMPOSITION ALGORITHM

The Direction-Of-Arrival (DOA) estimation with Coherent Signal Subspace (CSS)is not easy to cxtend from narrowband to wideband case.Time-frequency analysis is a powerfultechnique to deal with time-variant or non-stationary signal. Its combination with CSS exploresa new field in signal processing, especially the wideband DOA estimation. The chirp function isone of the most fundamental functions in nature. Many nature events can be modeled as chirpletfunction, such as radar system or scismic exploring.Hence,the chirplet-based signal decomposi-     

Adaptive Beamforming and Recursive DOA Estimation Using Frequency-Invariant Uniform Concentric Spherical Arrays

This paper proposes recursive adaptive beamforming and broadband 2-D direction-of-arrival (DOA) estimation algorithms for uniform concentric spherical arrays (UCSAs) having nearly frequency-invariant (FI) characteristics. The basic principle of the FI-UCSA is to transform the received signals to the phase mode and remove the frequency dependency of individual phase modes through a digital beamforming network. Hence, the far-field pattern of the array is determined by a set of weights. Thanks to the FI characteristic, traditional narrowband adaptive beamforming algorithms such as minimum variance beamforming and the generalized sidelobe canceller method can be applied to the FI-UCSA. Simulation results show that the proposed adaptive FI-UCSA beamformer achieves a lower steady-state error and converges faster than the conventional tapped-delay line approach while requiring fewer adaptive coefficients. A new broadband 2-D DOA estimation algorithm using ESPRIT techniques for FI-UCSA is proposed to recursively estimate the DOAs of the moving targets. Simulation results show that the proposed DOA estimation algorithm achieves a satisfactory performance for slowly varying sources at low arithmetic complexity.      

Incoherent DOA Estimation in Uniform Antenna Arrays with Inordinate Spacing Using a Subband Hopping Approach

Given a certain transmission frequency, the Shannon spatial sampling limit defines an upper bound for the antenna element spacing. Beyond this bound, the exceeded ambiguity avoids correct estimation of the signal parameters (i.e., array manifold crossing). In this survey, the problem of simultaneous signal and direction-of-arrival (DOA) estimation of broadband sources is addressed when the element spacing of a uniform array antenna (uniform linear array (ULA)) is inordinate. It is illustrated that one can resolve the aliasing ambiguity by utilizing the inherent frequency diversity of the broadband sources. An algorithm based on maximum likelihood estimation (MLE) has been developed to estimate the transmitted data signal and the DOA of each source. Through confirmatory simulation, it is shown that the performance gain of the proposed setup is potentially significant, specifically under a low signal-to-noise ratio (SNR) and when the transmitters are closely spaced. This relaxes the stringent maximum element-spacing constraint of ULAs pertinent to the upper-bound frequency of transmission and suggests that the element spacing—which in practical applications results in detrimental element coupling—can be conveniently increased, in particular under wide transmission spectrum and low SNR (e.g., license-free communication). A method similar to the frequency hopping approach (i.e., subband hopping) is utilized for the problem of source associations and identification. In the sequel, a suboptimal subspace-based algorithm is proposed and its performance is investigated. An approximate expression for the estimation error has also been developed to gauge the behavior of the proposed setup.     

基于具有时序结构的稀疏贝叶斯学习的水声目标 DOA 估计研究

现有的基于CS-MMV（Compressed Sensing-Multiple Measurement Vectors）模型的DOA估计一般都假定信号源为独立同分布（ i．i．d）,算法建立在信号的空间结构上进行分析,而当处理具有时序结构的源信号时表现出性能和鲁棒性差的问题,为此该文提出一种具有时序结构的稀疏贝叶斯学习的DOA算法,该方法通过建立一阶自回归过程（ AR）来描述具有时序结构的水声信号,将信号源的时间结构特性充分应用到DOA估计模型中,然后采用针对多测量矢量的稀疏贝叶斯学习（ Muti-vectors Sparse Bayesian Learning ）算法重构信号空间谱,建立多重测量向量中恢复未知稀疏源的信号的CS（ Compressed Sensing ）模型,最终完成DOA估计．仿真结果表明该方法相对于传统的算法具有更高的空间分辨率和估计精度的特点,且抗干扰能力强．     

基于互Wigner-Ville分布的到达角估计

研究了对线性调频信号的到达角估计;提出了基于互Wigner-Ville分布(XWVD)估计信号到达角的方法.通过时频分布,在时频面上进行信号预分选;根据XWVD时频脊点上的相位获得信号时延,从而获得信号到达角.该算法可实现多信号分辨,也可实现对时变频率信号的到达角估计.计算机仿真结果证实了该算法的有效性.     

一种新的DOA估计方法

本文提出了一种用天线阵来进行多个人射平面波的DOA(direction of arrival)估计方法.这种方法可以解决以往像MUSIC、ESPRIT等算法信号数不能超过阵元数的问题.这种方法计算量少、精度高、可适用于任意几何形状天线阵.同时得到信号频率估计,在平面阵中可得到自动成对的2维角估计.并且借助于相应技术,可...     

Double-constraint flexible tree search-based orthogonal matching pursuit for DOA estimation using dynamic sensor arrays

Greedy algorithms have leveraged sparse signal models for parameter estimation purposes in applications including bearing estimation and direction-of-arrival (DOA) estimation. A dictionary whose elements correspond to observations for a sampling of the angle space is used for sparse approximation of the received signals; the resulting sparse coefficient vector’s support identifies the DOA estimates. Increasing the angle space sampling resolution provides better sparse approximations for arbitrary observations, while the resulting high dictionary coherence hampers the performance of standard sparse approximation, preventing accurate DOA estimation. To alleviate this shortcoming, in the each iteration, we use the structured sparsity model that keeps high coherent and close spacing dictionary elements. Besides, the proposed approach allows exploitation of the array orientation diversity (achievable via array dynamics) in the compressive sensing framework to address challenging array signal processing problems such as left-right ambiguity and poor estimation performance. And the simulation results show that our proposed algorithm can offer significantly improved performance in single-snapshot scenario with multiple sources.     

Chirp parameter estimation from a sample covariance matrix

This paper considers the problem of estimating the bandwidth and the center frequency of a linear chirp signal. The nonstationarity property of chirp signals implies that the signal has high rank and reduces the applicability of subspace-based algorithms significantly. However, the special structure of the sample covariance matrix invites the use of regular frequency estimation algorithms. Herein, we show how subspace-type algorithms may be modified to provide accurate signal parameter estimates for linear chirp signals at reasonable complexity. The root-MUSIC algorithm is used as an example     

空间非平稳噪声下的宽带DOA估计算法

投影子空间正交性测试(TOPS)法是利用子空间的正交性实现宽带信号DOA估计,而在空间非平稳噪声环境下子空间的正交性条件不再满足,尤其是在低信噪比或低快拍条件下子空间估计将出现较大误差,TOPS算法性能将急剧下降。针对该问题,提出了一种空间非平稳噪声下宽带DOA估计算法。该算法首先通过构造特殊对角矩阵将噪声从数据协方差矩阵中剔除,从而克服非平稳噪声对DOA估计的影响;然后利用平方TOPS法实现宽带信号DOA估计,消除了传统TOPS算法中的伪峰。该算法适用于空间非平稳噪声背景及低信噪比环境,提高了对角度相近目标的分辨性能;仿真实验表明了该算法的有效性。     

线性调频信号时/频差估计算法

提出了一种新的快速估计线性调频信号时/频差的算法.该算法将抽取的自模糊函数与Radon变换结合估计线性调频信号的调频率, 通过分数阶傅里叶变换估计出模糊函数脊线与频率轴交点位置, 应用解调频沿脊线搜索模糊函数峰值.对于接收信号中存在多分量的情况, 根据其模糊函数脊线位置的不同, 该算法能够分辨各分量信号, 并分别精确估计出各分量的时/频差.由于只需一维搜索模糊函数峰值, 并可用快速傅里叶变换实现, 该算法大大减少了运算量.仿真实验表明, 随着信噪比的提高, 该算法估计的时/频差均方误差逐渐逼近克拉美-罗下界.     

Resolving manifold ambiguities in direction-of-arrival estimationfor nonuniform linear antenna arrays

This paper addresses the problem of ambiguities in direction of arrival (DOA) estimation for nonuniform (sparse) linear arrays. Usually, DOA estimation ambiguities are associated with linear dependence among the points on the antenna array manifold, that is, the steering vectors degenerate so that each may be expressed as a linear combination of the others. Most nonuniform array geometries, including the so-called “minimum redundancy” arrays, admit such manifold ambiguities. While the standard subspace algorithms such as MUSIC fail to provide unambiguous DOA estimates under these conditions, we demonstrate that this failure does not necessarily imply that consistent and asymptotically effective DOA estimates do not exist. We demonstrate that in most cases involving uncorrelated Gaussian sources, manifold ambiguity does not necessarily imply nonidentifiability; most importantly, we introduce algorithms designed to resolve manifold ambiguity. We also show that for situations where the number of sources exceeds the number of array sensors, a new class of locally nonidentifiable scenario exists     

Tracking of unknown nonstationary chirp signals using unsupervisedclustering in the Wigner distribution space

This paper is concerned with the problems of (1) detecting the presence of one or more FM chirp signals embedded in noise, and (2) tracking or estimating the unknown, time-varying instantaneous frequency of each chirp component. No prior knowledge is assumed about the number of chirp signals present, the parameters of each chirp, or how the parameters change with time. A detection/estimation algorithm is proposed that uses the Wigner distribution transform to find the best piecewise cubic approximation to each chirp's phase function. The first step of the WD based algorithm consists of properly thresholding the WD of the received signal to produce contours in the time-frequency plane that approximate the instantaneous frequency of each chirp component. These contours can then be approximated as generalized lines in the (ω, t, t²) space. The number of chirp signals (or equivalently, generalized lines) present is determined using maximum likelihood segmentation. Minimum mean square estimation techniques are used to estimate the unknown phase parameters of each chirp component. The authors demonstrate that for the cases of (i) nonoverlapping linear or nonlinear FM chirp signals embedded in noise or (ii) overlapping linear FM chirp signals embedded in noise, the approach is very robust, highly reliable, and can operate efficiently in low signal-to-noise environments where it is hard for even trained operators to detect the presence of chirps while looking at the WD plots of the overall signal. For multicomponent signals, the proposed technique is able to suppress noise as well as the troublesome cross WD components that arise due to the bilinear nature of the WD     

采用单次快拍数据实现相干信号DOA估计

该文针对均匀线性阵列的相干信号DOA估计问题,提出了一种基于单次快拍数据的解相干算法,该算法直接利用快拍数据构造一个Toeplitz矩阵,经理论推导,该Toeplitz矩阵的秩不受信号的相干性影响,仅和入射信号的个数有关,因此对该矩阵进行特征值分解可得到正确的信号子空间和噪声子空间,结合MUSIC,ESPRIT等子空间算法,即可实现对相干信号的DOA估计。数值仿真验证了该算法的有效性。     

2-D SPATIAL-SPECTRUM ESTIMATION FORWIDE-BAND SOURCES BASED ON INTERPOLATED CIRCULAR ARRAYS

In array signal processing, 2-D spatial-spectrum estimation is required to determine DOA of multiple signals. The circular array of sensors is found to possess several nice properties for DOA estimation of wide-band sources. C. U. Padmini, et al.(1994) had suggested that the frequency-direction ambiguity in azimuth estimation of wide-baud signals received by a uniform linear array (ULA) can be avoided by using a circular array, even without the use of any delay elements. In 2-D spatial-spectrum estimation for wide-band signals, the authors find that it is impossible to avoid the ambiguity in source frequency-elevation angle pairs using a circular array. In this paper, interpolated circular arrays are used to perform 2-D spatial-spectrum estimation for wide-band sources. In the estimation, a large aperture circular array (Υ>λmin/2) is found to possess superior resolution capability and robustness.     

基于插值圓阵的宽带信号二维空间谱估计

在阵列信号处理中,确定信号的波达方向(DOA)需要估计信号的二维(2-D)空间谱。C。Usha Padmini等人(1994)已证明,圆阵用于估计宽带信号的DOA时具有许多好的特性。尤其是在基于圆阵的宽带信号子空间一维DOA估计中,即使不用延迟抽头也不会出现频率-方向模糊。在估计宽带信号的2-D空间谱时,我们发现用不带延迟抽头的圆阵会出现频率-仰角模糊。本文提出了一种用插值圆阵估计宽带信号2-D空间谱的新方法。在估计中,采用大孔径的圆阵(rmin/2)能获得更好的分辨性能和估计稳健性。     

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。