共形天线阵元位置误差校正的辅助阵元法

共形天线的安装和测量以及载体平台表面的变形和振动均会引起阵元位置误差,严重影响 共形天线的测向性能。通过设置４个方位未知的精确校正的辅助阵元,实现了共形天线大尺度三维 的阵元位置误差校正。给出了阵元位置误差条件下共形天线导向矢量的方位依赖的幅相误差等效 表示模型;基于子空间原理得到等效的方位依赖的幅相误差估计,进而得到共形天线阵元的三维位 置误差估计。该方法可以实现共形天线校正信源来波方位和阵元三维位置误差的联合但“去耦”估 计。计算机仿真结果验证了共形天线阵元位置误差校正的辅助阵元法的有效性     

Performance analysis of higher order ESPRIT for localization ofnear-field sources

Most existing array processing techniques for estimating the directions of arrival (DOAs) or signal copy rely heavily on the plane-wave assumption of far-field sources. When the sources are located relatively close to the array, these techniques may no longer perform satisfactorily. In this paper we present an asymptotic performance analysis of an ESPRIT-like method for passive localization of nearfield sources. The algorithm, which is based on fourth-order cumulants, is formulated for observations collected from a single uniformly spaced linear array. We examine the least-squares version of the algorithm and derive the expressions for the asymptotic variance of the estimated DOAs (relative to a reference sensor) and estimated ranges of the sources. We also derive an algorithm independent bound on the asymptotic variance of the estimated parameters. This bound can be used as a measure against the theoretically predicted algorithmic performance. Some insight into the achievable performance of this algorithm is obtained by numerical evaluation of the bound for several test cases of interest, and the results are compared with those obtained by numerical evaluation of the theoretically predicted performance. Monte Carlo simulations are used to verify the theoretical analysis     

2-D DOA Estimation in the Presence of Mutual Coupling

We present a 2-D direction of arrival (DOA) estimation algorithm in the presence of unknown mutual coupling for the uniform rectangular array (URA) based on the multiple signal classification (MUSIC) algorithm. By setting the sensors on the boundary of the URA as auxiliary sensors, it can accurately estimate the DOAs without any calibration sources or iterative operations. We prove that the effect of mutual coupling can be eliminated by the inherent mechanism of the proposed method. Twice search technique is used to reduce the computation of the 2-D spectrum search. Moreover, we provide a method to estimate the mutual coupling coefficients after getting the DOA estimates. Simulation results confirm the effectiveness of the proposed algorithm.     

A Practical Simple Geometry and Gain/Phase Calibration Technique for Antenna Array Processing

A calibration technique is proposed in this paper for an arbitrary array. This technique estimates the array sensor gain/phase and geometry with a set of simultaneous equations formed by using the MUSIC null spectrum property. Note that the technique does not use iterative calculation in estimating the array parameters and hence it has no convergent problem; however, it requires that $n$ directions of arrival (DOAs) of signal sources to be known to calibrate the array which is perturbed $n$-dimensionally. The efficacy of the method is demonstrated by means of simulations and on experimental data collected with an antenna array operating in high-frequency radio band.      

一种基于空域滤波的空间临近相干源角度估计方法

相干源常见于存在多径的场景,如何解相干历来是阵列信号处理领域亟待解决的难题之一,特别针对空间临近相干源,其角度估计精度尚有待提高。针对空间临近相干源该文提出一种基于空域滤波的角度估计方法。首先利用空域滤波技术将多个相干源分离,再对滤波分离后的各个信号分别进行角度估计,并通过对滤波器系数和相干源角度的迭代优化提高测角精度。针对非均匀线阵,该方法采用虚拟阵列技术扩展其适用范围。计算机仿真结果表明该方法的测角精度较现有方法更高,信噪比较高时其测角的均方根误差可达克拉美罗界,验证了该方法的有效性和在空间临近相干源场景的优越性。     

Method of solving ambiguity for sparse array via power estimation based on MUSIC algorithm

Sparse linear arrays provide better performance than the filled linear arrays in terms of direction estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. A method based on the Multiple Signal Classification (MUSIC) algorithm to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method consists of two steps. The first step is to obtain all the directions of arrivals (DOAs), including true and spurious DOAs, using traditional MUSIC. The second step is to estimate the power values of the all DOAs by substituting all the DOAs to a cost function. The well-known Davidson Fletcher Powell (DFP) and Broyden Fletcher Goldfarb Shanno (BFGS) algorithms are used to estimate the power values. The power values of spurious DOAs are very small or tend to zero compared with the values of the true DOAs. The true DOAs are then discriminated easily from the spurious DOAs with the power values. Simulation results demonstrate the effectiveness and the feasibility of the method.     

Particle filters for tracking an unknown number of sources

This paper addresses the application of sequential importance sampling (SIS) schemes to tracking directions of arrival (DOAs) of an unknown number of sources, using a passive array of sensors. This proposed technique has significant advantages in this application, including the ability to detect a changing number of signals at arbitrary times throughout the observation period and that the requirement for quasistationarity over a limited interval may be relaxed. We propose the use of a reversible jump Monte Carlo Markov chain (RJMCMC) step to enhance the statistical diversity of the particles. This step also enables us to introduce two novel moves that significantly enhance the performance of the algorithm when the DOA tracks cross. The superior performance of the method is demonstrated by examples of application of the particle filter to sequential tracking of the DOAs of an unknown and nonstationary number of sources and to a scenario where the targets cross. Our results are compared with the PASTd method.     

A Novel Online Mutual Coupling Compensation Algorithm for Uniform and Linear Arrays

In this paper, a novel online mutual coupling compensation algorithm especially tailored to uniform and linear arrays is presented. It is conceived to simultaneously compensate for mutual coupling and estimate the direction-of-arrivals (DOAs) of signals impinging on the array since the estimated calibration matrix can be embedded within any classical super-resolution direction-finding method. An alternating minimization procedure based on closed-form solutions is performed to estimate the mutual coupling matrix in the field of complex symmetric Toeplitz matrices. Unlike many existing array calibration methods, it requires neither the presence of calibration sources nor previous calibration information as initialization. Computer simulations show the effectiveness of the proposed technique and prove that the nice statistical properties of classical super-resolution DOA estimation algorithms can be restored despite the presence of mutual coupling     

Joint Channel and DOA Estimation for Multicarrier CDMA Uplink Transmissions

The use of antenna arrays in wireless communications makes it possible to estimate the directions of arrival (DOAs) of impinging waveforms. The latter can be exploited to enhance channel estimation accuracy or as an input for advanced mobile positioning systems. In this paper, we consider the uplink of a multicarrier code-division multiple-access (MC-CDMA) network in which the base station is endowed with multiple receiving antennas arranged in a uniform linear array. Transmission takes place over a multipath channel, and the goal is the joint estimation of the channel responses and DOAs of the uplink signals. In doing so, we follow a maximum-likelihood (ML) approach and assume that users transmit orthogonal training sequences to facilitate the task of separating the signals. This way, all unknown parameters are estimated independently of each other with affordable complexity. Theoretical analysis and computer simulations are used to assess the performance of the proposed scheme and make comparisons with existing alternatives.     

The effects of array calibration errors on DF-based signal copyperformance

This paper studies the effect of array calibration errors on the performance of various direction finding (DF) based signal copy algorithms. Unlike blind copy methods, this class of algorithms requires an estimate of the directions of arrival (DOAs) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is sufficiently long, the following algorithms are studied: classical beamforming, least squares, total least squares, linearly constrained minimum variance beamforming, and structured stochastic estimation. Expressions for the mean-square error of the signal estimates are derived as a function of the calibration errors for both the case where the DOAs are known precisely and for the case where the DOAs must be estimated     

A modified likelihood function approach to DOA estimation in thepresence of unknown spatially correlated Gaussian noise using a uniformlinear array

The problem of modified ML estimation of DOAs of multiple source signals incident on a uniform linear array (ULA) in the presence of unknown spatially correlated Gaussian noise is addressed here. Unlike previous work, the proposed method does not impose any structural constraints or parameterization of the signal and noise covariances. It is shown that the characterization suggested here provides a very convenient framework for obtaining an intuitively appealing estimate of the unknown noise covariance matrix via a suitable projection of the observed covariance matrix onto a subspace that is orthogonal complement of the so-called signal subspace. This leads to a formulation of an expression for a so-called modified likelihood function, which can be maximized to obtain the unknown DOAs. For the case of an arbitrary array geometry, this function has explicit dependence on the unknown noise covariance matrix. This explicit dependence can be avoided for the special case of a uniform linear array by using a simple polynomial characterization of the latter. A simple approximate version of this function is then developed that can be maximized via the-well-known IQML algorithm or its variants. An exact estimate based on the maximization of the modified likelihood function is obtained by using nonlinear optimization techniques where the approximate estimates are used for initialization. The proposed estimator is shown to outperform the MAP estimator of Reilly et al. (1992). Extensive simulations have been carried out to show the validity of the proposed algorithm and to compare it with some previous solutions     

WAVES: weighted average of signal subspaces for robust widebanddirection finding

Existing algorithms for wideband direction finding are mainly based on local approximations of the Gaussian log-likelihood around the true directions of arrival (DOAs), assuming negligible array calibration errors. Suboptimal and costly algorithms, such as classical or sequential beamforming, are required to initialize a local search that eventually furnishes DOA estimates. This multistage process may be nonrobust in the presence of even small errors in prior guesses about angles and number of sources generated by inherent limitations of the preprocessing and may lead to catastrophic errors in practical applications. A new approach to wideband direction finding is introduced and described. The proposed strategy combines a robust near-optimal data-adaptive statistic, called the weighted average of signal subspaces (WAVES), with an enhanced design of focusing matrices to ensure a statistically robust preprocessing of wideband data. The overall sensitivity of WAVES to various error sources, such as imperfect array focusing, is also reduced with respect to traditional CSSM algorithms, as demonstrated by extensive Monte Carlo simulations     

MSE-based regularization approach to direction estimation ofcoherent narrowband signals using linear prediction

This paper addresses the problem of directions of arrival (DOAs) estimation of coherent narrowband signals impinging on a uniform linear array (ULA) when the number of signals is unknown. By using an overdetermined linear prediction (LP) model with a subarray scheme, the DOAs of coherent signals can be estimated from the zeros of the corresponding prediction polynomial. Although the corrected least squares (CLS) technique can be used to improve the accuracy of the LP parameters estimated from the noisy array data, the inversion of the resulting matrix in the CLS estimation is ill-conditioned, and then, the CLS estimation becomes unstable. To combat this numerical instability, we introduce multiple regularization parameters into the CLS estimation and show that determining the number of coherent signals is closely related to the truncation of the eigenvalues. An analytical expression of the mean square error (MSE) of the estimated LP parameters is derived, and it is clarified that the number of signals can be determined by comparing the optimal regularization parameters with the corresponding eigenvalues. An iterative regularization algorithm is developed for estimating directions without any a priori knowledge, where the number of coherent signals and the noise variance are estimated from the noise-corrupted received data simultaneously     

Quaternion-MUSIC for vector-sensor array processing

This paper considers the problem of direction of arrival (DOA) and polarization parameters estimation in the case of multiple polarized sources impinging on a vector-sensor array. The quaternion model is used, and a data covariance model is proposed using quaternion formalism. A comparison between long vector orthogonality and quaternion vector orthogonality is also performed, and its implications for signal subspace estimation are discussed. Consequently, a MUSIC-like algorithm is presented, allowing estimation of wave's DOAs and polarization parameters. The algorithm is tested in numerical simulations, and performance analysis is conducted. When compared with other MUSIC-like algorithms for vector-sensor array, the newly proposed algorithm results in a reduction by half of memory requirements for representation of data covariance model and reduces the computational effort, for equivalent performance. This paper also illustrates a compact and elegant way of dealing with multicomponent complex-valued data.     

动目标多径回波的时延、到达角和多普勒频率联合估计

动目标回波的时延、到达角与多普勒频率等参数,都是定位与跟踪目标所需的重要参数。本文针对动目标多径回波的参数估计问题,利用照射信号的周期重复性建立了具有旋转不变结构的阵列接收信号模型,并将PRO-ESPRIT算法应用于该信号模型,从而利用模型中的旋转不变因子及其对应的特征向量估计得到各多普勒频率、时延和到达角参数。这一方法对阵列形式没有特殊要求;与已有的联合估计方法相比,多参数联合估计时无需高维搜索或迭代,估得的对应相同回波的信号参数自动配对;在照射信号未知及存在相同多普勒频率参数的条件下,仍具适用性。计算机仿真结果证实了该方法的有效性。     

On the Use of a Calibration Emitter for Source Localization in the Presence of Sensor Position Uncertainty

Sensor position uncertainty is known to degrade significantly the source localization accuracy. This paper investigates the use of a single calibration emitter, whose position is known to the sensor array, to reduce the loss in localization accuracy due to sensor position errors that are random. Using a Gaussian noise model, we first derive the CramÉr–Rao lower bound (CRLB) for a time difference of arrival (TDOA)-based source location estimate with the use of a calibration source. The differential calibration technique that is commonly used in Global Positioning System through the use of a calibration source to mitigate the inaccuracy in satellite ephemeris data is analyzed. The analysis indicates that differential calibration in most cases cannot reach the CRLB accuracy. The paper then proceeds to propose an algebraic closed-form solution for the source location estimate using both TDOA measurements from the unknown and the calibration source. The proposed algorithm is shown analytically, under high signal-to-noise ratio (SNR) and small sensor position noise, or under moderate level of SNR and sensor position noise together with distant unknown and calibration sources, to reach the CRLB accuracy. Simulations are used to corroborate and support the theoretical development.      

Direction-of-arrival estimates in the presence of wavelength, gain,and phase errors

Direction-of-arrival (DOA) estimation using an array of sensors relies on an accurate characterization of the array manifold. In the absence of characterization errors, established techniques like MUSIC can be shown to perform well both theoretically and in simulation. However, in the presence of unknown sensor and/or source characteristics, the performance of most methods degrades significantly. We consider the problem of estimating gain and phase errors of an array of sensors whose physical positions are known. Our algorithm assumes that the gain and phase characteristics of the sensors are independent of DOA and employs multiple calibration sources with known DOA's. It differs from other algorithms in that the signal wavelengths are unknown. A least-squares formulation of the problem is then shown to be NP-complete, implying that an efficient solution is unlikely to exist. An implicit, enumerative technique is used to obtain the exact solution. For the special case of collinear sensors, we further show that an inherent ambiguity in the model prevents exact phase characterization unless the wavelength of one calibration source is assumed known. A theorem is presented relating the error in DOA to the difference between the assumed and true wavelengths of this calibration source. Simulation results are presented for both noncollinear and collinear arrays     

A NEW ARRAY STRUCTURE FOR ESTIMATING 2-D DIRECTION-OF-ARRIVAL:Y-SHAPED ARRAY

This paper presents a new array structure for estimating two-dimensional (2-D) direction-of-arrivals (DOAs). The structure is called Y-shaped array, which has 10% better accuracy potential than the newly-developed L-shaped array. A great merit is its ability to estimate 2-D DOAs whichever directions the arriving signals come from, compared with L-shaped array whose performance depends on DOAs. Simulation results are given to demonstrate the performance of the new array.     

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。