线性极化敏感阵列的极化平滑算法及相干源参数估计

 研究了基于极化敏感阵列的相干信号参数估计问题。首先,提出一种利用均匀线性极化敏感阵列的新型极化平滑算法(ULAPSA),其能够处理更多相干信号的参数估计,且具有更小的计算量。然后,基于ULAPSA给出了波达方向（DOA）和极化参数估计的两种方法：第1种方法利用角度搜索得到信号的DOA估计,适应任何多分量的电磁矢量传感器阵列;第2种方法结合传播算子算法,无需奇异值分解和角度搜索,能够同时估计信号的DOA和极化参数。该方法适应完备的电磁矢量传感器、三偶极子或三磁环构成的矢量传感器。最后,通过仿真实验比较和分析了所提算法的性能。     

存在阵列模型误差情况下的宽带DOA估计

 针对工程应用中存在阵列模型误差的任意形状天线阵列的宽带波达方向（DOA）估计问题,提出一种基于信号分离的相关域宽带松弛（RELAX）算法。此算法利用具有记忆与遗忘特征的矩阵算子的投影机理对入射信号进行有效的分离,因此对一定范围内的阵列模型误差较之基于子空间理论的传统宽带测向算法具有较好的鲁棒性与良好的工程应用前景。分析并证明了此算法的信号分离机理及此算法对阵列模型误差稳健的原理。理论分析与仿真结果均表明存在阵列模型误差时此算法宽带DOA估计的有效性和稳健性。     

运动单阵元被动合成阵列波达方向估计

 提出了一种运动单阵元被动合成阵列波达方向(DOA)估计算法。该算法基于被动合成阵列(PSA)的概念,结合空间谱估计的思想构建了运动单阵元被动合成阵列模型,通过多次不同速度合成阵列过程实现对信号DOA的无模糊估计。通过对单次匀速合成阵列过程进行分析得到,在假设信号频率已知条件下,合成阵列算法能够达到与同孔径实阵列多重信号分类（MUSIC）算法相当的DOA估计性能。仿真验证了被动合成阵列与同孔径实阵列的渐近等效性及算法的有效性。     

基于迭代QR分解的DOA估计技术

提出了一种基于迭代QR分解的信源到达角(DOA)估计技术.DOA估计的子空间方法主要是通过估计信号协方差矩阵的信号子空间或者噪声子空间来求出信号的DOA参数.估计这些子空间通常需要大量的计算,采用ASIC实现时其成本会非常昂贵.本文采用迭代QR分解方法进行子空间分解,可以利用较少量的计算资源完成处理任务.仿真实验结果达到0.23毫弧度,说明该算法比较可靠有效.     

General direction-of-arrival estimation: a signal subspace approach

A high-resolution algorithm is presented for resolving multiple incoherent and coherent plane waves that are incident on an array of sensors. The incident sources can be a mixture of narrowband and broadband sources, and, the geometry of the array is unrestricted. The algorithm makes use of a fundamental property possessed by those eigenvectors of the array spectral density matrix that are associated with eigenvalues that are larger than the sensor noise level. Specifically, it is shown that these eigenvectors can each be represented as linear combinations of the steering vectors identifying the incident plane waves. This property is then used to solve the important special cases of incoherent sources incident on a general array and coherent sources incident on an equispaced linear array. Simulation results are presented to illustrate the high-resolution performance achieved with this approach relative to that obtained with MUSIC and spatial smoothed MUSIC in which the coherent-signal-subspace focusing method is used     

基于压缩感知的嵌套FDA 雷达距离- 角度联合估计

针对频率分集技术，与非均匀阵列、压缩感知理论相结合，实现目标距离- 角度的联合估计。通过 频率分集技术提高信号空间自由度，将距离参数引入导向矢量矩阵，实现距离- 角度参数的联合。并结合嵌套 阵列，显著提高雷达阵列孔径。该方法针对嵌套阵虚拟阵列流型矩阵的优化过程导致的秩亏缺问题，传统的空 间平滑算法会以牺牲部分阵列孔径为代价将单快拍问题转化为多快拍问题，采用正交匹配追踪算法可以实现单 快拍下的高精度参数估计，避免了空间平滑技术对阵列孔径的影响。并通过压缩感知理论的应用降低信号维度， 减小了计算复杂度。     

DOA estimation under unknown mutual coupling and multipath

We propose a new method for direction of arrival (DOA) estimation in the presence of multipath propagation and mutual coupling for a frequency hopping (FH) system. With the use of pilot symbols and assuming perfect time-frequency synchronization for a linear array, we take mutual coupling and multipath propagation into account, and derive a maximum likelihood (ML) estimator for both the mutual coupling matrix and DOA estimation. We then formulate an iterative alternating minimization (AM) algorithm for finding the mutual coupling and DOA parameters in an alternate manner. Simulation results illustrating the performance of the algorithm and comparison with the Cramer-Rao bound (CRB) are also presented.     

MIMO雷达中一种降维MUSIC的相干角度估计算法

研究单快拍下双基地多输入多输出（Multiple—InputMultiple-Output,MIMO）雷达中相干信源的离开角（Directionofdeparture,DOD）与到达角（directionofarrival,DOA）联合估计问题。利用单快拍下双基地MIMO雷达的接收信号构造一组Toeplitz矩阵,利用这组ToepIitz矩阵重构一个信号矩阵,提出一种基于降维多重信号分类（ReducedDimensionMultipleSignalClassification,RD-MUSIC）的DOD与DOA联合估计算法。提出的算法能够有效估计相干信源以及非相干信源的角度,实现角度的自动配对,并且角度估计性能远优于FBSS—ESPRIT算法以及ESPRIT-like算法。仿真结果验证了算法的有效性。     

一种稀疏阵列下的二维DOA估计方法

研究了稀疏阵列下二维波达方向（DOA）的估计问题,提出一种基于不动点迭代的空间谱估计（FPC-MUSIC）算法。首先建立基于矩阵填充的DOA估计信号模型,并验证该信号模型满足零空间性质（NSP）,其次通过不动点迭代算法将稀疏阵列信号恢复为完整信号,最后利用恢复信号估计二维DOA。该算法可在稀疏阵列下大幅度降低谱估计平均副瓣,在大幅度降低阵元数的同时具有较高的估计精度。计算机仿真表明：FPC-MUSIC算法可在稀疏阵列下准确估计二维DOA,验证了该算法的有效性和优越性。     

一种适用于任意阵列的极化和二维DOA联合估计算法

提出了一种适用于任意阵列的极化和二维DOA联合估计算法。该算法基于信号空时二维结构特征,利用空域采样和时域采样构造时空矩阵,通过DOA矩阵方法进行极化和二维DOA参数估计,不需要二维谱峰搜索,计算量小。仿真实验证明了算法的有效性。     

增益幅度不同时信号二维方向角和多普勒频率的盲估计

 在各阵元增益幅度不一致的条件下,提出了一种起伏目标的二维方向角和多普勒频率盲估计的新方法。此方法在各阵元增益幅度不一致的条件下,仍可获得很好的估计性能,并能应用于各个信号的频率相同的场合。且具有对噪声不敏感,不需进行谱峰搜索,适用范围广等特点。仿真结果表明了此算法的有效性。     

Direction of arrival tracking below the ambiguity threshold

We present an algorithm for direction-of-arrival (DOA) tracking that allows operation below the ambiguity threshold of the direction-finding (DF) system. Using multiple target tracking techniques, the algorithm turns the most likely DOAs of each measurement into multiple potential tracks and then selects the true track as that with the maximum cumulative likelihood. The improvement offered by the algorithm, namely the extension of the ambiguity-free signal-to-noise ratio (SNR) domain, is demonstrated in several simulated experiments using several array structures, including a sparse array and a uniform linear array     

DOA Estimation for Uniform Linear Array with Mutual Coupling

An algorithm is presented for direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling based on the generalized eigenvalues utilizing signal subspace eigenvectors (GEESE) algorithm for uniform linear array (ULA). It is not an iterative algorithm, and a spectral peak search is not required. The DOA can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. An algorithm for estimating the mutual coupling coefficients is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.     

DOA estimation and mutual coupling calibration with the SAGE algorithm

In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.     

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given previously (1989, 1991). Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE     

Sensitivity analysis of DOA estimation algorithms to sensor errors

A unified statistical performance analysis using subspace perturbation expansions is applied to subspace-based algorithms for direction-of-arrival (DOA) estimation in the presence of sensor errors. In particular, the multiple signal classification (MUSIC), min-norm, state-space realization (TAM and DDA) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are analyzed. This analysis assumes that only a finite amount of data is available. An analytical expression for the mean-squared error of the DOA estimates is developed for theoretical comparison in a simple and self-contained fashion. The tractable formulas provide insight into the algorithms. Simulation results verify the analysis     

Array shape estimation tracking using active sonar reverberation

This paper concerns the problem of array shape estimation and tracking for towed active sonar arrays, using received reverberation returns from a single transmitted CW pulse. Uniform linear arrays (ULAs) deviate from their nominal geometry while being towed due to ship maneuvers as well as ocean currents. In such scenarios, conventional beamforming performed under the assumption of a ULA can sometimes lead to unacceptably high spatial sidelobes. The reverberation leaking through the sidelobes can potentially mask weak targets in Doppler, especially when the target Doppler is close to that of the mainlobe reverberation and the reverberation-to-target ratio (RTR) is very high. Although heading sensors located along the array can be used to provide shape estimates, they may not be sufficiently available or accurate to provide the required sidelobe levels. We propose an array shape calibration algorithm using multipath reverberation returns from each ping as a distributed source of opportunity. More specifically, a maximum likelihood (ML) array shape calibration algorithm is developed, which exploits a deterministic relationship between the reverberation spatial and Doppler frequencies causing it to be low rank in the space-time vector space formed across a single coherent processing interval (CPI). In this application, a sequence of overlapped CPI length snapshots of duration less than the CW pulse is used. The ML estimates obtained for each snapshot are tracked using a Kalman filter with a state equation corresponding to the water pulley model for array dynamics. Simulations performed using real heading sensor data in conjunction with simulated reverberation suggest that 8-10 dB improvement in sidelobe level may be possible using the proposed array shape tracking algorithm versus an algorithm that uses only the available heading information.     

DOA and steering vector estimation using a partially calibratedarray

We consider the problem of estimating directions of arrival (DOAs) using an array of sensors, where some of the sensors are perfectly calibrated, while others are uncalibrated. We identify a cost function whose minimizer is a statistically consistent and efficient estimator of the unknown parameters-the DOAs and the gains and phases of the uncalibrated sensors. Next we present an iterative algorithm for finding the minimum of that cost function The proposed algorithm is guaranteed to converge. The performance of the estimation algorithm is compared with the Cramer Rao bound (CRB). The derivation of the bound is also included. It is shown that DOA accuracy can be improved by adding uncalibrated sensors to a precisely calibrated array. Moreover, the number of sources that can be resolved may be larger than the number that can be resolved by the calibrated portion of the array     

非圆信号的贝叶斯稀疏重构阵列测向方法

对信号非圆特性的有效利用能显著改善子空间类阵列测向方法的性能,但难以弥补此类方法在低信噪比（SNR）、小样本等信号环境适应能力方面的局限。本文引入贝叶斯稀疏学习（SBL）技术以解决非圆信号的波达方向（DOA）估计问题,在结合信号非圆特性的同时对入射信号的空域稀疏性加以利用,通过将非圆信号阵列输出协方差矩阵和共轭协方差矩阵在预先定义的空域字典集上进行稀疏重构,得到入射信号的空间谱重构结果,并依据其谱峰位置估计各信号的方向。该方法对独立和相关信号都具有较好的适应能力,仿真结果验证了该方法在信号环境适应能力和相关信号测向精度等方面的优势。     

Design of on-board calibration methods for a digital sun sensor based on Levenberg–Marquardt algorithm and Kalman filters

Digital sun sensor is one of the most important sensors used in the Attitude Determination System (ADS) of the satellite. Due to the harsh environmental conditions that exist in the space, various distortions may occur in the sun sensor optical system that lead to the reduced accuracy of this equipment. So, it is necessary to recalibrate the optical parameters of the aforementioned sensors. For this purpose, first a novel attitude independent error model is proposed for the SS-411 sun sensor that includes the central point of the CCD array, installation error, filter thickness and sensor misalignment. So, the mutual interfaces between the sensor parameters are considered in the developed model. In order to extract the sensor parameters, a nonlinear optimization technique called the Levenberg–Marquardt is applied to the developed model as a batch algorithm. In addition, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) have been utilized as sequential strategies. It will be shown that by considering a worst case of variation amount for sensor parameters, an accuracy improvement of about 17° is achieved by the developed calibration algorithms. Comparison between the developed algorithms represents that UKF has higher accuracy, shorter time convergence but higher computational load.