基于MUSIC算法的L型阵列MIMO雷达降维DOA估计

该文提出一种基于MUSIC算法的L型阵列多输入多输出雷达降维波达方向(DOA)估计算法。该算法首先针对L型阵列导向矢量的结构,构造出一个降维矩阵,将回波信号转换到低维空间。然后利用二次优化方法将2维DOA估计分解为两个1维DOA估计。最后利用MUSIC空间谱估计其中1维角度,并利用求得的角度回代谱函数,对另1维角度进行求根估计。该算法将2维空间谱搜索降为1维搜索,极大地降低了运算复杂度。理论分析和仿真结果验证了该算法的准确性和可行性。     

基于双向传播算子的互质面阵二维波达方向估计

针对谱峰搜索的二维波达方向估计中现有算法复杂度高,精度受搜索间隔影响较大的问题,给出了一种双向传播算子的互质面阵二维波达方向估计算法,实现了俯仰角和方位角的低复杂、高精度、无模糊联合估计.该方法首先将互质阵列引入到二维波达方向估计中,构造互质平面阵模型,然后采用两次旋转不变传播算子方法计算出不同阵列流型方向上的旋转因子矩阵,根据旋转因子矩阵解算出目标信号的俯仰角和方位角,同时利用互质理论消除了稀疏阵列角度估计的不确定性,证明了互质阵列模型下采用双向传播算子方法进行俯仰角和方位角估计的无模糊性.对算法的复杂度进行理论分析,并给出了平面阵列角度估计的克拉美罗界推导.理论分析与仿真结果表明,算法不需要进行角度匹配和谱峰搜索,在相同条件下的均方根误差性能优于均匀平面阵的多重信号分类算法,并且以较低的复杂度无模糊的达到了高维网格搜索的精度.     

基于DML-ESPRIT算法的多维参数快速联合估计

将确定性最大似然估计准则的多维参数估计能力与ESPRIT算法的高时效性有机结合,提出了一种二维DOA-功率-频率快速联合估计方法--DML-ESPRIT算法。利用双L阵列的空间特性,通过引入空间锥角,将多维空间搜索问题转换为一维角度估计,并基于确定性最大似然估计准则推导得到了多信源的空间锥角、功率和频率联合估计的数学模型;然后在对子阵进行扩展的基础上,利用TLS-ESPRIT算法对模型进行求解,避免了谱峰搜索问题,实现了多维参数的快速联合估计。实验结果表明,DML-ESPRIT算法在保持高估计精度的同时运行耗时约35 ms,具有较好的工程应用前景。     

一种新的波达方向和多普勒频率估计算法

基于均匀圆形阵列，提出了一种同时估计空间非相干信号源方位角、仰角和多普勒频率的快速算法。该方法对均匀圆阵的输出信号进行模式空间转换，使得阵列流形具有类似于均匀线阵的形式，然后通过构造相应的数据矩阵得到传播算子的最小二乘（LS）估计，并由传播算子构造出一个特殊的低维矩阵，其特征值给出多普勒频率估计，特征向量舍有阵列流形的信息。结合模式空间阵列流形的性质，给出了一种DOA估计的总体最小二乘算法，在低信噪比条件下可提高测向精度。该方法不需要谱峰搜索和参数配对，具有运算量小的优点。计算机仿真验证了该方法的有效性。     

基于阵列流型盲辨识的MIMO雷达二维DOD和二维DOA联合估计

MIMO（Multiple Input Multiple Output）雷达基于分集增益理念,使其相对于相控阵雷达,在目标探测、参数测量、多目标分辨及干扰识别和抑制等方面具有明显优势.目标角度估计是雷达目标参数测量的核心内容,也是雷达对空域目标进行定位和跟踪的前提.本文基于双L型阵列,提出了一种高精度低复杂度的双基地MIMO雷达二维离开角和二维到达角联合估计的新算法.通过对匹配滤波后的阵列接收数据进行子空间分解,实现了阵列流形矩阵的盲辨识,进而获得目标二维到达角和二维离开角的闭式解.所提算法估得的收发四维角（二维离开角和二维到达角）能够自动配对,与2-D ESPRIT（Two Dimensional Estimating Signal Parameters via Rotational Invariance Techniques）算法相比,运算复杂度约是其三分之一,角估计性能相当.仿真实验证明了所提算法以较低的运算复杂度,实现了对目标收发四维角的高精度联合估计.     

非均匀噪声下频率及二维到达角的联合估计

提出一种适用于L型阵列的频率及二维到达角联合估计新算法.算法通过不同子阵的互协方差矩阵构建平行因子模型,应用三线性交替最小二乘算法求解,无需参数配对及多维搜索,可有效抑制非均匀噪声.均方根误差的仿真结果验证了该算法的有效性.     

基于ESPRIT算法的L型阵列MIMO雷达降维DOA估计

该文针对L型阵列MIMO雷达的2维角度估计问题,基于ESPRIT算法提出两种降维DOA估计方法。首先通过降维矩阵的设计及回波数据的降维变换,将高维回波数据转换至低维信号空间;然后分别基于特征分解和传播算子获得信号子空间的估计,最后利用ESPRIT算法实现2维空间角参量的联合估计及参数的自动配对。算法不牺牲阵列孔径,最大程度地降低了回波数据的维数,具有更低的运算复杂度。仿真结果验证了该文理论分析的正确性和算法的有效性。     

宽频段相干信号三维参数联合估计的新算法

基于非均匀的L型阵列,本文提出了一种宽频段相干信号频率和二维到达角联合估计的时空二维处理新方法-WTSS(wide-band time-space smoothing)算法.该算法能精确地估计具有相同数字频率的相干信号的三维参数,无需多维谱峰搜索,具有计算量小,参数自动配对的优点.另外,在WTSS算法的基础上,利用L型阵列的特点进行分维处理,成功地实现了具有频率兼并现象的相干信号的三维参数估计.该算法能够并行实现以进一步增强其实时性.计算机仿真结果验证了算法的有效性.     

Joint estimation of TOA and DOA in IR-UWB system using a successive propagator method

Impulse radio ultra-wideband (IR-UWB) ranging and positioning require accurate estimation of time-of-arrival (TOA) and direction-of-arrival (DOA). With receiver of two antennas, both of the TOA and DOA parameters can be estimated via two-dimensional (2D) propagator method (PM), in which the 2D spectral peak searching, however, renders much higher computational complexity. This paper proposes a successive PM algorithm for joint TOA and DOA estimation in IR-UWB system to avoid 2D spectral peak searching. The proposed algorithm firstly gets the initial TOA estimates in the two antennas from the propagation matrix, then utilises successively one-dimensional (1D) local searches to achieve the estimation of TOAs in the two antennas, and finally obtains the DOA estimates via the difference in the TOAs between the two antennas. The proposed algorithm, which only requires 1D local searches, can avoid the high computational cost in 2D-PM algorithm. Furthermore, the proposed algorithm can obtain automatically paired parameters and has better joint TOA and DOA estimation performance than conventional PM algorithm, estimation of signal parameters via rotational invariance techniques algorithm and matrix pencil algorithm. Meanwhile, it has very close parameter estimation to that of 2D-PM algorithm. We have also derived the mean square error of TOA and DOA estimation of the proposed algorithm and the Cramer-Rao bound of TOA and DOA estimation in this paper. The simulation results verify the usefulness of the proposed algorithm.     

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.     

L-shaped array-based elevation and azimuth direction finding in the presence of mutual coupling

For two-dimensional (2-D) directions-of-arrival (DOA) estimation problem, both the mutual coupling and the failure in pairing can cause severe performance degradation. In this paper, a new elevation and azimuth direction finding algorithm is developed to overcome the above-mentioned two difficulties in the L-shaped array configuration. The key points of this paper are: (i) constructing several correlation matrices to blindly compensate the effect of unknown mutual coupling using the outputs of properly chosen sensors and (ii) deriving a rank-reduction propagator method to estimate elevation and azimuth angles so as to avoid pairing parameters. Simulation results are presented to validate the performance of the proposed method.     

An automatically paired two-dimensional direction-of-arrival estimation method for two parallel uniform linear arrays

In two-dimensional (2-D) direction-of-arrival (DOA) estimation, paring the azimuth and elevation angles of multiple sources is an important issue. In this letter, we propose a new automatically paired 2-D DOA estimation method by designing the geometry of two antenna subarrays and using the propagator method (PM). A special geometry between two parallel uniform linear arrays (ULAs) with a position displacement on the axial direction is proposed to facilitate the elevation and azimuth pairing and estimation. The simulation results have shown that the proposed method can achieve the same 2-D DOA estimation performance as the existing methods, while the complexity is reduced considerably.     

稀疏表示框架下无需参数配对的二维到达角估计

现有二维到达角估计算法大多基于子空间理论及需要参数配对,针对这一问题,在稀疏表示理论框架下提出了一种参数自动配对的二维到达角估计新算法。该算法在L阵列下构建阵列互相关矩阵的稀疏表示模型,利用奇异值分解降低复杂度并基于群LASSO(Least Absolute Shrinkage and Selection Operator)获得方位角估计。在方位角估计的基础上,基于向量化操作构建稀疏空间谱匹配模型,然后利用LASSO 获得俯仰角估计。与参数配对ESPRIT 和改进的传播算子方法相比,所提算法不仅无需参数配对过程,而且可以提供改进的估计精度。计算机仿真结果验证了所提算法的有效性。     

互相关矩阵重排获取信号子空间的二维DOA估计算法

本文提出一种L型阵列的二维子空间DOA估计算法,该算法通过重排阵列接收数据的互相关矩阵获取信号子空间,然后根据信号子空间生成一个二维谱函数,最后通过二维搜索估计信号的来波方向。由于该算法采用二维谱峰搜索,所以不需要对俯仰角和方位角进行配对。与二维MUSIC算法相比,该算法的估计精度略有下降,但该算法不需要对矩阵做特征值分解,计算量降低且易于实现。文中给出了该算法的推导过程和具体实现步骤,并进行了实验仿真,仿真结果说明了算法的有效性。      

基于MIMO技术的二维波达方向估计

传统的L型阵相比面阵精简了阵列结构,以较少的阵元实现二维波达方向估计,但是波达方向估计性能受到物理孔径限制。本文将MIMO技术和L型阵结合,提出一种基于MIMO技术的L型阵二维波达方向估计方法。该方法通过MIMO等效虚拟阵列原理,将L型阵等效为一矩形平面阵列,然后在等效矩形阵列的基础上,采用MUSIC进行二维波达方向估计,以L型阵的物理孔径实现矩形平面阵列的估计性能。本文推导了二维波达方向估计的CRB,计算机仿真实验证实了所提方法的有效性。      

Joint Angle and Array Gain-Phase Errors Estimation Using PM-like Algorithm for Bistatic MIMO Radar

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramér–Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach.     

Two-dimensional DOA estimation for acoustic vector-sensor array using a successive MUSIC

This paper discusses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for acoustic vector-sensor array, and derives a successive multiple signal classification (MUSIC) algorithm therein. The proposed algorithm obtains initial estimations of the azimuth and elevation angles obtained from the signal subspace, and uses successively one-dimensional local searches to achieve the joint estimation of 2D-DOA. The proposed algorithm, which requires the one-dimension local searches, can avoid the high computational cost within 2D-MUSIC algorithm. The proposed algorithm can obtain automatically-paired 2D-DOA estimation for acoustic vector-sensor array, and it has better DOA estimation performance than propagator method, estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Meanwhile, it has very close angle estimation to 2D-MUSIC algorithm. Furthermore, it is suitable for non-uniform linear arrays, works well for the sources with the same azimuth angle, and imposes less constraint on the sensor spacing, which does not have to be restricted within half-wavelength. We have also derived the mean-square error of DOA estimation of the proposed algorithm and the Cramer-Rao bound of DOA estimation. Simulation results verify the usefulness of the proposed algorithm.     

Unfolded coprime L-shaped arrays for two-dimensional direction of arrival estimation

Generally, a coprime L-shaped array (CLsA) is composed of two uniform L-shaped subarrays with larger spacing among inter-element to accomplish the improved direction of arrival (DOA) estimation performance. In this paper, the two subarrays are unfolded to extend the array aperture and the performance of the unfolded CLsA (UCLsA) for two-dimensional (2D) DOA estimation is investigated. In addition, an all array multiple signals classification (AA-MUSIC) algorithm is proposed for the UCLsA. By stacking the received signals of the two subarrays, the ambiguity problem can be avoided on the basis of the coprime property. Simultaneously, due to the combination of the cross-correlation and auto-correlation, the proposed AA-MUSIC algorithm can achieve the full degrees of freedom (DOFs) and obtain more accurate DOA estimates, nevertheless, the expensive total spectral search is entailed. Consequently, a reduced complexity MUSIC (RC-MUSIC) algorithm is proposed to relieve the computational burden. The Cramer-Rao Bounds (CRBs) are utilised as a theoretical benchmark for the lower bound of unbiased estimate. Furthermore, numerical simulations verify the effectiveness and superiority of the AA-MUSIC algorithm and RC-MUSIC method for the UCLsA.     

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。     

相干信号源的空时三维参数分离估计方法

文中提出了一种宽频段窄带相干信号源的频率和二维波达方向估计的新方法。该方法利用L型阵列并结合一定的时域信息,将相干源的三维参数估计问题转化为3个一维问题,并在每一维使用空间平滑方法和ESPR IT算法求解相应参数,最后通过求解联立方程,获得相干信号的三维参数。因此,避免了谱峰搜索,并且算法本身的并行处理能力,提高了测向系统的实时性。计算机仿真结果表明了该算法的有效性。