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.     

A low-complexity joint 2D-DOD and 2D-DOA estimation algorithm for MIMO radar with arbitrary arrays

In this article, we study the problem of four-dimensional angles estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays, and propose a joint two-dimensional direction of departure (2D-DOD) and two-dimensional direction of arrival (2D-DOA) estimation algorithm. Our algorithm is to extend the propagator method (PM) for angle estimation in MIMO radar. The proposed algorithm does not require peak searching and eigenvalue decomposition of received signal covariance matrix, because of this, it has low computational complexity. And it can achieve automatic pairing of four-dimensional angles. Furthermore, the proposed algorithm has much better angle estimation performance than interpolated estimation method of signal parameters via rotational invariance techniques (ESPRIT), and has very close angle estimation performance to ESPRIT-like algorithm which has higher computational cost than the proposed algorithm. We also analyze the complexity and angle estimation error of the algorithm, and derive the Cramer‐Rao bound (CRB). The simulation results verify the effectiveness and improvement of the proposed algorithm.     

A RD-ESPRIT algorithm for coherent DOA estimation in monostatic MIMO radar using a single pulse

This paper discusses the problem of coherent direction of arrival (DOA) estimation in a monostatic multi-input multi-output (MIMO) radar using a single pulse, and proposes a reduced dimension (RD)-estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. We reconstruct the received data and then utilise it to construct a set of Toeplitz matrices. After that, we use RD-ESPRIT to obtain the DOAs of the sources. The proposed algorithm is effective for coherent angle estimation based on a single pulse, and it has much better angle estimation performance than the forward backward spatial smoothing (FBSS)-ESPRIT algorithm and the ESPRIT-like of Li, as well as very close angle estimation performance to the ESPRIT-like of Han. For complexity comparison, our algorithm has very close complexity to the FBSS-ESPRIT algorithm, and lower complexity than the ESPRIT-like of Han and the ESPRIT-like of Li. Simulation results present the effectiveness and improvement of our approach.     

波形相关矩阵未知情况下单基地MIMO雷达中一种改进MUSIC的DOA估计算法

该文研究了波形相关矩阵未知情况下多输入多输出(MIMO)雷达中的角度估计问题,提出了一种单基地MIMO雷达中改进多重信号分类(MUSIC)的到达角(DOA )估计算法。该算法可以在波形相关矩阵未知的情况下工作且性能优于传统的传播算子(PM)和借助旋转不变技术估计信号参数(ESPRIT)算法以及基于接收信号重构的MUSIC算法。该文算法可以扩展到任意阵列结构的MIMO雷达中进行角度估计。该文还给出了单基地MIMO雷达中DOA估计的克拉美罗界(CRB)。仿真结果验证了该算法的有效性。     

Angle Estimation Using Quaternion-ESPRIT in Bistatic MIMO-Radar

In this letter; we present a novel two-dimensional angle estimation for bistatic multiple-input multiple-output (MIMO) radar. We reconstruct the received signal of MIMO radar to model with quaternion theory, and then angle estimate using quaternion estimation of signal parameters via rotational invariance technique for MIMO radar is proposed. The proposed algorithm can obtain automatically paired two-dimensional angle estimation in MIMO-radar. The proposed algorithm has much better angle estimation performance than the Wang’s quaternion algorithm, which has a much heavier computational load than the proposed algorithm. Simulation results verify the usefulness of our algorithm.     

Reduced-Dimensional ESPRIT for Direction Finding in Monostatic MIMO Radar with Double Parallel Uniform Linear Arrays

In this paper, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with double parallel uniform linear arrays is studied, and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through a series of reduced-dimensional transformations, the proposed algorithm has very low complexity due to the low dimension. Meanwhile, the estimation performance of the proposed algorithm is slightly improved compared to the conventional ESPRIT, especially in low signal-to-noise ratio. Furthermore, the algorithm can estimate azimuth and elevation angles without additional pair matching in monostatic MIMO radar. Error analysis of the angle estimation and Cramér–Rao bound are derived. Simulation results verify the usefulness of our algorithm.     

CS quadrilinear model-based angle estimation for MIMO radar with electromagnetic vector sensors

In order to estimate the angles for bistatic MIMO radar with electromagnetic vector sensors, we link the compressed sensing (CS) theory with quadrilinear model, and propose a novel angle estimation algorithm. In the proposed algorithm, the received data is firstly arranged into a quadrilinear model and then it is compressed according to the compressed sensing theory. We then perform quadrilinear decomposition on the compressed quadrilinear data model via the quadrilinear alternating least square (QALS) algorithm and finally obtain the automatically paired angle estimates with sparsity. Owing to compression, the proposed algorithm has smaller storage requirement and lower computational complexity than the conventional quadrilinear decomposition-based algorithm. Moreover, our algorithm has higher angle estimation accuracy than the estimation signal parameters via rotational invariance techniques (ESPRIT) algorithm and its estimation performance is close to that of the conventional quadrilinear decomposition-based algorithm. Our proposed algorithm needs neither additional pair matching, nor spectral peak searching, and it can be applied to both uniform and non-uniform arrays. Effectiveness of our proposed algorithm is assessed through various simulation results.     

一种双基地MIMO雷达目标参数估计算法

为了实现双基地多输入多输出(MIMO)雷达目标参数估计,建立了双基地MIMO雷达信号模型,提出了一种目标方向角和多普勒频率估计方法,该方法利用2次ESPRIT方法分别估计目标的多普勒频率和来波角度(DOA),然后利用ROOT-MU-SIC方法估计得到目标去波方向(DOD)。所提算法只进行3次特征值分解,避免了多维非线性的谱峰搜索,配对过程简单。仿真结果证明了该算法的有效性。     

低快拍下多输入多输出雷达中的角度估计算法

研究了双基地多输入多输出(MIMO)雷达中的角度估计问题,提出了一种低快拍下的MIMO雷达的离开角(DOD)和波达角(DOA)联合估计算法。该算法利用矩阵束方法从接收数据中构造出扩展矩阵来进行奇异值分解(SVD),进而进行二维角度估计联合估计。在低快拍数情况下所提算法的角度估计性能优于传统的借助旋转不变技术的信号参数估计(ESPRIT)方法,同时该算法能自动配对、无需谱峰搜索,而且复杂度也低于传统的ESPRIT算法。分析了所提算法复杂度,推导了克拉美-罗界(CRB)。仿真结果验证了该算法的有效性。     

基于四元数的Root-MUSIC的双基地MIMO雷达中角度估计算法

该文将四元数理论应用到双基地集中式多输入多输出(MIMO)雷达的角度估计中。文中通过传统数据模型构造四元数矩阵,提出了基于四元数的求根-多重信号分类(Root MUltiple SIgnal Classification, Root-MUSIC)的MIMO雷达中角度估计算法,该算法通过奇异值分解和Root-MUSIC来估计出发射角(Direction Of Departure, DOD)和接收角(Direction Of Arrival, DOA)。该算法的角度估计性能远优于现有文献的方法,并且无需谱峰搜索,复杂度大大降低。仿真结果验证了算法的有效性。     

A Low-Complexity Algorithm for Coherent DOA Estimation in Monostatic MIMO Radar

This paper investigates the topic of direction of arrival (DOA) estimation for coherent sources in monostatic multi-input multi-output (MIMO) radar, and proposes a low-complexity algorithm for coherent DOA estimation. The direction vector of MIMO radar can be firstly mapped into a vector of virtual uniform linear array (ULA), and after that, a linear operator is constructed by partial cross-correlations from the received data of the virtual ULA. Finally, the DOAs can be obtained via roots finding method based on this linear operator. The DOAs can be estimated without any eigen-decomposition, nor evaluating all correlations of the received data. The proposed algorithm has much lower complexity as well as much better angle estimation performance than conventional forward backward spatial smoothing (FBSS)-propagator method (FBSS-PM), FBSS- estimation method of signal parameters via rotational invariance techniques (FBSS-ESPRIT), FBSS- root multiple signal classification (FBSS-Root MUSIC), and ESPRIT-like algorithm. Simulations present the effectiveness and improvement of our approach.     

双基地分布式阵列MIMO雷达的DOA和DOD估计方法

结合分布式阵列和双基地多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达的特点, 提出了一种新的双基地分布式阵列MIMO雷达的接收角(Direction of Arrival, DOA)和发射角(Direction of Departure, DOD)估计方法.根据发射阵列和接收阵列的空域旋转不变特性, 利用旋转不变估计技术(Estimation of Signal Parameters via Rotational Invariance Techniques, ESPRIT)获取无模糊DOA粗估计和高精度周期性模糊的DOA、DOD精估计; 再利用无模糊DOA粗估计、目标的双基地距离信息以及双基地MIMO雷达的几何特点, 解除DOA、DOD精估计的周期性模糊, 得到高精度且无模糊的DOA和DOD估计.最后, 根据ESPRIT算法原理和估计误差的概率统计特性进行算法的性能分析, 给出算法基线模糊门限的近似计算方法.该算法有效地放宽了发射阵列孔径扩展程度的限制, 从而提高了阵列在大孔径下的角度估计精度, 且能够实现DOA和DOD估计的自动配对.仿真结果验证了所提算法和性能分析方法的有效性.     

双基地MIMO雷达目标快速定位算法

基于多级维纳滤波器（MSWF）的双基地多输入多输出（MIMO）雷达定位算法,能够有效的降低计算复杂度,但是会带来信号子空间的扩展问题,降低算法性能。因此本文提出了一种新的目标快速定位算法。首先根据MSWF的自相关模值的特点确定需要的MSWF级数,得到扩展子空间;其次利用ESPRIT方法得到目标的初始发射角（DOD）和接收角（DOA）,最后利用判别准则从初始目标角度得到真实目标角度,并完成目标收发角度的配对。算法不需要特征值分解,计算复杂度较低,并且充分利用了扩展子空间,具有更高的角度估计性能。      

双基地MIMO雷达四维角度和多普勒频率联合估计

该文提出了一种新的基于L型阵列双基地MIMO雷达多目标的4维角度和多普勒频率联合估计的算法,该算法根据DOA 矩阵法的思想构造矩阵,通过特征参数与待估参数之间的特定关系,推导出了目标2维DOA, 2维DOD及多普勒频率联合估计式,并得到闭式解。该算法无需谱峰搜索,只需一次特征值分解,且估计出的5维参数自动配对,与ESPRIT算法相比,计算复杂度降低,且性能非常接近,并能克服空间色噪声的影响,在发射信号非完全正交时仍旧适用。仿真结果验证了该算法的有效性。     

基于块正交匹配追踪预处理的米波多输入多输出雷达测高方法研究

米波雷达具有很好的反隐身性能。多输入多输出(MIMO)雷达的波形分集具有高自由度特点,使MIMO雷达在检测和参数估计等方面具有更多优势,故米波MIMO雷达受到广泛研究。而测高是米波MIMO雷达最重要的问题之一。针对米波MIMO雷达测高问题,最大似然和广义多重信号分类方法是米波MIMO阵列雷达测高方法行之有效的算法,但其计算量大,工程中难以接受。该文提出一种基于块正交匹配追踪(BOMP)预处理的方法来降低计算量。首先对MIMO阵列接收数据稀疏化处理,然后通过数学操作将其变形至适合于BOMP算法的信号模型,然后利用粗栅格得到角度粗估计。并以此为初始值中心,取MIMO雷达波束宽度作为搜索范围。仿真结果表明该算法能有效降低搜索类测高算法的计算量。      

双基地MIMO雷达目标角度快速跟踪算法

针对双基地多输入多输出（MIMO）雷达目标角度跟踪问题,提出了一种低复杂度目标角度跟踪算法。首先估计出相邻时刻接收数据协方差矩阵的差;然后推导了协方差矩阵的差与目标角度差的关系,并且得到了相应的公式;最后通过最小二乘法得到相邻时刻的角度差,估计出目标的收发角度。算法避免了协方差矩阵的分解,实现了相同时刻收发角度的自动配对,降低了计算复杂度。由于算法能够实现相邻时刻的角度的自动相关,不需要复杂度高的角度相关算法,能够实现目标角度的快速跟踪,为双基地MIMO雷达的实际应用提供理论支持。      

Closed-Form Blind 2D-DOD and 2D-DOA Estimation for MIMO Radar with Arbitrary Arrays

In this paper, we study the problem of four-dimensional angle estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays, and propose a close-form joint two-dimensional direction of departure and two-dimensional direction of arrival estimation algorithm. Our work is to extend the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to angle estimation in MIMO-radar with arbitrary arrays. The algorithm can achieve automatically paired four-dimensional angles, requires no peak searching, has low complexity, and does not need to compensate for the phase. Furthermore, the proposed algorithm has much better angle estimation performance than the interpolated ESPRIT algorithm and propagator method. We also analyze and derive the complexity of the algorithm and the Cramer–Rao bound. The simulation results verify the effectiveness of the algorithm.     

DOD and DOA estimation in bistatic MIMO radar for nested and coprime array with closed-form DOF

It is well known that sparse array can offer better angle resolution than that of uniform linear array (ULA) in the same number of physical sensors. But in bistatic minimum redundancy sparse array multi-input multi-output (MIMO) radar, it cannot offer closed-form degree of freedom (DOF) for the arbitrary number of sensors with direction of departure and direction of arrival estimation. Therefore, this article introduces a nested array and coprime array into sparse array to solve the problem. First, construct no holes difference-coarray by extracting specified covariance matrix elements. Then, transform the difference-coarray into ULA within bistatic MIMO radar through some mathematical operations. As a result, many angle estimation methods for traditional ULA can be applied to the sparse bistatic MIMO array radar. The proposed algorithm offers closed-form DOFs for sparse array and the array aperture is much larger than that of ULA with identical number of sensors. The usefulness of the proposed methods is verified through computer simulations.     

Unitary Subspace-Based Method for Angle Estimation in Bistatic MIMO Radar

In this paper, the issue of joint angle estimation for bistatic multiple-input multiple-output (MIMO) radar is investigated, and an algorithm for the joint estimation under real-valued computation is proposed. By utilizing the unitary transformation, the direction matrices and the data matrix are transformed to be real-valued ones. The direction of departure (DOD) can be estimated via the real-valued rotational invariance in the subspace, and the direction of arrival (DOA) can be obtained via the real-valued reduced-dimension function of multiple signal classification (MUSIC). The proposed algorithm utilizes both the signal and noise subspaces, requires no peak searching, and can achieve automatically paired estimations of the angles. Furthermore, it has better angle estimation performance than some existing methods. The simulation results verify the algorithmic effectiveness and robustness of the proposed algorithm.     

基于 GSVD 的分布式 MIMO 雷达测向算法

针对分布式多输入多输出（multi-input multi-output, MIMO）雷达测向中存在的数据信息提取不充分、运算量偏大等问题，开展了基于广义奇异值分解（generalized singular value decomposition, GSVD）的测向算法研究，以提高低信噪比条件下的角度估计性能。首先，建立了分布式阵列MIMO雷达回波信号的统一化表征模型；其次，将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题，运用交替最小二乘（alternating least squares, ALS）技术实现阵列信号流行矩阵的拟合，并引入子空间类算法实现目标角度联合估计；最后，对优化问题增加l1范数约束，避免了每次迭代中进行的奇异值分解运算，降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。