色噪声环境下双基地MIMO雷达相干目标角度估计算法

通过研究多输入多输出(Multiple input and multiple out,MIMO)雷达的角度估计算法,针对色噪声环境下双基地MIMO雷达相干目标角度估计问题,基于双基地MIMO雷达的信号模型,利用MIMO雷达的接收数据,通过四阶累积量的计算消除了色噪声的影响,并证明得到的一组矢量包含所有目标的角度信息;然后利用得到的四阶累积量矢量构造出块Hankel矩阵,并证明该矩阵的秩等于目标总数,且不受信号相干性的影响,通过奇异值分解,结合ESPRIT算法实现了色噪声环境下对相干目标的角度估计.算法结合四阶累积量和MIMO雷达的估计性能,具有自动抑制加性高斯白噪声和色噪声的能力,实现了相干目标的有效估计和参数的自动配对,提高了抗噪能力,更利于在实际中应用.最后计算机仿真结果证实了算法的有效性和可行性.     

基于双L阵的双基地MIMO雷达多目标角度估计

提出了发射和接收均为L阵配置下的双基地MIMO雷达多目标角度估计的方法.基于ESPRIT算法,分别提取出发射、接收端的方位和俯仰旋转不变因子,构造了2个配对复矩阵并对其特征分解,根据特征值的实部和虚部分别估计出发射方位和俯仰角,接收方位和俯仰角,并实现了各角度间的自动配对.推导了双L阵配置下双基地MIMO雷达角度估计的克拉美罗界.仿真结果验证了本文算法的有效性.     

Massive MIMO系统基于子空间的半盲信道估计

针对大规模MIMO系统中存在的导频污染问题,结合目前研究的基于奇异值（SVD）分解的信道估计算法,在考虑到该算法中的协方差矩阵是用有限的样本数据代替真实数据必然存在偏差的问题,给出了一种联合ILSP（Iterative Least Square with Projection）的基于SVD的半盲信道估计算法。仿真结果表明改进后的信道估计算法能够有效减小已有算法中存在的偏差问题,提高信道估计精确度,有效减轻导频污染给大规模MIMO系统带来的影响,从而实现大规模MIMO系统性能的提升。     

未知互耦下双基地MIMO雷达阵列DOD和DOA估计算法

针对MIMO雷达阵列中存在的互耦效应会严重影响发射角（DOD）和接收角（DOA）估计性能的问题,提出了一种基于ESPRIT算法的双基地MIMO雷达阵列DOA和DOD估计方法。该方法利用阵列流型矩阵的结构特点和均匀线阵互耦矩阵的带状Toeplitz特性,能实现互耦效应未知情况下DOA和DOD的精确估计。本文方法无需对角度进行搜索,计算量小,对角度的估计精度高,特别是在低信噪比环境下依然能取得 较好的估计性能。仿真结果验证了本算法的可行性和正确性。     

空间色噪声环境下双基地MIMO雷达多目标角度估计和互耦自校正

提出了在空间色噪声环境下双基地MIMO雷达多目标角度估计和互耦自校正的算法.利用四阶累积量能抑制空间色噪声的特性,对数据进行预处理.通过互耦系数矩阵的Toeplitz特性和ESPRIT算法思想,分别提取出发射端和接收端的旋转不变因子,并且实现了发射角和接收角的自动配对.最后利用估计出来的发射角和接收角,采用拉格朗日乘数法实现了互耦系数的自校正.仿真结果证明了本文算法的有效性.     

一种基于联合加权和截断的毫米波大规模MIMO信道估计

提出一种联合加权和截断核范数的毫米波大规模多输入多输出（MIMO）信道估计算法。针对毫米波大规模MIMO信道估计问题中训练和反馈开销大的问题,首先利用毫米波信道天线角度域稀疏的特性,把信道估计问题转化为低秩矩阵恢复问题。采用一种有效而灵活的秩函数——联合加权截断核范数作为核范数的松弛,构造出一种新的矩阵恢复模型用于信道估计问题,以最小化加权截断核范数为优化目标,并利用交替优化框架求解。仿真结果表明,该方法可以有效地提高信道估计的精度,并且具有可靠的收敛性。     

基于IALM的穿墙雷达成像算法

针对传统压缩感知穿墙雷达成像算法存在的计算过程复杂、对噪声较敏感等问题,提出一种压缩感知框架下的基于非精确增广拉格朗日乘子（Inexact Augmented Lagrange Multiplier,IALM）的穿墙成像算法。考虑到墙体回波信号的低秩性和目标信号的稀疏性,将穿墙成像视为正则化最小二乘优化问题,并转化为一个含核范数和[l1]范数的复合优化问题。通过非精确增广拉格朗日乘子法交替更新迭代求解墙杂波矩阵和目标矩阵,从而完成目标重构。仿真结果表明所提算法的信杂比较好,并在显著提高目标成像精度的同时提高了处理速度。     

四元数扩维无迹卡尔曼滤波算法及其在大失准角快速传递对准中的应用

针对快速传递对准中量测失准角为大角度的情况,在非线性欧拉角误差模型基础上,推导了一种基于乘性四元数的等效快速传递对准模型.为解决四元数在无迹卡尔曼滤波（UKF）算法中的应用问题,提出了一种基于四元数的状态扩维无迹卡尔曼滤波（Q--AUKF）算法.该算法将系统噪声增广到状态向量中,解决了乘性四元数噪声无法进行向量意义下四则运算的问题.针对四元数加权均值规范化的限制,采用平均四元数算法保证其正交规范化要求.最后将其应用到快速传递对准中的仿真实验结果表明,在量测误差角为大角度的情况下,该算法具有更高的估计精度与收敛速度.     

全双工MIMO中继系统中基于最大速率的波束成形算法

全双工系统能实现在同一时隙与同一频率传输数据,相比于半双工系统能大大地提升数据吞吐量和频谱效率。为了进一步提高全双工多输入多输出(Multi-input and multi-output,MIMO)中继系统传输速率,本文基于放大转发(Amplify-and-forward,AF)传输模式,在全双工双向中继系统中引入梯度下降算法,将用户发送端、接收端波束成形与中继端波束成形矩阵相结合设计一种最大化速率的交替迭代算法,并构造出一种最小均方误差（Minimum mean square error,MMSE）迭代算法作为初始条件,在此基础上推导出中继接收端与发射端的波束成形矩阵表达式。仿真结果表明,本文构造的交替迭代算法收敛速度快,而且相比于迫零、最小均方误差以及最大泄信噪比算法,和速率有显著提高。     

基于格基缩减的MU-MIMO下行传输策略

块对角化（Block diagonalization, BD）预编码算法通过两次奇异值分解实现多用户间干扰消除并将下行多用户多输入多输出（Multi-user multiple-input multiple-output, MU-MIMO）信道解耦成多个独立 的单用户MIMO（Single-user MIMO, SU-MIMO）信道,但其计算复杂度也随着用户数量和矩阵维数的增加而增加 。在MU-MIMO下行系统中,提出基于格基缩减的改进块对角化传输策略,将BD算法的第2次奇异值分解替换为基于格基缩减的线性检测,可得到比传统BD传输策略更好的误码率性能以及更低的计算复杂度。     

Two-stage ESPRIT for unambiguous angle and range estimation in FDA-MIMO radar

Frequency diverse array (FDA) offers potential applications for joint range and angle estimation, but ambiguous estimates may be generated due to its range-angle coupling and time-variant beampattern. This problem can be addressed by jointly utilizing FDA and multiple-input multiple-output (MIMO) radar, but only multiple signal classification (MUSIC) algorithm was considered in the FDA literature. In order to avoid high computational complexity in the MUSIC algorithm due to the required 2-D peak searching, in this paper, we propose a two-stage estimating signal parameters via rotation invariance technique (ESPRIT) algorithm for FDA-MIMO radar to estimate both range and angle of targets, along with the proposed pairing method for unambiguous estimates. Moreover, closed-form expressions of the mean squared error (MSE) and Cramér-Rao lower bound (CRLB) for angle and range estimations are also derived. All proposed methods and derivations are verified by both theoretical analysis and numerical results, which show the superiority of FDA-MIMO radar over conventional phased-array radar and MIMO radar in target localization.     

Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

单基地MIMO雷达中相干目标的波达角和多普勒频率快速联合估计算法

针对单基地MIMO中相干目标的波达角（Direction-of-arrival,DOA）和多普勒频率联合估计问题,提出了一种降维-前向平滑-传播算子算法（Reduced dimension-forward spatial smoothing-propagator method,RD-FSS-PM）。该算法首先通过对接收信号进行降维变换以降低复杂度,继而利用前向平滑技术（Forward spatial smoothing,FSS）实现解相干,最后通过传播算子算法（Propagator method,PM）实现了对相干目标的波达角和多普勒频率联合估计,且无需额外配对。与传统的FSS-PM算法相比,所提算法波达角估计性能提升,多普勒频率估计性能接近而复杂度大大降低。本文同时分析了算法的理论均方误差（Mean squared error,MSE）和单基地MIMO雷达中波达角和多普勒频率联合估计问题的克拉美罗界（Cramer-Rao bound,CRB）。最后提供了详尽的仿真实验以验证算法的性能。     

双基地MIMO雷达中基于传播算子的DOD和DOA估计算法

多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势.本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,DOA)估计...     

Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter

This paper mainly deals with distributed targets detection with a polarimetric MIMO radar against compound-Gaussian clutter dominated scenario with unknown covariance matrix. First, the general polarimetric detecting problem of the distributed targets is developed to the MIMO radar, and then, the fully adaptive Generalized Likelihood Ratio Test (GLRT) is devised according to the well known two-step design procedure. Three covariance matrix estimation strategies using the secondary data are introduced to make derived receiver fully adaptive. A thorough performance assessment is given, and via several numerical examples, the results highlight that the spatial and polarization diversities can be exploited to improve the detection performance of the distributed targets in compound-Gaussian background, and it outperforms the phased-array counterpart, the adaptive loss is completely acceptable in practical applications. Meanwhile, the fixed-point estimation strategy is more suitable to implement the adaptive detection algorithm.     

Two-dimensional angle estimation for monostatic MIMO arbitrary array with velocity receive sensors and unknown locations

In this paper, the problem of two-dimensional angle estimation for monostatic multi-input multi-output (MIMO) array is studied, and an algorithm based on the usage of velocity receive sensors is proposed. The algorithm applies the estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm to obtain automatically paired two-dimensional angle estimation. By utilizing the relationship within the outputs of velocity sensors, the rotational invariance property of ESPRIT does not depend on the array geometry any more. Hence, the proposed algorithm can provide two-dimensional DOA estimation for the MIMO array without the knowledge of sensor locations in the array. The algorithm requires no peak searches, so it has low complexity. Furthermore, it has better angle estimation performance than propagator method using the same sensor configuration. Error analysis and Cramér–Rao bound (CRB) of angle estimation in MIMO radar are derived. Simulation results verify the usefulness of the algorithm.     

十字阵的单基地MIMO雷达中一种低复杂度的二维DOA估计算法

提出了在单基地多输入多输出(Multiple-input multiple-output,MIMO)雷达中的基于十字阵的一种低复杂度的二维波达方向(Direction of arrival,DOA)估计算法。该算法利用传播算子法(Propagator method,PM)避免了协方差矩阵的构造及其特征值分解,也无需谱峰搜索,从而大大降低了运算的复杂度;同时该算法可实现方位角和仰角的自动配对。本文算法的性能在高信噪比下逼近借助旋转不变技术估计信号参数(Estimation ofsignal parameters via rotational invariance techniques,ESPRIT)算法。文中还推导了目标方位角和仰角的均方误差。仿真结果证明了该算法的有效性。     

MIMO雷达最大似然参数估计

多输入多输出(MIMO)雷达使用多个天线同时发射多个独立探测信号,并使用多个天线接收目标回波信号.本文考虑了发射空域分集、相干接收MIMO雷达模型及其最大似然(ML)参数估计方法.基于最大似然准则,本文推导了两种渐近最大似然算法.仿真实验的结果表明,在均匀噪声模型中,其中一种渐近算法与基于延迟求和波束形成的最大似然算法性能接近,而另一种渐近算法性能略差,但具有较低的计算复杂度.而在非均匀噪声模型中,本文所提出的两种渐近最大似然算法的性能均优于基于延迟求和波束形成的最大似然算法.     

A coherent direction of arrival estimation method using a single pulse

This paper addresses the problem of coherent direction of arrival (DOA) estimation in monostatic multi-input multi-output (MIMO) radar using a single pulse, and links the trilinear model to derive a coherent DOA estimation method. We use the received data to construct a set of Toeplitz matrices through which a trilinear model is formed, and then the trilinear decomposition is used to attain the DOAs of sources. The proposed algorithm is effective for a single pulse. Compared to the forward backward spatial smoothing estimation method of signal parameters via rotational invariance techniques (ESPRIT), the ESPRIT-like of Han, and the ESPRIT-like of Li algorithms, our method has better angle estimation performance. Numerical simulations present the effectiveness and improvement of our approach.