酉稀疏贝叶斯学习降维MIMO雷达角度估计

针对收发共址多输入多输出（Multiple-Input Multiple-Output,MIMO）雷达的低计算复杂度波达方向（Direction of Arrival,DOA）估计问题,提出一种降维的MIMO雷达高精度DOA新算法。首先采用经过白化的降维矩阵对MIMO雷达脉冲压缩后的接收信号进行降维;然后通过最优信号子空间拟合对无幅度误差阵列流型下的接收信号矩阵进行重构;接下来通过酉变换得到实值增广数据矩阵,并在实值稀疏字典矩阵下对其进行稀疏表示;接着将DOA估计问题转化为行稀疏矩阵的稀疏恢复问题,通过改进的稀疏贝叶斯学习对其进行求解,实现目标DOA的估计。理论分析和仿真实验结果验证了该方法的有效性和实用性。     

基于稀疏表示的多输入多输出雷达多目标定位

针对双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达目标定位问题,提出一种基于稀疏表示的双基地MIMO雷达多目标定位方法.利用点目标所在的二维角度空间构造冗余字典; 通过对接收信号的协方差矩阵进行特征分解,从中选取不同数目的特征向量在该冗余字典下稀疏表示,构建以特征向量为观测信号的多重测量向量(Multiple Measurement Vectors,MMV)模型,提取的特征向量在充分包含目标的角度信息的前提下,降低了直接以接收信号为观测信号的矩阵维数,形成低维稀疏线性模型; 最后,通过特征向量的稀疏重构,得到目标的角度估计.与现有算法相比,该算法对特征向量的稀疏重构降低了重构原始接受信号的计算复杂度,且在低信噪比和低快拍下仍有较好的估计性能,仿真实验验证了算法的有效性.     

DOA estimation for monostatic MIMO radar based on unitary root-MUSIC

Direction of arrival (DOA) estimation is an important issue for monostatic MIMO radar. A DOA estimation method for monostatic MIMO radar based on unitary root-MUSIC is presented in this article. In the presented method, a reduced-dimension matrix is first utilised to transform the high dimension of received signal data into low dimension one. Then, a low-dimension real-value covariance matrix is obtained by forward–backward (FB) averaging and unitary transformation. The DOA of targets can be achieved by unitary root-MUSIC. Due to the FB averaging of received signal data and the eigendecomposition of the real-valued matrix covariance, the proposed method owns better angle estimation performance and lower computational complexity. The simulation results of the proposed method are presented and the performances are investigated and discussed.     

多基线数据融合的双基地MIMO雷达角度估计

提出了一种双基地MIMO雷达角度估计方法。在发射端利用ESPRIT算法获取长短基线的旋转不变因子,通过联合对角化提高离开角度(DOD)的估计精度。根据已估计的DOD,采用迭代算法得到接收导向矢量。针对等距线阵和非等距线阵,分别提出了基于范德蒙矩阵特性和解模糊的多基线联合到达角度(DOA)估计方法,避免了两维搜索且角度自动配对。计算机仿真验证了该方法的有效性和优越性。     

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.     

机电扫描天线阵列接收信号调制效应分析

提出了一种新的单基地MIMO雷达波达方向(DOA)估计算法:降维Power-ESPRIT算法。该算法首先通过降维变换将MIMO雷达数据变换至低维信号空间,然后进行从复数域到实数域的转换,并在实数域上使用采样数据协方差矩阵的幂获得信号子空间的估计,最后构造实值旋转不变性方程估计目标的DOA。仿真结果表明,在低信噪比、低快拍数的环境下,该算法与已有ESPRIT方法相比,具有近似性能,却拥有较低的计算复杂度。该算法的计算复杂度是RD-ESPRIT的25%左右,是RD-UESPRIT的65%左右。     

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.     

Low complexity 3D-OMP algorithms for DOD DOA and Doppler frequency estimation in bistatic MIMO radar

The biggest challenge of the traditional 3D orthogonal matching pursuit (OMP) method for direction-of-departure (DOD), direction-of-arrival (DOA) and Doppler frequency estimation in bistatic multiple-input multiple-output (MIMO) radar is the heavy computational burden due to a large number of atoms in the overcomplete dictionary. In this paper, low complexity 3D-OMP algorithms are investigated. First, the traditional 3D-OMP algorithm is given. Then, two-dimensionality reduced OMP-based algorithms are proposed exploiting the property of Khatri-Rao product and proper sparse representation. Also, the multiple measurement vectors (MMV) model is introduced to our OMP algorithms to guarantee the estimation performance. The simulation results show that the DOD, DOA and Doppler frequency can be effectively estimated with a small number of pulses and low computation cost. With similar accuracy compared with the traditional 3D-OMP method, much lower computational burden can be achieved by using the proposed methods.     

基于降维子空间重构的双基地MIMO雷达角度估计算法

根据双基地MIMO雷达的工作原理和回波模型,结合阵列信号处理,提出了降维子空间重构的角度估计算法,并给出了算法有效性证明。理论分析表明,该算法可以有效解决二维空间搜索计算成本高,矩阵运算量大和低快拍数下算法适应性差的问题,并通过蒙特卡罗实验对不同信噪比、快拍数和接收阵元数等情况下角度估计的方差进行了仿真,证实了理论分析的正确性。     

Joint parameter estimation of DOD/DOA/polarization for bistatic MIMO radar

In the paper,polarization-sensitive array is exploited at the receiver of multiple input multiple output (MIMO) radar system,a novel method is proposed for joint estimation of direction of departure (DOD),direction of arrival (DOA) and polarization parameters for bistatic MIMO radars. A signal model of polarimetric MIMO radar is developed,and the multi-parameter estimation algorithm for target localization is described by exploiting polarization array processing and the invariance property in both transmitter array and receiver array. By making use of polarization diversity techniques,the proposed method has advantages over traditional localization algorithms for bistatic MIMO radar. Simulations show that the performance of DOD and DOA estimation is greatly enhanced when different states of polarization of echoes is fully utilized. Especially,when two targets are closely spaced and cannot be well separated in spatial domain,the estimation resolution of traditional algorithms will be greatly degraded. While the proposed algorithm can work well and achieve high-resolution identification and accurate localization of multiple targets.     

一种基于宽带MIMO雷达时域成像的阵列布阵模型

多输入多输出(MIMO)霄达足近几年发展起来的一种新慨念雷达体制.为了进一步降低宽带MIMO雷达的阵列规模和硬件复杂度,使MIMO雷达成像技术实用化成为可能,结合改进型后向投影(BP)成像算法,提出了一种非均匀线阵成像系统阵列布阵模型.提出的阵列布阵模型通过成像系统方位向分辨率婴求和目标方位向成像场景大小确定阵元间距,使得系统的阵列规模和硬件复杂度显著降低.同时,时域成像算法的使用避免了频域算法存在的采样定理限制,有效增强了阵列设计的灵活性,并使得成像阵列规模和硬件复杂度得到进一步降低.最后仿真实验验证了该成像系统布阵模型的正确性和有效性.     

Unfolded coprime bistatic MIMO radar for joint DOD and DOA estimation: from viewpoint on aperture augment for sum coarray

Generally, the coprime linear array (CLA) consisting of two interleaved uniform linear subarrays can enlarge the array aperture to attain the better angle estimation performance compared with the uniform linear array (ULA). In this paper, we ulteriorly study the virtual sum coarray of the unfolded coprime (UC) bistatic multiple-input multiple-output (MIMO) radar whose transmitter and receiver array are both unfolded CLA from the viewpoint on the geometry and array aperture. The UC MIMO radar can be exploited to obtain the better joint direction of departure (DOD) and direction of arrival (DOA) estimation performance due to the larger array aperture. Furthermore, we propose an all sum coarray multiple signals classification (ASCA-MUSIC) algorithm for the UC MIMO radar. ASCA-MUSIC can fully exploit all the degrees of freedom (DOFs) in the sum coarray and can obtain the better estimation performance. We also prove that ASCA-MUSIC can avoid the phase ambiguity problem due to the coprime property. In addition, we devise a reduced complexity scheme for ASCA-MUSIC to reduce the high computational complexity and utilize Cramer–Rao Bound (CRB) as a benchmark for the lower bound on the root-mean-square error (RMSE) of unbiased angle estimation. Finally, the numerical simulations verify the effectiveness and superiority of the UC MIMO radar, ASCA-MUSIC and the reduced complexity scheme.     

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation,against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.Then,estimation model was transformed from complex value to real value by using unitary transformation.In the next step,off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.The accuracy of estimation could made relatively high.The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm,but also reduces the computational complexity significantly.Under the situation of lower signal noise ratio (SNR) and low number of snapshots,the running time of algorithm is reduced about 50%.This shows the RV-OGSBL method is a rapid DOA estimation algorithm.     

基于原子范数最小化的单比特稀疏双极子阵列的波达角估计

现有的单比特稀疏双极子阵列的波达角估计方法为子空间方法,其估计精度依赖于信号的统计特征,并且没有充分利用协方差矩阵的结构,导致其估计精度较低。为了提高该阵列的波达角估计精度,本文提出了一种基于原子范数最小化的无网格稀疏化波达角估计方法。该方法将稀疏双极子阵列的波达角估计转化为标量阵波达角估计,并根据参数空间的连续性构造基于原子集的阵列信号稀疏模型,随后利用单比特采样下噪声的稀疏特征,将该波达角估计问题转化为相似文献   

压缩感知稀疏表示在雷达目标识别中的应用

高分辨距离像(HRRP)目标识别算法很多,在其利用高分辨距离像蕴含的目标结构信息的同时,也需要面对数据量巨大的难题.事实上,尽管高分辨距离像数据量巨大,但却是稀疏的,然而利用其稀疏特性进行识别的方法却不多.为此,提出了一种基于压缩感知稀疏表示方法实现目标识别的算法.该算法首先采用遗传正交匹配追踪(OMP)算法对一维距离像训练样本进行稀疏分解以获得类别字典,然后根据类别字典分析测试样本的重构误差实现目标识别.仿真实验证明,所提算法简捷、识别率更高,相较于常规算法识别率提高最多可达20％,并且在受到噪声干扰情况下依然能够稳健地识别目标.     

一种性价比高的TDM MIMO雷达系统设计和实验

基于大量使用昂贵的收发组件,传统的具有M阵元发射阵列和N阵元接收阵列的相干多输入多输出(MIMO)雷达系统实际应用受高成本的限制,文中提出一种性价比高的时分复用(TDM) MIMO雷达系统设计。首先,建立TDM MIMO雷达的收发结构模型;其次,从理论上对距离估计和角度估计进行了仿真比较,将TDM MIMO雷达与单输入多输出雷达通过实验对比验证;最后,结果证实相比于相同阵列的传统MIMO雷达,TDM MIMO雷达不仅目标波达方向性能未受影响,而且以较低的成本实现较高的角度分辨率。     

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.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

This paper proposes low-cost yet high-accuracy direction of arrival (DOA) estimation for the automotive frequency-modulated continuous-wave (FMCW) radar. The existing subspace-based DOA estimation algorithms suffer from either high computational costs or low accuracy. We aim to solve such contradictory relation between complexity and accuracy by using randomized matrix approximation. Specifically, we apply an easily-interpretable randomized low-rank approximation to the covariance matrix (CM) and approximately compute its subspaces. That is, we first approximate CM \begin{document}${\bf{R} }\in {\mathbb{C}}^{M\times M} $\end{document} through three sketch matrices, in the form of \begin{document}$\mathbf{R}\approx \mathbf{Q}\mathbf{B}{\mathbf{Q}}^{\mathrm{H}} $\end{document}. Here the matrix \begin{document}$\mathbf{Q}\in {\mathbb{C}}^{M\times z} $\end{document} contains the orthonormal basis for the range of the sketch matrix \begin{document}$\mathbf{C}\in {\mathbb{C}}^{M\times z} $\end{document} which is extracted from \begin{document}$ \mathbf{R} $\end{document} using randomized uniform column sampling and \begin{document}$ \mathbf{B}\in {\mathbb{C}}^{z\times z} $\end{document} is a weight-matrix reducing the approximation error. Relying on such approximation, we are able to accelerate the subspace computation by the orders of the magnitude without compromising estimation accuracy. Furthermore, we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation. As validated by the simulation results, the DOA estimation accuracy of the proposed algorithm, efficient multiple signal classification (E-MUSIC), is high, closely tracks standard MUSIC, and outperforms the well-known algorithms with tremendously reduced time complexity. Thus, the devised method can realize high-resolution real-time target detection in the emerging multiple input and multiple output (MIMO) automotive radar systems.     

An ESPRIT-like algorithm for coherent DOA estimation based on data matrix decomposition in MIMO radar

A direction-of-arrival (DOA) estimation method for coherent sources is presented for MIMO radar. It uses symmetrical array mode for both the transmit and receive arrays and reconstructs a special data matrix from the range-compressed receive data. In the reconstructed matrix, the signal term is a Toeplitz matrix with the rank only related to the DOAs of the signals and independent with their coherency. Taking the noise term into account, the average method of multiple pulses is utilized to obtain the signal and noise subspaces. And then the DOA can be resolved via the SVD-based ESPRIT algorithm. Furthermore, the presented method is also useful in spatial colored noise scenario for MIMO radar. Theoretical and numerical simulations show the effectiveness of the proposed algorithm.