Computationally Efficient MUSIC-Based Algorithm for Joint Direction of Arrival (DOA) and Doppler Frequency Estimation in Monostatic MIMO Radar

The problem of joint direction of arrival (DOA) and Doppler frequency estimation in monostatic multiple-input multiple-output (MIMO) radar is studied and a computationally efficient multiple signal classification (CE-MUSIC) algorithm is proposed.Conventional MUSIC algorithm for joint DOA and Doppler frequency estimation requires a large computational cost due to the two dimensional (2D) spectral peak searching.Aiming at this shortcoming,the proposed CE-MUSIC algorithm firstly uses a reduced-dimension transformation to reduce the subspace dimension and then obtains the estimates of DOA and Doppler frequency with only one-dimensional (1D) search.The proposed CE-MUSIC algorithm has much lower computational complexity and very close estimation performance when compared to conventional 2D-MUSIC algorithm.Furthermore,it outperforms estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm.Meanwhile,the mean squared error (MSE) and Cramer-Rao bound (CRB) of joint DOA and Doppler frequency estimation are derived.Detailed simulation results illustrate the validity and improvement of the proposed algorithm.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.

Estimation of DOA and Doppler Frequency on Nonideal UCA

The problem of estimating direction of arrivals (DOA) and Doppler frequency for many sources is considered in the presence of general array errors (such as amplitude and phase error of sensors, setting position error of sensors). Adopting direct array manifold in a uniform circular array (UCA), the estimation of Doppler frequency can be obtained by DOA matrix. Based on analyzing the statistic characters of general array errors, the estimation of DOA can be obtained by Weight Total Least Squares. Numerical results illustrate that the estimator is robust to general array errors and show the capabilities of the estimator.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

L型阵水下目标方位和距离联合最大似然估计

空间目标定位是阵列信号处理领域中的研究热点,广泛应用于雷达、声纳、通信、射电天文、医学诊断、地震遥感等众多领域。传统的水下声源定位仅获取目标声源的深度和距离信息,却无法估计目标的方位信息。针对这一问题,在充分考虑水下声传播模型的基础上,利用L型阵列固有的结构特征,将最大似然估计技术应用到水下目标定位,提出一种基于L型水听器阵列的水下目标方位与距离联合最大似然估计算法。研究表明:最大似然估计法可有效突破传统波束形成分辨力“瑞利限”的限制,提高水下目标定位的分辨能力和精度。计算机仿真分析显示,在单目标和相干双目标的场景中,文中所提出的方法均具有更为尖锐的谱峰及更低的旁瓣水平,可对水下目标进行有效的方位角和距离联合估计。

一种基于OFDM-chirp的雷达通信一体化波形设计与处理方法

雷达通信一体化波形设计是近年来的研究热点。有学者提出利用正交频分复用(OFDM)信号的奇偶载波分别调制雷达与通信功能来实现一体化。但OFDM通信系统一般采用循环前缀(CP)来避免多径效应带来的载波间干扰(ICI)和符号间干扰(ISI),这会降低能量利用率,并会形成虚假目标,影响雷达性能;此外,传统的OFDM一体化信号对多普勒比较敏感,微小的多普勒频偏也会带来正交性能的严重下降。该文在此基础上提出了一种新的波形设计和处理方法。该方法利用空白保护间隔替代循环前缀,可以在对抗多径效应的同时避免出现由于循环前缀引入的虚假目标,有效防止载波间干扰和符号间干扰。在信号处理方法上,该文提出利用雷达发射信号的先验信息进行信道估计与补偿多普勒频偏的方法。与传统方法相比,该文方法降低了系统在导频与训练序列等资源上的开销,提升了能量利用率和频谱效率,并且改善了峰值旁瓣比(PSLR)、积分旁瓣比(ISLR)和通信误码率(BER)等指标。仿真实验验证了该文方法的有效性。

基于稀疏阵列的多带信号频率和到达角联合估计

随着工作频段的不断拓展,传统的基于空时Nyquist采样的多目标源频率和到达角联合估计方法受到阵列孔径和采样速率的约束,提高估计精度往往伴随着较高的硬件成本。为此,基于互质阵列设计了一种十字型空时欠采样接收结构,并结合噪声空间重构、信源相关性配对,提出了一种具备闭式解的频率和到达角的无模糊估计方法。得益于稀疏阵列可有效拓展阵列孔径,所提方法相比基于传统均匀线阵的方法,在相同的硬件资源和较低信噪比条件下即可取得明显的估计精度提升效果。

A NEW ESPRIT METHOD FOR BLIND ESTIMATES OF DOA-DOPPLER FREQUENCY WITH UNKNOWN ARRAY MANIFOLD

A novel rotational invariance technique for blind estimates of direction of arrival (I)OA) and Doppler frequency with unknown array manifold due to array sensor uncertainties is proposed, taking Doppler frequency difference between a successive pulses as rotational parameter. The effectiveness of the new method is confirmed by computer simulation. Compared with the existing 2-D DOA-frequeucy estimate techniques, the computation load of the proposed method can be saved greatly.

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather radar.This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter.After data preprocessing,the radar data should be classified according to the precipitation intensity.And then,they are respectively substituted into the improved filter for calibration.The state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this process.Then the optimal parameter value of each type of precipitation intensity can be obtained.The state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining data.This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of calculation.It avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time.

基于金枪鱼算法的最大似然信号方位与频率联合估计

针对多维非线性搜索在传统最大似然估计(ML)上的波达方向(DOA)估计存在计算量大、传统网格搜索速度慢等不足,提出了一种基于金枪鱼算法的窄带信号频率方位联合估计的最大似然DOA估计,该算法体现了良好的估计性能。仿真结果表明,该算法对单声源有精确的角度和频率估计值;在声源个数不同的情况下,与基于松鼠搜索算法、正余弦算法、原子搜索优化算法、人工蜂群算法等的估计相比,该算法有着更快的收敛速度和更低的均方根相对误差。

基于多普勒域补偿的车载雷达距离角度联合成像算法

高性能、高分辨率单快拍前视成像技术是赋能车载雷达发展的关键,但距离/多普勒走动问题会限制相干积分的实施,同时系统分辨率也往往受限于硬件参数难以提高。根据车载毫米波雷达时分多输入多输出(TDMMIMO)的前视成像体制,该文提出多普勒域补偿和点对点回波校正方法,完成多域信号解耦合,同时完成距离多普勒走动校正和多普勒解模糊。由于有限阵元数及强噪声干扰限制了传统单维度距离角度成像准确性,因此,该文提出一种基于改进贝叶斯匹配追踪方法(IBMP)的多域联合估计算法。该方法基于伯努利-高斯(BG)模型,在最大后验(MAP)准则约束下迭代更新估计参数和支撑域,实现了多维联合信号的高精度重构。仿真和实测结果表明该文方法能够有效解决距离走动问题,并提高雷达前视成像的角度分辨率,具有较强噪声鲁棒性。

基于L型级联互素阵列的多目标源载频和到达角联合估计

随着通信频段的不断拓展,受到Nyquist意义下的阵列间隔和采样频率的限制,多带信号载频和到达角的联合估计难度急剧增大。为突破该瓶颈,设计了一种基于L型级联互素阵列的空时欠采样接收结构,并结合伪噪声空间重构、信源相关性配对,提出了一种全解析的载频和到达角的配对和无模糊联合估计方法。得益于稀疏阵列和时域欠采样,所提结构有效增大了阵列孔径并降低了采样成本。仿真实验证明了所提载频和到达角联合估计方法在较低快拍数和信噪比的条件下,可取得较高的估计精度。

基于DOA估计的前视多通道SAR成像方法

针对前视合成孔径雷达(synthetic aperture radar,SAR)方位分辨受限以及左右多普勒模糊问题,提出一种基于波达方向估计的前视多通道合成孔径雷达(forward-looking multi-channel SAR,FLMC-SAR)成像方法。算法综合利用系统空时资源,采用空时级联的处理流程。首先通过合成孔径对前视区域进行初步成像,在二维图像域实现逐个成像单元目标信号的分离。然后利用目标空域导向的差异,建立分离信号的空时模型,通过阵列多通道对每个成像单元内的信号进行波达方向估计,实现目标信号重定位,增强前视成像分辨率。同时,空时模型考虑了模糊目标具备不同的导向矢量,可在分辨增强的同时实现左右多普勒解模糊。最后通过仿真以及实测的FLMC-SAR数据验证了所提算法的有效性。

基于载波变频稀疏采样与阵列移动的TOA-DOA联合估计

文中提出了一种利用少量传感器和少量快拍实现多目标波达时间(Time of Arrival,TOA)和波达方向(Direction Of Arrival,DOA)联合估计的方法。该方法设计了一种基于移动平台的完全稀疏阵列,保证不同时间的阵列位置合成后不存在重叠,并且通过差分变换可以得到更大的阵列孔径。同时,考虑了载波频率变换场景下一种新的稀疏采样方法,可以利用少量的快拍得到大量的虚拟数据,进而能够实现小快拍条件下TOA/DOA的精确估计。仿真结果表明,与传统的均匀线性阵列结合均匀采样的方式相比,文中所提方法能够识别更多的目标。同时,该方法的估计精度优于基于固定平台的嵌套阵列结合嵌套采样的方式。

多跳频信号波达方向与极化状态联合估计算法

为了有效辅助跳频(FH)网台分选和信号识别、跟踪,该文用正交偶极子对构造极化敏感阵列,基于空间极化时频分析,在欠定条件下实现了多跳频信号波达方向(Direction Of Arrival,DOA)与极化状态的高效联合估计。首先建立跳频信号的极化敏感阵列观察模型,然后根据参考阵元时频分析结果建立各跳信号的空间极化时频分布矩阵,再利用该矩阵中蕴含的信号极化-空域特征信息分别运用线性、二次型空间极化时频以及多项式求根共3种方法实现DOA与极化参数联合估计,最后蒙特卡罗仿真结果验证了该算法的有效性。

一种波束内目标与诱饵DOA联合估计新方法

拖曳式诱饵干扰下目标和诱饵DOA参数的准确估计是导引头实现目标分选的前提条件。波束内目标和诱饵的存在引起雷达回波混叠和观测耦合,导致常规观测提取与参数测量方法失效。通过挖掘雷达接收回波和、差通道信号条件概率分布协方差所包含的未知参数信息,获得了目标和诱饵DOA估计的解析表达式,针对估计求解中所需相对功率比未知的情况,提出了基于干扰检测、雷达测量与目标跟踪信息辅助循环估计相对功率比的DOA二次联合估计方法。不同干扰条件的仿真实验验证了估计方法的有效性。

一种水下多目标方位、频率、距离联合估计新方法

本文提出了一种可在单次回波内同时完成多目标方位、频率、距离 (时延 )三维参数联合估计的高分辨新方法 ,它借用ESPRIT方法的思路构造两个空间子阵 ,通过对其自相关矩阵和互相关矩阵的广义特征分解 ,用特征值估计多目标方位 ,用特征向量估计多目标回波的频率 ,再用估计得到的频率去修正包络时延向量进而得到目标时延估计 .三维参数的配对由特征向量和特征值的对应关系自动完成 .计算机仿真结果表明 ,该方法在较低的信噪比条件下可以实现三维参数的高分辨联合估计 ,而且无需额外的配对算法 .作者还进行了多目标三维参数联合估计的消声水池实验 ,也取得了较好的结果 ,表明该方法对阵列误差有较好的稳健性 ,具有良好的工程应用前景 .

一种MIMO雷达二维DOA估计新方法

提出了一种基于立体阵的多输入多输出(MI MO:Multiple Input Multiple Output)雷达二维波达方向(DOA:Direction Of Arrival)估计新方法,该方法利用MI MO雷达形成的虚拟阵列数据来构造两个二阶统计量并对其进行联合对角化得到了目标方位角和俯仰角的闭式解。该方法不仅很好地解决了存在一维角度兼并时目标的二维DOA估计问题,而且无需二维谱峰搜索和参数配对即可实现对目标DOA的全方位估计。计算机仿真结果证明了所提方法的正确性和可行性。

多普勒方位联合估计的蒙特卡洛算法

目标回波的多普勒和方位参数能体现目标的重要特征.本文从参数的最大似然估计出发,采用非线性来放大似然函数并构造一个新的代价函数,该函数能更明显区分局部和全局最佳值.针对新代价函数的复杂形式,我们提出了基于蒙特卡洛的重要性采样方法来求解最佳值,并给出了重要性函数的一种选取方法.该方法不仅在低信噪比和小样本数条件下性能良好,而且各个参数自动配对并适用于任意的已知阵型.与已有的基于子空间的联合估计算法的仿真比较表明,本文提出的算法在性能上非常接近参数估计的CRLB.

非理想阵列的信号波达方向和多普勒频率估计

在均匀线阵存在误差的情况下 ,提出了一种信号波达方向和多普勒频率的估计方法。此方法在空间域 ,不仅考虑了阵元幅相误差 ,而且考虑了阵元位置误差 ,使包括幅相误差和位置误差在内的误差矩阵达到Frobe nius范数的最小值。在时间域考虑了测量噪声 ,使得多普勒频率估计的似然概率达到最大。仿真结果表明了此算法的有效性。