Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

NON-UNIFORM LINEAR ARRAY CONFIGURATION FOR MIMO RADAR

Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.

基于对抗自编码填补网络的MIMO雷达故障阵元缺失数据重构

多输入多输出(Multiple In Multiple Out,MIMO)雷达的阵元故障会导致其协方差矩阵出现整行整列数据缺失,从而降低其角度估计性能。为此,提出一种对抗自编码填补网络(Adversarial Autoencoder Imputation Network,AAEIN)来重构故障阵元的缺失数据。该网络由负责重构缺失数据的自动编码(Autoencoder,AE)网络和负责分辨数据来源的鉴别器组成。在二者的对抗训练中,AE网络的重构能力和鉴别器的分辨能力不断得到提升,直至两者收敛。为避免网络训练过程中参数量大和计算复杂度高的问题,文中结合MIMO雷达协方差矩阵的Hermitian特性,使用协方差矩阵上三角部分构建数据集用于网络训练。仿真结果表明,文中方法可以有效地重构故障阵元的缺失数据且具有较高的重构精度。

一种动平台MIMO雷达对海广域探测波束锐化方法

为解决动平台相控阵雷达前视成像方位分辨率低的问题,文中利用多输入多输出(Multiple Input Multiple Output,MIMO)雷达角度分辨力的优势,给出了一种适用于广域宽角扫描的机载MIMO雷达波束锐化方法。首先,根据MIMO雷达虚拟阵列原理,在宽发窄收和同时多波束探测模式下,建立了子阵级MIMO雷达前视成像回波模型;然后,在贝叶斯准则下,结合噪声统计先验信息,给出了基于最大似然估计的波束锐化方法。仿真和数据分析发现,该方法在较宽的角域内均可以获得良好的波束锐化性能,同时又极大节省了时间资源,具有一定的工程应用价值。

一种基于稀疏重构的多视角MIMO雷达关联成像算法研究

为改善复杂目标的成像表现、提升雷达图像的视觉效果,提出一种基于稀疏重构的多视角多输入多输出(multiple input multiple output,MIMO)雷达关联成像方法,设计了多视角雷达散射面积(radar cross section,RCS)起伏的稀疏重构关联成像模型。所提算法能提高辐射场信号随机性,改善复杂目标雷达关联成像参考信号与回波之间相关性退化的情况,减小多视角RCS起伏对相关性的影响,提升多视角下的关联成像质量。最后,通过仿真验证了所提方法的有效性。

MIMO雷达分组正交波形集优化设计方法

在现代电子对抗中,数字射频存储(DRFM)设备能够快速截获机载脉冲多普勒雷达信号,能够实现对多输入多输出(MIMO)雷达的干扰。MIMO雷达可基于多组相互正交的波形集来对抗DRFM干扰。同时,为最大化MIMO雷达波形分集增益,每个脉冲内发射的波形也需要正交。为了平衡组内和组间的正交性,文中建立了一种分组正交波形集优化模型,其目标函数为组内和组间相关函数性能评估指标值的加权和;为了求解该优化问题,提出了一种分组正交波形集设计方法。所提方法将原优化问题简化为p-范数优化问题,基于MM算法导出了最小化目标函数的迭代求解表达式。仿真结果表明,所提方法可通过改变权重系数来灵活平衡MIMO雷达的干扰抑制性能和距离压缩性能。

距离角度失配条件下的FDA-MIMO雷达运动目标检测算法

频控阵-多输入多输出(Frequency Diverse Array-Multiple Input Multiple Output,FDA-MIMO)雷达是一种新体制雷达,其发射频率分集特性带来了额外的距离维信息,然而采样误差同样带来了导向矢量失配的问题,不仅如此,角度误差的存在也会进一步加重导向矢量的失配,极大地影响检测器的检测性能。此外,目标速度过快也会对FDA-MIMO雷达的目标检测性能产生影响。速度带来的影响具体表现在两个方面:一方面会导致目标的距离走动,从而导致不同慢时间的回波包络不能对齐,无法相干积累;二是频率增量引起的多普勒扩展,使得不同发射通道的多普勒频率不一样,这会进一步影响检测性能。针对上述问题,本文针对运动目标情况下的目标检测问题进行研究,为了解决目标运动带来的距离徙动和多普勒扩展效应,引入Keystone变换进行校正。此外,为了提升阵列失配条件下的目标检测性能,本文引入子空间模型,提出了距离角度失配情况下的子空间构建方法,并基于广义似然比检验(Generalized Likelihood Ratio Test,GLRT)准则推导了FDA-MIMO雷达在距离和角度失配条件下的自适应检测器。仿真结果表明:在高斯白噪声背景下,所提算法可以校正运动目标在速度较快情况下导致的距离徙动和多普勒扩展效应,且在阵列距离和角度失配条件下的检测性能优于传统的GLRT检测器。此外本文所提Keystone-空域处理检测器与Keystone-全空时处理检测器的性能接近,且计算复杂度更低。

基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计

多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算法要求缺失数据随机分布于不完整的矩阵中,无法适用于整行缺失数据的恢复问题。为此,提出了一种基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计方法。首先,通过奇异值分解(Singular Value Decomposition,SVD)降低虚拟阵列输出矩阵的维度,以减少计算复杂度。然后,对降维数据矩阵建立基于块Hankel矩阵正则化的低秩矩阵填充模型,在该模型中将MIMO雷达降维数据矩阵排列成块Hankel矩阵并施加Schatten-p范数作为正则项。最后,结合交替方向乘子法(Alternate Direction Multiplier Method,ADMM)求解该模型,获得完整的MIMO雷达降维数据矩阵。仿真结果表明,所提方法能够有效恢复降维数据矩阵中的整行数据缺失,具有较高的DOA估计精度和实时性,在阵元故障率低于50.0%时DOA估计精度优于现有方法。

Design of integrated radar and communication system based on MIMO-OFDM waveform

Orthogonal frequency division multiplexing (OFDM) waveform enables radar and communication functions simultaneously, which encounters low angle resolution and poor data rate for traditional single input single output (SISO) systems. To solve these problems, an integrated radar and communication system (IRCS) with multiple input multiple output (MIMO) OFDM waveform is proposed. The different limitations of radar and communication in designing such a system are investigated. Then, an optimization problem is devised to obtain suitable system parameters, including the number of subcarriers, subcarrier spacing, number of symbols, pulse repetition frequency (PRF) and length of cyclic prefix (CP). Finally, to satisfy the requirements of both radar and communication, the IRCS parameters are derived in three typical cases. Several numerical results are presented to illustrate the demands of radar and communication, inconsistent or consistent, for the IRCS parameters and the superiority of the proposed system.

重叠阵元时分复用MIMO雷达多目标参数估计方法研究

针对多普勒-角度耦合和速度模糊问题,该文提出一种基于重叠阵元MIMO阵列的多目标参数估计方法。该方法基于虚拟孔径原理,在传统MIMO天线阵列中引入重叠阵元,构建重叠阵元MIMO天线阵列。通过在角度快速傅里叶变换(FFT)算法中引入循环迭代的方法估计阵列位置参数,利用重叠阵元回波信号的相位差值进行频率估计。同时,引入频谱搬移方法对速度区间进行转换,实现多目标的距离和速度估计。通过蒙特卡罗仿真实验,信噪比15 dB的条件下,解模糊正确率为100%,速度误差为0.1 m/s,角度误差为0.1°。基于城市交通场景采集的车辆数据集进行测试,测试结果表明,该方法能够实现对车辆目标的速度和角度精确估计,可满足交通雷达对车辆信息监测的实时性和准确性需求。

Angle estimation in bistatic MIMO radar using improved reduced dimension Capon algorithm

This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.

Angle estimation for bistatic MIMO radar with unknown mutual coupling based on three-way compressive sensing

The problem of angle estimation for bistatic multiple-input multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two over-complete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions. © 2017 Beijing Institute of Aerospace Information.

基于混沌序列的MIMO-OFDM雷达通信一体化波形设计

为了解决多输入多输出的正交频分复用(multiple input multiple output orthogonal frequency division multiplexing,MIMO-OFDM)雷达通信一体化信号波形由于通信信息的不确定性导致模糊函数旁瓣水平较高以及系统雷达探测能力较差的问题,提出利用混沌序列对通信信息进行直接扩频。但普通单级混沌序列具有保密性低,可用数量少,容易被破解的缺点。因此采用一种基于加权算法优化的混合型级联混沌序列对通信信息进行扩频调制,然后再通过MIMO-OFDM一体化系统进行传输。由仿真结果可知,加权后的级联混沌序列相关性和误码率性能较m序列和Gold序列更好,经此序列扩频后的一体化信号模糊函数旁瓣水平会更低,故该一体化波形雷达探测性能更佳。且混沌序列越长,信号波形雷达探测能力越好。

Polyphase coded signal design for MIMO radar using MO-MicPSO

A novel modified optimization technique known as the multi-objective micro particle swarm optimization（MO-MicPSO） is proposed for polyphase coded signal design.The proposed MO-MicPSO requires only a small population size compared with the standard particle swarm optimization that uses a larger population size.This new method is guided by an elite archive to finish the multi-objective optimization.The orthogonal polyphase coded signal（OPCS） can fundamentally improve the multiple input multiple output（MIMO） radar system performance,with which the radar system has high resolution and abundant signal channels.Simulation results on the polyphase coded signal design show that the MO-MicPSO can perform quite well for this high-dimensional multi-objective optimized problem.Compared with particle swarm optimization or genetic algorithm,the proposed MO-MicPSO has a better optimized efficiency and less time consumption.

Non-fluctuating target detection in low-grazing angle with MIMO radar

The non-fluctuating target detection in low-grazing angle using multiple-input multiple-output(MIMO) radar systems was studied, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced for non-fluctuating target in low-grazing angle. Using the generalized likelihood ratio test(GLRT) criterion, the detector of non-fluctuating target with multipath was analyzed. The simulation results demonstrate that the MIMO radar outperforms the conventional radar in non-fluctuating target detection and show that the performance can be enhanced markedly when the multipath effects are considered.

MIMO radar dwell scheduling based on novel pulse interleaving technique

According to the signal processing characteristic of MIMO radars,an adaptive dwell scheduling algorithm is proposed.It is based on a novel pulse interleaving technique,which makes full use of transmitting,waiting and receiving durations of radar dwells.The utilization of transmitting duration is unique for MIMO radars and is realized through transmitting duration overlapping.Simulation results show that,compared with the conventional scheduling algorithm,the scheduling performance of MIMO radars can be improved effectively by the proposed algorithm,and the scheduling rule can be chosen arbitrarily when using the proposed algorithm.

Tracking multiple targets in MIMO radar via adaptive asymmetric joint diagonalization with deflation

In view of the low performance of adaptive asymmetric joint diagonalization（AAJD）, especially its failure in tracking high maneuvering targets, an adaptive asymmetric joint diagonalization with deflation（AAJDd） algorithm is proposed. The AAJDd algorithm improves performance by estimating the direction of departure（DOD） and direction of arrival（DOA） directly, avoiding the reuse of the previous moment information in the AAJD algorithm.On this basis, the idea of sequential estimation of the principal component is introduced to turn the matrix operation into a constant operation, reducing the amount of computation and speeding up the convergence. Meanwhile, the eigenvalue is obtained, which can be used to estimate the number of targets. Then, the estimation of signal parameters via rotational invariance technique（ESPRIT） algorithm is improved to realize the automatic matching and association of DOD and DOA. The simulation results show that the AAJDd algorithm has higher tracking performance than the AAJD algorithm, especially when the high maneuvering target is tracked. The efficiency of the proposed method is verified.

Fast multi-parameter estimation and localization for MIMO radar

This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output （MIMO） radar with arbitrary arrays in spatial co- lored noise. A novel method for joint estimation of Doppler fre- quency, two-dimensional （2D） direction of departure and 2D direc- tion of arrival based on the propagator method （PM） for arbitrary arrays is discussed. A special matrix is constructed to eliminate the influence of spatial colored noise. The four-dimensional （4D） angle and Doppler frequency are extracted from the matrix and the three- dimensional （3D） coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix al- gorithm in the high signal to noise ratio and the Cramer-Rao bound （CRB） is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorithm to avoid performance degra- dation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method.

Detection of weak target for MIMO radar based on Hough transform

An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals （CPIs）. Within each CPI, conventional methods such as fast Fourier transform （FFT） is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved.

Cramer-Rao bound of joint estimation of target location and velocity for coherent MIMO radar

The optimal estimation performance of target parameters is studied. First, the general form of Cramer-Rao bound （CRB） for joint estimation of target location and velocity is derived for coherent multiple input multiple output （MIMO） radars. To gain some insight into the behavior of the CRB, the CRB with a set of given orthogonal waveforms is studied as a specific case. Second, a maximum likelihood （ML） estimation algorithm is proposed. The mean square error （MSE） of the ML estimation of target location and velocity is obtained by Monte Carlo simulation and it approaches CRB in the high signal-to-noise ratio （SNR） region.