发射分集MIMO雷达融合成像处理方法

发射分集MIMO雷达具有潜在的高分辨成像能力,但成像时存在高旁瓣问题。为分析它的成像性能,该文首先建立了发射分集MIMO雷达的成像模型。通过空间频谱的支撑区分布,阐明了分集成像的高分辨性能,并着重分析了其旁瓣特性。基于谱外推的思想,提出了一种发射分集MIMO雷达融合成像处理方法,该方法能够实现MIMO雷达高分辨成像同时具有良好的旁瓣性能。仿真实验验证了该文所提方法的有效性。     

MIMO雷达成像外场实验研究

以MIMO雷达的成像性能为研究对象,本文首先探讨了MIMO雷达的成像原理,论证了MIMO雷达的高分辨成像能力.而后构建了MIMO雷达成像实验系统,利用波束形成聚焦成像的方法对外场实验数据进行了处理,初步验证了MIMO雷达优良的成像性能.最后的成像结果表明:MIMO雷达的方位分辨率较同等接收阵列的实孔径方式提高约158%,同时旁瓣电平比实孔径方式降低约3dB.     

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.     

地基MIMO雷达快速成像算法研究

地基多输入多输出(Multiple-Input Multiple-Output, MIMO)成像雷达采用数字波束形成技术实现二维成像,具有成像速度快的技术优势。本文提出了一种适用于大景深、宽视角场景的地基MIMO雷达的快速二维成像算法。首先,根据MIMO雷达的阵列构型建立回波信号模型;其次,基于该模型补偿天线阵列近场空变性的相位中心近似(Phase Center Approximation, PCA)误差并进行数据重排,通过Kesytone变换校正距离徙动;然后,利用方位分块Dechirp处理实现方位向聚焦,实现MIMO雷达二维成像;最后利用MIMO雷达外场实验数据完成了算法验证。研究结果表明:在保证成像精度要求的情况下,该算法可以实现大景深、宽视角场景的高分辨快速成像,成像处理时间优于反向投影成像(Back Projection, BP)算法。      

基于空间谱域填充的MIMO雷达成像研究

 雷达成像需要获得目标空间谱域一定范围和密度的采样以重建目标图像,目标空间谱域的填充方式与雷达成像的质量有直接关系.论文首先阐述了MIMO(Multiple-Input Multiple-Output)雷达收发信号模型的空间卷积效应,基于电磁逆散射原理,推导出一种MIMO雷达成像方法,详细论述了成像性能与空间谱域填充的关系.讨论了收/发阵列配置对空间谱域填充的影响,得到阵列排布的基本准则,最后对成像算法的有效性和理论分析结果进行仿真验证.     

MIMO雷达信号模型

介绍了为改善雷达性能的一种新概念雷达模型,即多输入多输出(MIMO)雷达,他利用了目标雷达截面积(RCS)在方位上的闪烁。描述了多功能数字阵列雷达如何在常规和MIMO模式下工作的情况。用数字阵列实现强聚焦发射波束(如用于跟踪)和宽的发射照射波束(如用于搜索)。最后试验验证了全景MIMO阵列的优势性能,并给出了MIMO雷达的发展前景。     

时分MIMO滑坡雷达稀疏成像算法

针对现用于成像的MIMO山体滑坡雷达均匀线性阵列数目过多、数据处理复杂度高的问题，引入稀疏阵列时分地基MIMO雷达模型，提出一种基于逆傅里叶变换和混合匹配追踪算法的成像方法。首先通过对雷达回波信号作逆傅里叶变换实现距离向压缩，并进行近似相位补偿，然后采用一种基于时延补偿因子稀疏基的压缩感知算法实现方位向压缩。同时针对多目标成像的伪影点问题，方位向数据压缩引入子空间追踪算法和正交匹配追踪算法的结合算法重构出高分辨率且没有伪影的二维图像。根据真实的山体滑坡监测成像场景参数，通过数值仿真验证了该方法能够在低于传统均匀阵列的天线数目情况下实现目标高质量成像，且具有一定的抗噪性。     

UWB MIMO近场成像雷达稀疏阵列设计方法研究

传统方法设计近场高分辨成像雷达的阵列时,往往需要大量且分布密集的阵元,这样的设计会带来巨大的开销和制作工艺要求高等问题。为了避免上述问题,本论文将MIMO与UWB相结合,提出了一种基于等效相位中心原理设计UWB MIMO近场成像雷达稀疏阵列的方法,并对等效相位中心近似带来的误差做了补偿。然后通过数值仿真工具(FDTD)进行仿真,考察了设计的阵列的波束方向图,并分别对单个点目标和分布式目标进行成像,仿真结果验证了该阵列设计方法的有效性和应用于近场高分辨成像的可行性。     

基于压缩感知的稀疏阵列MIMO雷达成像方法

针对MIMO雷达对空目标单次快拍成像时天线数目较多问题,该文提出了一种稀疏阵列MIMO雷达的成像方法。首先分析了MIMO雷达天线的稀疏布阵方式,其次结合压缩感知理论具体阐述了稀疏阵列MIMO雷达的成像方法。该方法不仅能够对运动目标实现单次快拍成像,避免了目标机动带来的运动补偿难题,同时又能够大幅减少MIMO雷达的天线规模,便于工程实现。最后利用仿真实验验证了所提方法的有效性。     

340 GHz稀疏MIMO阵列实时3-D成像系统

介绍了一种基于稀疏MIMO(Multiple Input and Multiple Output)阵列的340 GHz三维成像系统.系统采用水平放置的4发16收稀疏MIMO阵列配合垂直维的聚束扫描实现方位维的高分辨成像,每个发射通道的波形为16GHz带宽的线性调频连续波信号,通过脉冲压缩实现距离维的高分辨.测试结果表明,在4 m的探测距离(光程)上,成像系统方位向、垂直向和距离向的分辨率分别达到14 mm、10 mm和12 mm,通过对人体隐藏枪支的三维成像实验验证了系统对人体携带危险品的探测能力.     

MIMO阵列波束形成方法研究

相控阵雷达无法分辨一个波束宽度内的2个目标,多输入多输出(Multi-Input Multi-Output,MIMO)雷达阵列各阵元发射正交信号,提高了阵列系统的自由度,改善了雷达阵列系统分辨性能。文中给出了MIMO阵列相关滤波器组分离目标回波信号的原理框图,并进行了MIMO阵列常规波束形成器和最小方差无失真响应(MVDR)波束形成器理论分析和仿真实验。仿真结果表明,MIMO阵列比常规相控阵列具有更高的角度分辨率。     

Spatial diversity in radars-models and detection performance

Inspired by recent advances in multiple-input multiple-output (MIMO) communications, this proposal introduces the statistical MIMO radar concept. To the authors' knowledge, this is the first time that the statistical MIMO is being proposed for radar. The fundamental difference between statistical MIMO and other radar array systems is that the latter seek to maximize the coherent processing gain, while statistical MIMO radar capitalizes on the diversity of target scattering to improve radar performance. Coherent processing is made possible by highly correlated signals at the receiver array, whereas in statistical MIMO radar, the signals received by the array elements are uncorrelated. Radar targets generally consist of many small elemental scatterers that are fused by the radar waveform and the processing at the receiver, to result in echoes with fluctuating amplitude and phase. It is well known that in conventional radar, slow fluctuations of the target radar cross section (RCS) result in target fades that degrade radar performance. By spacing the antenna elements at the transmitter and at the receiver such that the target angular spread is manifested, the MIMO radar can exploit the spatial diversity of target scatterers opening the way to a variety of new techniques that can improve radar performance. This paper focuses on the application of the target spatial diversity to improve detection performance. The optimal detector in the Neyman-Pearson sense is developed and analyzed for the statistical MIMO radar. It is shown that the optimal detector consists of noncoherent processing of the receiver sensors' outputs and that for cases of practical interest, detection performance is superior to that obtained through coherent processing. An optimal detector invariant to the signal and noise levels is also developed and analyzed. In this case as well, statistical MIMO radar provides great improvements over other types of array radars.     

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.     

基于空域稀疏性的嵌套MIMO雷达DOA估计算法

针对传统MIMO雷达可分辨目标数受限于虚拟阵元数的问题,该文提出一种基于嵌套阵的MIMO雷达阵形设计新方法并改进了相应的稀疏DOA估计算法。首先分析对传统MIMO雷达的虚拟阵元进行嵌套采样给DOA估计性能带来的影响;然后提出嵌套MIMO雷达阵形设计方法,在虚拟阵元数相同的情况下,该阵形比传统阵形分辨更多的目标;最后提出一种基于空域稀疏性的嵌套MIMO雷达改进DOA估计算法,该算法使用噪声子空间加权,在提高分辨率的同时可以有效消除伪峰。仿真结果验证了该文算法的有效性和优越性。     

地基双基地MIMO成像雷达空间分辨特性分析

地基MIMO（Multiple-Input Multiple-Output,多输入多输出）成像雷达是一种新体制雷达系统,相比于传统的地基合成孔径雷达具有便携性好、无需运动补偿及图像帧频高等优势,在滑坡等地质灾害预警方面具有广阔的应用前景。在利用多部MIMO雷达系统进行三维形变监测时,采用双基地MIMO雷达构型能够对传统单基地观测进行有效补充,可大大提高系统的整体观测自由度,但目前尚无有效的双基地MIMO成像雷达成像性能评估方法。针对这一问题,本文通过广义模糊函数对双基地MIMO成像雷达的空间分辨特性进行了分析。首先,根据系统的几何构型推导了地基双基地MIMO成像雷达的广义模糊函数表达式并分析了等效阵列特性,然后根据广义模糊函数对系统的旁瓣走向、两维分辨率和栅瓣位置进行了分析,给出了系统空间分辨率的计算公式,最后利用Matlab仿真验证了分析结论的正确性。      

Sparse, Active Aperture Imaging

We describe an approach to radar imaging of an isolated, rotating target using coherent, sparse, or highly thinned arrays of transmit/receive elements. The array elements are assumed to be randomly positioned and accurately surveyed after placement. Further, the isolated target is assumed to occupy a limited angular sector such that there is no source of backscatter beyond the sector occupied by the target. Estimates of the resolution and image quality are provided when the array elements are widely separated and operate with coherent, multiple-input multiple-output (MIMO) signaling and inverse synthetic aperture radar (ISAR) processing at each MIMO element pair. The sparse array operation can provide superior resolution when compared to the ISAR processing and accurate estimation of scattering properties with a modest number of sparse array elements. The performance of a model-based estimator to physical optics like scattering with MIMO signaling is described. Several important issues relating to feasibility remain to be investigated. These include establishing coherence and timing among the widely separated array elements, adaptive beamforming and processing requirements and coherence of the scattering phenomena with the widely separated MIMO elements.      

MIMO-VSAR及其一种优化的阵列配置

 由于阵元数目有限,传统的速度合成孔径雷达(VSAR)存在方位定位模糊的问题,难以准确地定位地面高速运动目标.本文将多输入多输出(MIMO)雷达与VSAR相结合,提出了新体制MIMO-VSAR雷达及其一种实用的优化阵列配置.与传统VSAR相比,MIMO-VSAR利用同时发送的正交信号,获得了远多于阵元数目的等效密集空间采样,有效地提高了动目标的无模糊定位范围.理论分析和仿真结果均证明了MIMO-VSAR及其优化配置的有效性.     

Mimo radar imaging model and algorithm

Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.     

采用遗传算法的MIMO雷达L型阵列稀布优化

针对多输入多输出（MIMO）雷达阵列稀布导致栅瓣效应的问题,研究了L型阵列方向图综合方法。通过在发射和接收阵列的阵元位置中引入随机扰动,同步优化收发阵列,对MIMO雷达Ｌ型阵列等效构造的虚拟矩形平面阵列进行二维方向图综合,并且对遗传算法的变异算子进行改进,提高全局搜索性,从而有效抑制栅瓣,降低二维合成方向图中方位维和俯仰维的峰值旁瓣电平。仿真实验证明了所提算法的有效性。