自适应数字波束形成在MIMO雷达中的应用

MIMO雷达是近几年来发展起来的一种新体制雷达,由于采用空间分集与信号分集技术,相对于传统相控阵雷达在目标检测及参数估计性能都有很大提高。文中主要研究MIMO雷达的系统的自适应数字波束形成算法,介绍了基于MIMO雷达的SMI自适应DBF算法,该算法有较好的性能,易于实现。针对于空间中的有源干扰,从自由度的角度讨论分析了MIMO雷达处理系统中自适应模块的位置问题。     

频控阵MIMO雷达中基于稀疏迭代的多维信息联合估计方法

传统MIMO雷达为了获得准确的距离信息不仅需要引入转移矩阵,而且需要积累更多的快拍数。针对该问题,该文提出了基于稀疏迭代的频控阵MIMO雷达多维信息（距离、角度和幅度）估计方法,该方法利用频控阵MIMO的发射导向矢量不仅与目标的方位角有关,同时也与目标的距离有关这一特性,通过优化加权lq范数（0 q 1）的目标函数,采用稀疏迭代优化获取目标的距离、角度和幅度信息。该方法仅需单次快拍,并在每次迭代中求解到了目标的多维信息,故不需借助优化工具获得目标信息。仿真显示,相比DAS,IAA和IAA-R,该方法在有效减少快拍数和处理数据的同时,获得了高分辨率的目标距离、角度和幅度估计性能。      

Improved design of unimodular waveforms for MIMO radar

This paper proposes a novel method of unimodular transmitting waveforms design for multiple-input multiple-output (MIMO) radar to strengthen the detection performance in the presence of clutter and white Gaussian noise. An improved iterative algorithm is put forward to maximize the signal-to-clutter-plus-noise ratio (SCNR) under the constant modulus constraint. During iterations, the optimization of unimodular waveforms with filters fixed is a nonconvex fractional quadratically constrained quadratic program problem, which is NP-hard and not able to be solved in polynomial time. An algorithm based on semidefinite programming relaxation combined with bisection and Gaussian randomization is introduced to provide the high-quality suboptimal solutions with a polynomial time computational complexity. The analysis on the approximation bound is given to prove the tightness of the semidefinite programming relaxation and so the correctness of the proposed algorithm. The simulation results show that the improved method is efficient in designing unimodular waveforms for MIMO radar to achieve a better SCNR performance.     

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.     

MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter

The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods.      

空域分解的机载MIMO雷达空时处理方法

相对于传统机载相控阵雷达,应用于机载多输入多输出雷达中的空时自适应处理（MIMO-STAP）技术可以获得杂波抑制和动目标检测能力的大幅提升.但是传统MIMO-STAP所需要的计算量和样本需求量巨大,无法满足实际处理要求.为了解决这一问题,我们提出了一种基于空域多级分解的机载MIMO雷达后多普勒自适应方法.该方法将后多普勒自适应权系数进行分解,使其变为几个短向量的Kronecker乘积,然后利用循环迭代的思想求解自适应权.实验表明该方法具有快速收敛性,在小样本大阵列条件下该方法明显优于传统的后多普勒处理方法.     

一种车载毫米波FMCW MIMO雷达快速成像方法

针对车载毫米波FMCW MIMO雷达现有的常规波束形成算法的旁瓣效应造成的方位向分辨率低以及高分辨算法的工程实时性低的问题,提出了一种迭代自适应算法（IAA）高分辨成像的快速实现方法。该方法首先利用快速傅里叶变换（FFT）获取目标一维距离像,然后对每一距离单元利用FFT算子和Gohberg-Semencul（GS）因子分解计算迭代自适应算法（IAA）的数据协方差矩阵和其逆矩阵,利用快速Toeplitz矩阵向量乘法计算IAA迭代值,从整体上提升了IAA估计各角度散射系数的实时性。仿真和实验结果验证了该方法的可行性和有效性。     

基于时分MIMO的地基雷达高分辨率成像研究

基于时分多输入多输出(MIMO)的地基雷达成像有许多很好的应用价值,比如替代合成孔径雷达成像在山体滑坡监测方面的应用。针对基于时分MIMO的地基雷达高效高分辨率成像,该文提出一种基于逆傅里叶变换脉冲压缩和波束形成的成像算法。雷达通过步进频连续波技术获得高距离向分辨率,利用MIMO技术获得高方位向分辨率。通过逆傅里叶变换法实现雷达数据距离向压缩,采用波束形成算法实现雷达数据方位向压缩。同时该算法还针对MIMO天线阵列所引起的回波信号相位不连续问题进行了适当的校正,在保持算法高效性的同时提高了成像质量。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成像算法的可行性,应用在山体滑坡监测上理论效果良好。     

On Probing Signal Design For MIMO Radar

A multiple-input multiple-output (MIMO) radar system, unlike a standard phased-array radar, can choose freely the probing signals transmitted via its antennas to maximize the power around the locations of the targets of interest, or more generally to approximate a given transmit beampattern, and also to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. In this paper, we show how the above desirable features can be achieved by designing the covariance matrix of the probing signal vector transmitted by the radar. Moreover, in a numerical study, we show that the proper choice of the probing signals can significantly improve the performance of adaptive MIMO radar techniques. Additionally, we demonstrate the advantages of several MIMO transmit beampattern designs, including a beampattern matching design and a minimum sidelobe beampattern design, over their phased-array counterparts.     

MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space-time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity.     

Robust Joint Design of Transmit Beamforming and Receive Filter for TB-MIMO STAP Radar

In this paper, we consider the joint design of transmit beamforming and receive filter for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar red space-time adaptive processing (STAP) under the target information uncertainty. The associated robust joint design problem is formulated by maximizing the signal-to-interference-plus-noise ratio (SINR) to guarantee the target detection performance. An iterative optimization procedure is developed to resolve the NP-hard joint design problem. The optimized transmit beamforming weight vector and receive filter exhibit superior performance in terms of output SINR. Several simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.

     

Angle Estimation Using Quaternion-ESPRIT in Bistatic MIMO-Radar

In this letter; we present a novel two-dimensional angle estimation for bistatic multiple-input multiple-output (MIMO) radar. We reconstruct the received signal of MIMO radar to model with quaternion theory, and then angle estimate using quaternion estimation of signal parameters via rotational invariance technique for MIMO radar is proposed. The proposed algorithm can obtain automatically paired two-dimensional angle estimation in MIMO-radar. The proposed algorithm has much better angle estimation performance than the Wang’s quaternion algorithm, which has a much heavier computational load than the proposed algorithm. Simulation results verify the usefulness of our algorithm.     

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.     

Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.     

MIMO系统中一种改进的盲MMSE空时多用户检测算法

针对MIMO系统,提出了一种改进的基于子空间的盲MMSE空时多用户检测算法。该算法结合MIMO系统的空间分集技术与Alamouti空时分组码方案,预估计MIMO信道信息并对信号子空间进行预处理,使用正交性能和稳态性能较好的NOOja算法跟踪信号子空间,在自适应过程中对特征值矩阵进行优化,去除迭代带来的噪声,解决了跟踪过程中信号特征值矩阵的近似估计会带来检测器性能恶化的问题。仿真结果表明这种算法,能有效地抑制多址干扰,抗远近效应能力强,尤其在低信噪比、远近效应明显的恶劣环境下,有稳定良好的性能表现。      

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.      

Computationally efficient DOD and DOA estimation for bistatic MIMO radar with propagator method

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Cramér–Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm.     

基于十字阵的MIMO雷达的二维DOA估计

多输入多输出（MIMO）雷达使用多个天线同时发射独立波形,在不同位置的目标回波彼此线性独立,以获得较好的空间分辨率。文章提出了基于收发不共位的线阵在水平面内构成十字阵的MIMO雷达信号模型;研究利用MUSIC算法来估计该模型下的MIMO雷达的二维波达角;分析了基于均匀十字阵的MIMO雷达的优越性,并探讨了十字阵MIMO雷达性能与阵元间距的关系,仿真表明,在满足远场条件下,均匀十字阵比均匀线阵可以估计更多的目标而且精度更高;通过非均匀布阵,可以进一步提高MIMO雷达的二维波达角的估计精度。     

基于单通道MIMO雷达的毫米波安检成像

毫米波多入多出（MIMO）雷达是一种前沿的安检成像技术。然而,由于采用了大量的发射/接收通道,MIMO雷达的成本和复杂度大大高于传统的单通道雷达。为了研制低成本、低复杂度的毫米波MIMO安检成像雷达,引入了双重码分复用技术。该方法可通过单发射/接收通道实现与传统多通道MIMO雷达相似的成像性能,对安检成像领域的发展和应用具有重要意义。此外,设计了基于失配滤波器理论的解复用码,其可在干扰条件下实现最大码分集增益。进行了仿真与实验以验证所提出方法的正确性与有效性,得到了满意的结果。     

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。