具有未知参数的多输入多输出雷达弱目标检测

针对弱目标检测算法的特点,提出了一种新的多输入多输出(MIMO)雷达检测器。在经典线性模型的基础上,通过最大似然法对未知统计特性的噪声量进行了估计。利用所得到的估计量,分别推导了在信道完全不相关和信道任意相关条件下虚警概率和检测概率的闭合表达式,对具有未知参数的MIMO雷达在任意信道环境下的检测算法进行了分析和比较,有效地解决了具有未知统计特性的噪声的MIMO雷达目标检测问题。仿真结果证实了该方法的有效性。     

分散型多输入多输出雷达检测器设计及检测性能分析

对不同类型的目标,分散型多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达的最佳检测器结构各异,且通常不便于工程实现.针对此问题,提出了一种易于工程实现的MIMO雷达检测器结构.首先对MIMO雷达和普通单基地雷达的回波信号模型进行分析,指出了MIMO雷达回波和单基地雷达快起伏目标多脉冲回波之间的相似关系.借鉴单基地雷达针对快起伏目标的检测器结构,提出分散型MIMO雷达可以采用类似的包络检波级联累加器的检测器结构形式.就所设计检测器结构对不同空域起伏目标的检测性能进行了研究,给出了检测性能的理论和仿真曲线.     

基于Capon谱的MIMO雷达目标检测性能分析

密集多输入多输出(MIMO)阵列式雷达技术的一个重要方面,一般而言,MIMO雷达以距离为参数进行目标检测.文中基于Capon、APES等自适应参数估计算法,针对密集MIMO阵列,提出了一种以方位为参数的目标检测方法.以Capon谱为例,通过蒙特卡洛试验,仿真分析了这种检测的性能.结果表明,检测概率和虚警概率相同时,Capon谱检测方法的信噪比要求比传统检测高6 dB ～ 10 dB,因此,可认为密集MIMO阵列不适合利用自适应参数估计算法进行目标检测.     

多路输入多路输出雷达：性能问题

多路输入多路输出技术的应用为多基地雷达提供了很多优点，包括改进的分辨率和灵敏度。取决于雷达的操作方式、阵面设计以及环境，这些优点可能或者不可能显著地表现出来。在本文中论述了一个地面动目标指示（GMTI）雷达探测的简单分析模型，并将预测情况与模拟情况进行了比较。对传统的单一输入多路输出（SIMO）和MIMOGMTI雷达在最小探测速度、区域搜索率和阵面副瓣方面进行了比较。确定了MIMO雷达的最佳波形互相关矩阵，提出了不同的性能标准。最后，改进了MIMO阵面的均衡采样虚拟孔径的范围。     

非完备目标先验知识多输入多输出雷达稳健波形设计

多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达通常基于目标和环境的先验知识设计波形,而由估计得到的先验知识不可避免存在误差,基于此先验知识得到的优化波形的检测和参数估计等性能下降严重.针对此问题,提出两种稳健波形设计方法,分别提高MIMO雷达的检测性能和参数估计性能.在目标位置误差和信道误差有界的条件下,分别构造以信噪比(Signal Noise Ratio,SNR)和克拉美-罗界(Cramera-Rao Bound,CRB)为代价函数的优化问题.为最大化SNR和最小化CRB,分别给出迭代算法,交替以发射波形相关矩阵和通道矩阵误差为优化变量求解,将迭代的每一步转化为凸优化问题,从而在最差情况下提高MIMO雷达系统的检测性能和参数估计性能.仿真实验结果验证了所提方法能有效改进MIMO雷达的检测和参数估计性能.     

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.     

网络雷达中快起伏Rician目标检测性能分析

该文研究了总发射功率一定的条件下,网络雷达4种模式中快起伏Rician目标检测性能。结果表明快起伏Rician目标可分成3类,且对于不同的分类具有不同的检测特性,具体表现为：类斯怀林II目标的检测性能与斯怀林II目标相同,标准快起伏Rician目标显示出与斯怀林II目标不同的检测性能,混合快起伏Rician目标,除了MIMO模式与标准快起伏Rician目标变化规律一致外,其他模式均与斯怀林II变化规律相同。研究结果对网络雷达的系统设计具有一定的指导意义。     

目标特性对MIMO雷达检测性能影响研究

本文将更通用的Rician目标模型引入MIMO雷达,并根据检验统计量的特点将目标分成三类。分析和仿真结果表明,MIMO雷达对较小k值时的Rician目标,球形目标和Rayleigh目标的检测性能比较接近,对较大k值的Rician目标检测性能优于Rayleigh目标和球形目标,而且k值越大检测性能越好。仿真结果为MIMO雷达的系统设计提供了一定的理论基础。     

MIMO雷达与相控阵雷达的多脉冲检测性能比较

MIMO雷达是近几年发展起来的一种新体制雷达,由于同时采用了空间分集与信号分集技术,它表现出了许多不同于传统雷达的特性。该文着重研究了MIMO雷达与传统相控阵雷达多脉冲条件下的检测性能,建立了两种体制雷达多脉冲条件下的目标检测模型,理论给出了两种体制雷达对时间上服从快、慢起伏目标的检测门限和检测概率表达式,同时通过对两种目标模型下MIMO雷达和相控阵雷达的多脉冲理论检测性能的对比分析。     

正交信号MIMO雷达动态范围与弱目标检测性能分析

正交信号MIMO雷达各阵元发射相互正交的信号波形,多个信号波形在空间不能叠加形成高增益的窄波束,经目标反射回来的回波信号的动态范围较传统相控阵雷达有所降低,这将有利于改善对强杂波中的弱目标信号的检测性能。动态范围往往取决于杂波功率,本文分析了基于OFDM-LFM信号的MIMO雷达的信号功率问题,并提出利用优化的随机相位来降低信号瞬时功率,从而改善回波信号的动态范围。仿真比较了在接收机动态范围一定的条件下,MIMO雷达相对于传统相控阵雷达在弱目标检测性能方面的改善。      

Game theoretic design for polarimetric MIMO radar target detection

Polarimetric radar systems allow the flexibility of transmitting arbitrarily polarized waveforms that match the scattering profiles of the target. Since different types of targets have varying profiles, the advantages of a polarimetric radar system can fully be exploited only when the type of target is accurately estimated. However, accurate estimation requires a significant amount of training data, which can be expensive. We propose a polarimetric design scheme for distributed multiple input multiple output (MIMO) radar target detection. We formulate the selection of transmit polarizations using a game theoretic framework by examining the impact of all possible transmit schemes on the detection performance with different available target profiles (see also Gogineni and Nehorai, 2011 [1]). This approach does not require training data, and we show a significant performance improvement due to the polarimetric design. Other radar design problems can also be solved using this game theoretic approach.     

MIMO雷达检测性能分析

多输入多输出(MIMO)雷达是最近所提出的一种新型的雷达体制。通过利用空间分集和角度分集,从而避免了雷达目标的多径衰落问题。这里主要分析了MIMO雷达基于似然比的检测性能。另外,这里也将MIMO雷达跟相控阵雷达的检测性能做了对比,仿真结果表明MIMO雷达相比传统雷达具有的优异的检测性能。     

分布式MIMO雷达的参数估计与检测联合算法

本文针对分布式MIMO雷达系统,在站间大间隔配置获得的空间分集增益的基础上,提出了一种目标位置估计与检测的联合算法。与以往距离门检测不同的是,这里通过所定义的目标假设框架下进行联合估计与检测。通过理论分析证明,本文所提出的位置估计与检测联合算法在检测性能上要优于距离门检测法,且漏检概率与信噪比SNR成反比。仿真实验也验证了算法的有效性。     

Dynamic programming based adaptive step integration method for maneuvering fluctuating target detection

An adaptive long time integration method based on dynamic programming (DP) is proposed for the detection of high speed maneuvering fluctuating targets. The proposed method is aimed at detecting a target with an unpredictable range migration and fluctuating echoes by jointly applying three main ideas: the improved DP procedure that could search the maneuver position and velocity at each frame; the multi-pulse integration that could suppress the influence of fluctuation; and the adaptive step with fading factor that could allow the integration time to be suitable for each searching velocity. Compared with the existing methods, the target energy could be integrated along its trajectory using the proposed method without estimating the specific motion parameters, which makes the proposed method applicable to a target with arbitrary motion. Simulation results and performance comparisons show the superiority of the proposed method.     

Multiple detection of a slowly fluctuating target (Corresp.)

A "slowly" fluctuating target is assumed to keep its radar cross section constant for the duration of several(M)dwells on target. To resolve multiple range and/or Doppler ambiguities, the received signal, which is presumably coherently processed (i.e., predetection integrated or matched filtered) over each dwell, must often be tested against a threshold, {em independently} of those on other dwells. Such a procedure is referred to as {em multiple detection}. A technique for the evaluation of a tight lower bound on the multiple-detection probabilityP_{M}, under Swerling case I statistics for the cross section, is presented in term of an infinite series and worked out in detail forP_{2}andP_{3}. Estimates on the computation error due to the truncation of the series are derived. Numerical results indicate thatP_{3}comes much closer toP_{1}than top_{1}^{3}or even toP_{1}P_{2}; at an expected signal-to-noise ratio of13dB and atP_{1} = 0.51, it obtains thatP_{3} geq 0.40, whereasP_{1}P_{2} = 0.23andp_{1}^{3} = 0.17.     

MIMO雷达对Swerling Ⅲ和Swerling Ⅳ目标的检测性能

该文基于Swerling Ⅲ和Swerling Ⅳ目标模型,研究空间分集MIMO(多输入多输出)雷达发射相干脉冲串时的检测方法和性能,得到了检测概率和虚警概率的解析表达式。仿真表明,随着相干脉冲串数目的增加,目标的检测性能更好;同时,与传统雷达相反,在高检测概率时,空间分集MIMO雷达对Swerling Ⅲ型目标的检测性能优于Swerling Ⅳ型目标。     

Analytical performance evaluation of adaptive detection of fluctuating radar targets

Aradar target whose return varies up and down in amplitude as a function of time represents the basis of a large number of real targets. This paper is intended to provide a complete analysis of CFAR detection of fluctuating targets when the radar receiver post-detection integrates M returned pulses from χ² fluctuating targets with two and four degrees of freedom and operates in a non-ideal environment. Owing to the importance of Swerling models in representing a large number of such type of radar targets, we are interested here in adaptive detection of this class of fluctuation models. Swerling cases I and III represent scan-to-scan fluctuating targets, while cases II and IV represent fast pulse-to-pulse fluctuation. Exact expressions of detection probability are derived for all of these models. A simple and an effective procedure for calculating the detection performance of both fixed-threshold and adaptive-threshold algorithms is obtained. In the CFAR case, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. The performance of this detector is analyzed in the cases when the operating environment is ideal and when it includes some of spurious targets along with the target of interest. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the four Swerling’s models cited above. The numerical results show that for strength target return the processor detection performance is highest in the case of SWIV model while it attains its minimum level of detection in the case of SWI model. Moreover, SWII model has higher performance than the SWIII representation of fluctuating targets. For weak target return, on the other hand, the reverse of this behavior is occurred. This observation is common for both fixed-threshold or for adaptive-threshold algorithms.