Persymmetric adaptive polarimetric detection of subspace range-spread targets in compound Gaussian sea clutter

This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.

A general evaluation system for optimal selection performance of radar clutter model

The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

Considering the unmanned aerial vehicle(UAV) three-dimensional(3D) posture, a novel 3D non-stationary geometry-based stochastic model(GBSM) is proposed for multiple-input multipleoutput(MIMO) UAV-to-vehicle(U2V) channels. It consists of a line-of-sight(Lo S) and non-line-of-sight(NLo S) components. The factor of fuselage posture is considered by introducing a time-variant 3D posture matrix. Some important statistical properties, i.e.the temporal autocorrelation function(ACF) and spatial cross correlation function(CCF), are derived and investigated. Simulation results show that the fuselage posture has significant impact on the U2V channel characteristic and aggravate the non-stationarity. The agreements between analytical, simulated, and measured results verify the correctness of proposed model and derivations. Moreover, it is demonstrated that the proposed model is also compatible to the existing GBSM without considering fuselage posture.

Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria Using Non-Stationary Approach

This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the intensity levels, the open-access R-studio software was used based on the General Extreme Value (GEV) distribution function. Among the four linear parameter models adopted for integrating time as a covariate, the fourth linear model incorporating scale and location with the shape function constant produced the least corrected Akaike Information Criteria (AICc), varying between 306.191 to 101.497 for 15 and 1440 minutes, respectively, selected for calibration of the GEV distribution equation. The non-stationary intensities yielded higher values above those of stationary models, proving that the assumption of stationary IDF models underestimated extreme events. The difference of 13.71 mm/hr (22.71%) to 14.26 mm/hr (17.0%) intensities implies an underestimation of the peak flood from a stationary IDF curve. The statistical difference at a 95% confidence level between stationary and non-stationary models was significant, confirming evidence of climatic change influenced by time-variant parameters. Consequently, emphasis should be on applying shorter-duration storms for design purposes occurring with higher intensities to help reduce the flood risk and resultant infrastructural failures.

Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

高灵敏微波载荷卫星出舱电缆及舱体屏蔽的EMC设计

针对高灵敏微波载荷卫星对舱板电磁屏蔽效能的高需求与出舱电缆对舱体屏蔽效能巨大破坏性之间的矛盾,文章首先提出航天器理想舱板(无出舱电缆)的电磁屏蔽效能分析方法,给出提高对于1 GHz以上干扰电磁波屏蔽效能的设计原则;然后分析出舱电缆成为影响航天器外舱板对P波段(150 MHz~1 GHz)电磁波屏蔽效能主要因素的机理,给出干扰电流与干扰场强关系计算公式;最后给出出舱电缆及舱体屏蔽的电磁兼容(EMC)设计方法,提出用接地电阻测量法验证出舱电缆屏蔽皮接地的有效性,为总体总装EMC实施找到关键控制手段,并通过整星试验数据确认此方法能有效提升整星EMC试验效率。

初相捷变波形优化设计与信号处理方法研究

捷变波形具备一定的距离和速度模糊抑制能力,同时兼具优良的低截获和抗干扰性能,为雷达系统提供了新的信号处理维度。初相捷变波形相对简单,工程实现难度小,为实现波形设计和信号处理闭环,本文开展了初相捷变波形优化设计与信号处理方法研究。首先,介绍了初相捷变波形的距离选通特性,进而构建了基于距离选通特性的初相捷变波形优化设计模型,并通过Majorization-Minimization算法和牛顿法进行求解。所提算法可采用平方迭代法加快收敛速度,经仿真验证,与随机产生的初相捷变波形相比,所提算法可以显著改善优化后初相捷变波形的距离选通特性。其次,基于初相捷变波形的抗折叠杂波性能,探讨了基于距离选通特性的自适应杂波抑制算法,即利用初相捷变波形空时维导向矢量的距离选通特点,实现分距离段杂波抑制,使得近距主副瓣杂波不会折叠到远距离段,经仿真与实测数据验证,所提算法在强杂波环境下具备良好的远距低速目标探测能力。最后,研究了基于距离选通和交替反演的干扰抑制算法,利用远距支援干扰滞后于目标回波的特点,并基于初相捷变波形的距离选通特性,通过距离段间交替投影实现干扰与目标回波的分距离段重构,经仿真验证,所提算法可以实现对远距支援干扰的有效抑制。

基于杂波拖尾分布的雷达无人机检测性能分析

固定翼无人机(unmanned aerial vehicle,UAV)给雷达低空监视提出了严峻挑战。分析雷达对固定翼UAV的检测性能,可为雷达UAV检测能力评估和技术升级提供重要参考。本文结合雷达探测低空固定翼UAV外场实测数据,首先分析了低空固定翼UAV雷达接收信号幅度统计分布,采用多项式对杂波拖尾导致的虚警概率进行拟合建模;然后,根据虚警概率分布得到雷达检测门限;进而根据UAV回波+杂波幅度分布理论推导得到雷达检测概率;最后,将理论分析性能与传统性能分析结果、雷达实际检测性能进行对比。结果表明,采用多项式对杂波拖尾导致的虚警概率进行单独建模,由此获得的雷达检测门限精度更高,从而使雷达UAV检测性能分析结果较传统性能分析结果更准确。

基于残差单元与注意力门的非对称编解码海杂波抑制网络

针对非均匀海杂波环境下弱小目标检测困难的问题,本文基于复值残差单元和注意力门机制,提出一种用于海杂波抑制的非对称编解码网络(Asymmetric Encoder-Decoder Network,AED-Net).该网络以雷达回波经匹配滤波后得到的复值信号为输入,利用复值残差单元取代常规卷积单元进行弱小目标和海杂波特征的提取,增强网络特征提取能力的同时避免特征信息退化.然后采用注意力门模块将编码路径各模块提取的特征信息分别送入到解码路径对应的模块.最终输出海杂波抑制后的复值信号.由于各注意力门的输入和输出维度可根据网络结构自主选择,该网络设计是一种非对称编解码结构.与典型对称编解码网络UNet相比,复值残差单元与注意力门的引入显著降低了特征信息的冗余度,增强特征信息的提取与传递,提升了海杂波抑制性能.与此同时,复值残差单元的参数规模远小于卷积单元,而注意力门的引入也有效减少解码路径单元的数量,整个网络的参数规模显著减小.基于海杂波实测数据的实验结果表明,与典型复值UNet(Complex Value-UNet,CV-UNet)网络相比,AED-Net的输出信杂比平均提升9 dB,有效工作的最低信杂比降低了3 dB,模型参数量和计算量分别减少57.8%、50%.

矿井探地雷达背景杂波抑制方法综述

随着中国煤炭安全高效智能开采需求的不断提升,矿井探地雷达(ground-penetrating radar, GPR)作为一种高精度物探方法,在煤矿井下探测中有广阔的应用前景。由于探地雷达目标回波信号受系统内部噪声、外部电磁干扰、巷道回波、地下介质不均匀等干扰影响,虚警率较高。尤其对于小尺寸目标,其回波信号为弱信号,容易淹没在强烈的背景杂波中,成为目前制约探地雷达探测性能的主要因素之一,进一步实现井下煤层、构造探测、提高探测精度的关键在于对背景杂波的抑制。从对消法、滤波法、分解法和网络法出发,综述这些杂波抑制方法的基本原理、优缺点及其在探地雷达干扰抑制领域的研究进展,并探讨各种方法的适用范围和发展潜力。杂波抑制技术的不断创新将进一步提高探地雷达的信噪比与灵敏度,使其在煤矿井下探测中发挥更大的应用价值。

基于无监督图互信息最大化的海面小目标异常检测

受到复杂海洋环境的影响,雷达对海面慢速小目标难以实现高性能检测。对于这类目标,传统的基于能量的统计检测方法存在着严重的性能损失。针对这一问题,该文提出了基于互信息最大化框架下的海面小目标检测方法,实现海杂波背景下的无监督目标异常检测任务。首先,考虑到高分辨雷达回波不满足传统神经网络对样本独立同分布的假设,该文从图的角度重新建模数据,利用回波的空时相关特性来构建图拓扑结构。该文提出相对最大节点度并联合7个已有特征作为节点的初始表示向量。接下来,采用图注意力网络作为互信息最大化框架中的编码器学习节点表示向量。最后,使用异常检测算法进行目标检测,并实现虚警可控。经实测数据验证,使用快速凸包学习算法时,相比三特征检测器,所提检测器性能提升了9.2%;相比时频三特征检测器,性能提升了7.9%。当网络输出更高维的表示向量时,使用孤立森林算法的检测器的性能提升了27.4%。

基于WGAN-GP-CNN的海面小目标检测

针对传统基于统计理论的海面小目标检测方法在复杂海面环境中性能不高的问题,该文提出了一种改进的检测方法。首先通过分析海杂波和目标回波的特征,将检测问题转化为特征空间的分类任务。鉴于海面小目标样本数量有限,存在样本不平衡的问题,该文引入了一种基于梯度惩罚的沃瑟斯坦生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)来增强目标数据,从而在数量上平衡目标样本与海杂波样本。同时,对原始WGAN-GP网络的损失函数进行了改进,引入相位损失以确保生成数据能够反映真实数据的相位信息。基于这些数据,进一步提取了生成目标和海杂波的高维特征,并将其送入卷积神经网络(Convolutional Neural Network,CNN)进行训练。为了应对高维特征空间中虚警概率难以控制的问题,对CNN算法进行了改进,通过设置Softmax分类器的阈值,实现了虚警概率可控。最后,借助公开的IPIX雷达数据集进行实验验证,所提的WGAN-GP-CNN检测器在积累时间为1.024 s,虚警概率为0.001时,平均检测概率达到0.8683,具有良好的检测效果。

一种基于模糊融合规则的CFAR检测器

针对单元平均恒虚警检测器(CA-CFAR)在多目标干扰和杂波边缘效应下的性能局限,提出了基于子参考窗口中最小选择单元的恒定虚警检测器(MCA-CFAR)。通过选择子参考单元中的最小单元格,显著提升了检测器的性能,并在瑞利分布背景下详细推导了检测概率、虚警率和检测阈值。为进一步优化性能,设计了模糊逻辑融合检测器(FUMCA-CFAR),它利用两个传感器计算空间隶属函数值,并通过代数和、代数积、MAX、MIN四种规则进行融合,实现了平滑输出,减少了目标信息的丢失。仿真实验表明,基于代数和融合的FUMCA-CFAR检测器在均匀和非均匀背景下均展现出优异的检测性能和抗干扰能力。

双基地SAR动目标图像相位误差分析和二维稀疏重聚焦处理

双基地合成孔径雷达(Synthetic Aperture Radar,SAR)系统中,数据采集空间几何关系复杂,动目标回波距离徙动严重,重建图像中往往存在几何失真和散焦现象,需要进一步补偿运动相位误差。为此,分析并推导了双基地动目标极坐标格式图像的二维相位误差谱。根据相位误差模型建立字典,约束动目标回波谱在字典上的投影系数,进而实现杂波和动目标能量的分离和重聚焦成像。点目标仿真验证了算法的有效性。

基于改进SFLA-Elman神经网络的电离层杂波抑制方法

针对高频地波雷达目标检测中电离层杂波的干扰问题,提出了一种基于改进混合蛙跳算法优化Elman神经网络预测抑制电离层杂波的策略。为解决混合蛙跳算法初始种群分布不均匀、收敛精度低、易陷于局部极值等问题,引入Cubic混沌映射、莱维飞行策略、非线性平衡因子和复制操作,增强种群多样性,提高算法搜索能力。利用改进后的算法和其他算法分别优化Elman神经网络预测抑制模型,结果表明,改进后的算法无论是在收敛精度和稳定性上,还是在临近距离单元电离层杂波的预测抑制上,都取得了显著的提升。在基本保留目标信号的基础上,平均信杂比较原始回波提升18.52 dB,较原始混合蛙跳算法提升1.08 dB,对于电离层杂波的抑制具有较高应用价值。

基于KA-SRCN-pSTAP的低空风切变风速估计方法

针对由于独立同分布(IID)样本严重不足,导致极化空时自适应(pSTAP)处理性能下降,进而导致低空风切变风速估计不准确的问题,本文提出了一种基于知识辅助的稀疏表示杂波零陷极化空时自适应(KA-SRCN-pSTAP)的低空风切变风速估计方法。该方法首先利用杂波脊的先验知识辅助构造极化空时稀疏字典,然后利用极化空时稀疏字典,通过SRCN算法挑选原子并对到杂波线性子空间补空间上的投影矩阵进行估计,从而得到pSTAP权矢量,最后构造pSTAP滤波器对地杂波进行抑制,准确估计低空风切变风速。该方法仅使用少量IID样本,将SRCN算法与极化-空时域相结合,完成对风切变风速的有效估计。仿真实验结果证明该方法可以有效实现少样本情况下的风速准确估计。

基于MIMO阵列的一体化射频技术研究进展及展望

基于多输入多输出(multiple-input-multiple-output,MIMO)阵列的一体化射频系统通过充分利用空间自由度和波形自由度,不仅能够同时实现雷达探测、数据通信、电子干扰等多种功能,而且具有目标探测可靠性好、参数估计精确度高、反侦察识别能力强等突出特点,在高动态强对抗环境中展现了巨大的潜力。从基于MIMO阵列的一体化射频系统的检测性能分析入手,剖析了一体化射频系统的检测性能极限以及影响检测性能的主要因素;针对MIMO阵列参数高精度估计问题,提出通过单个发射脉冲完成参数估计以及一种增强参数估计精度的波形优化算法;考虑到目标角度估计误差不可避免,提出了一种稳健的一体化波形设计算法;针对杂波环境中一体化射频系统性能下降的问题,提出了发射波形和接收滤波器联合优化算法,该算法不仅能够实现同时多功能,而且有效地抑制了杂波,提高了目标检测性能。最后,展望了一体化射频系统参数估计精度极限、高精度参数估计、稳健波形设计、波形设计算法快速实现等有待突破的信号处理关键技术。

基于注意力自相关机制的剩余杂波抑制方法

雷达工作时面临着复杂多变的环境,杂波特性经常呈现非均匀性和时变性。未被完全抑制的杂波剩余可能会产生大量虚警,进而导致虚假航迹产生或目标跟踪精度降低。在严重情况下,这些虚警还可能使雷达数据处理系统饱和,影响雷达系统的探测能力。传统的剩余杂波抑制算法需要进行特征提取和构建分类器两个步骤,存在泛化能力差、特征组合难和分类器要求高等问题。为解决这些问题,受到自注意力机制和领域知识的启发,提出了一种数据与知识双驱动的注意力自相关机制。该机制可以有效提取用于区分目标和杂波的雷达回波相互关系的深度特征。同时,基于该机制,构建了一种剩余杂波抑制方法,充分利用雷达回波特征,提高了算法在剩余杂波抑制方面的性能。仿真和实测数据处理结果表明,该方法在剩余杂波抑制方面具有显著的性能优势和泛化能力。此外,该方法的并行计算结构可以提高算法的运行效率。

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

基于模型阶数选择准则的稳健杂波边缘检测方法

在雷达目标自适应检测问题当中,辅助数据存在杂波边缘的情况将导致杂波协方差矩阵(CCM)的估计性能出现严重下降,极大地影响目标检测性能。为了解决这一问题,该文提出一种杂波边缘检测方法,能够对辅助数据中杂波边缘数量与位置进行自适应判别。首先,假定辅助数据中存在杂波边缘,采用模型阶数选择算法和最大似然估计方法完成杂波参数估计,其中杂波边缘位置由循环搜索方法得到。之后将杂波参数估计结果应用到检测算法中,通过广义似然比检验方法来判断杂波边缘是否存在。此外为了进一步提升算法在小样本条件下的稳健性,引入CCM的特殊结构作为先验知识,将算法推广至CCM为斜对称、谱对称以及中心对称3种结构的情况。仿真及实测数据均表明该文所提算法能够高效地识别雷达辅助数据中的杂波边缘数量和位置,先验知识的引入更能进一步改善算法在辅助数据量较小时的性能。