New structure of Kalman filter for radar networking

Due to the different data rates of the sensors and communication delays in the radar netting, the research of the asynchronous multisensor data fusion problem is more practical than that of the synchronous one. Through discussing the sequential approach, which is the classical asynchronous multisensor data fusion algorithm, a new algorithm based on distributed computation structure is proposed. The new algorithm can meet the requirement of real-time computation of netting fusion system, and is more practical for engineering compared with the classical sequential approach. Simulation results show the validity of the presented algorithm.

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network(PARN)tracking multiple targets with Measurement Origin Uncertainty(MOU),i.e.,considering the false alarms and missed detections.A Joint Dwell Time Allocation and Detection Threshold Optimization(JDTADTO)strategy is proposed for resource saving in this case.The Predicted Conditional Cramér-Rao Lower Bound(PC-CRLB)with Bayesian Detector and Amplitude Information(BD-AI)is derived and adopted as the tracking performance metric.The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio.It is shown that the objective function is nonconvex due to the Information Reduction Factor(IRF)brought by the MOU.A cyclic minimizer-based solution is proposed for problem solving.Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios.The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

基于CNN-Swin Transformer Network的LPI雷达信号识别

针对在低信噪比(SNR)条件下,低截获概率雷达信号调制方式识别准确率低的问题,提出一种基于Transformer和卷积神经网络(CNN)的雷达信号识别方法。首先,引入Swin Transformer模型并在模型前端设计CNN特征提取层构建了CNN+Swin Transformer网络(CSTN),然后利用时频分析获取雷达信号的时频特征,对图像进行预处理后输入CSTN模型进行训练,由网络的底部到顶部不断提取图像更丰富的语义信息,最后通过Softmax分类器对六类不同调制方式信号进行分类识别。仿真实验表明:在SNR为-18 dB时,该方法对六类典型雷达信号的平均识别率达到了94.26%,证明了所提方法的可行性。

Optimized deployment of a radar network based on an improved firefly algorithm

The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.

Three-dimensional Fusion of Spaceborne and Ground Radar Reflectivity Data Using a Neural Network–Based Approach

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite （TRMM PR） can provide good measurement of the vertical structure of reflectivity, while ground radar （GR） has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network （NN）-based approach. The main steps included are： quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method： interpolation of reflectivity data that are below PR＇s sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data： data with the NN method, data below PR＇s sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

基于DenseNet和卷积注意力模块的高精度手势识别

非接触的手势识别是一种新型人机交互方式,在增强现实(AR)/虚拟现实(VR)、智能家居、智能医疗等方面有着广阔的应用前景,近年来成为一个研究热点。由于需要利用毫米波雷达进行更精确的微动手势识别,该文提出一种新型的基于MIMO毫米波雷达的微动手势识别方法。采用4片AWR1243雷达板级联而成的毫米波级联(MMWCAS)雷达采集手势回波,对手势回波进行时频分析,基于距离-多普勒(RD)图和3D点云检测出人手目标。通过数据预处理,提取手势目标的距离-时间谱图(RTM)、多普勒-时间谱图(DTM)、方位角-时间谱图(ATM)和俯仰角-时间谱图(ETM),更加全面地表征手势的运动特征,并形成混合特征谱图(FTM),对12种微动手势进行识别。设计了基于DenseNet和卷积注意力模块的手势识别网络,将混合特征谱图作为网络的输入,创新性地融合了卷积注意力模块(CBAM),实验表明,识别准确率达到99.03%,且该网络将注意力放在手势动作的前半段,实现了高精度的手势识别。

船载测控雷达高频接收机无人值守功能的设计

随着我国航天测控事业的发展,在轨航天飞行器数量逐年增加,陆地测控站已经由原来的人工操作模式过渡到自动化运行模式;但是在日常工作中,船载S频段测控雷达高频接收机仍需频繁借助人工进行本地开关机,并且在跟踪飞行目标过程中需要人工进行现场识别并设置信号工作频点,存在时效性差和工作效率低的问题;为解决此问题,设计一种设备远程加去电、飞行目标极化判断和频点自动识别设置系统;该系统采取非侵入式,可在常态化任务和海上测控任务中进行灵活切换,不影响系统原有工作方式;该系统基于LabWindows/CVI环境平台控制网络继电器和频谱仪实现了高频接收机的远程加去电、目标信号自动搜索捕获和跟踪旋向和点频设置,提高了船载测控站完成常态化任务的时效性和可靠性。

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。

一种新型雷达探测方法

在弱信噪比场景下,目标回波信号往往非常微弱,难以被雷达探测到,其中包含的一些重要信息更难被识别。因此,需要采取有效措施来发掘受噪声影响较大的弱信噪比目标。研究采用组网雷达回波数据时空同步融合技术,将多组雷达回波信息时频对齐叠加处理。通过单类信号仿真测试,在同等条件下,叠加信号中能有效发掘出普通雷达无法准确识别的目标,采用多类雷达信号混合测试,该方法依然适用。实验证实该方法识别目标效果好、适应范围广,可以拓展到其他雷达信号处理领域,为雷达信号处理提供一种新的思路。

非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

该文针对分布式相控阵多雷达网络的多目标跟踪场景,研究非理想检测条件下的节点选择与辐射资源联合优化分配算法。首先,根据分布式相控阵多雷达网络构成、目标运动模型、雷达量测模型以及雷达节点检测情况,推导非理想检测下以雷达节点选择、辐射功率和信号带宽为变量的贝叶斯克拉默-拉奥下界(BCRLB)闭式解析表达式,并以此作为多目标跟踪精度衡量指标。在此基础上,以最小化系统各雷达节点对所有目标的总辐射功率为优化目标,以满足目标跟踪精度门限以及给定的系统射频辐射资源限制为约束条件,建立非理想检测条件下多雷达网络节点选择与辐射资源联合优化分配模型,对各时刻雷达节点选择、辐射功率和信号带宽等参数进行联合优化设计,以提升多雷达网络的射频隐身性能。最后,针对上述非线性、非凸优化问题,采用基于障碍函数法和循环最小化算法的4步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能在满足给定多目标跟踪精度的条件下有效降低分布式相控阵多雷达网络的总辐射功率,至少降低了约32.3%,从而提升其射频隐身性能。

基于掩模提取的SAR图像对抗样本生成方法

合成孔径雷达(synthetic aperture radar,SAR)图像的对抗样本生成在当前已经有很多方法,但仍存在对抗样本扰动量较大、训练不稳定以及对抗样本的质量无法保证等问题。针对上述问题,提出了一种SAR图像对抗样本生成模型,该模型基于AdvGAN模型架构,首先根据SAR图像的特点设计了一种由增强Lee滤波器和最大类间方差法(OTSU)自适应阈值分割等模块组成的掩模提取模块,这种方法产生的扰动量更小,与原始样本的结构相似性(structural similarity,SSIM)值达到0.997以上。其次将改进的相对均值生成对抗网络(relativistic average generative adversarial network,RaGAN)损失引入AdvGAN中,使用相对均值判别器,让判别器在训练中同时依赖于真实数据和生成的数据,提高了训练的稳定性与攻击效果。在MSTAR数据集上与相关方法进行了实验对比,实验表明,此方法生成的SAR图像对抗样本在攻击防御模型时的攻击成功率较传统方法提高了10%~15%。

基于几何的星载探测雷达组网空中目标高度估计算法

星载探测雷达具有全球覆盖、全天候、全天时、不易受到攻击等优点,在预警防御系统中具有巨大潜力。单个星载探测雷达由于俯仰角误差过大,导致其不具备空中目标高度估计能力;准确的目标高度估计对改善目标定位精度及威胁估计能力等具有重要意义。考虑多部星载探测雷达组网,提出了一种基于几何的空中目标高度估计方法,并通过仿真验证方法的有效性。通过仿真比较了雷达探测区域不同位置目标高度估计精度的差别,为组网雷达系统设计等提供技术支撑。

基于可解释网络解耦表征的低成本雷达定位解算方法

为降低调频连续波(FMCW)雷达成本,同时提高定位精度,设计可解释解耦表征模型。该模型由网络解算器、虚假信号生成器以及可解释潜变量三部分组成。首先处理雷达信号获得中频频谱;然后输入到网络解算器中生成位置潜变量;再通过物理机制对潜变量进行转换,生成虚假中频信号频谱;最后,设计局部光滑损失函数对模型进行自监督训练,实现潜变量的解耦物理表征。实验结果表明:所提算法能对雷达系统频谱信号的粗粒度进行超分辨率细化,其机理能有效应对雷达系统的硬件公差、环境噪声、安装误差等问题,并可自动地训练出雷达的解算网络,从而具有大规模室内、机载联网定位的应用潜力。

机器学习的高精度毫米波雷达测距信号误差补偿方法

毫米波雷达是一种常用的非接触式测距技术,受环境因素以及测量过程中存在的各种误差影响,测距结果可能存在一定的误差。研究误差补偿方法可以有效提高毫米波雷达的测距精度,从而更准确地获取目标物体的距离信息。为此,提出了机器学习的高精度毫米波雷达测距信号误差补偿方法。通过高斯滤波器去除雷达测距信号中的噪声,完成信号的去噪处理。利用模拟插入脉冲计数法和四象限光斑定位法,测量目标物体的距离和角度信息,通过自适应惯性权重与收敛因子优化粒子群算法,并利用优化后的粒子群算法改进BP神经网络,将测量的距离和角度信息输入到改进的BP神经网络中展开训练,即可得到补偿后的雷达测距信号。实验结果表明,该方法的信号处理效果好,补偿后的毫米波雷达测距信号方位角和俯仰角误差接近于0,且信号平滑度较高。

基于多模型神经网络的湿度廓线反演研究

为提升微波辐射计对大气廓线探测的精度,利用ARM大气观测站提供的地基微波辐射计、毫米波测云雷达以及探空数据,构建了两种添加不同云信息的反向传播神经网络(back propagation neural network,BPNN)模型(添加入云和出云高度的C-BPNN模型与添加雷达反射率因子的Z-BPNN模型)与一种未添加云信息的BPNN模型(记为BPNN0),并对反演结果进行了对比,结果表明:C-BPNN模型和Z-BPNN模型在任何天气下(有云或无云),得到的反演误差都小于BPNN0模型;C-BPNN相较于另外两种模型反演结果具有更高的稳定性。对3种模型各自反演结果最好的个例分析发现,C-BPNN与Z-BPNN模型主要的误差存在于高空无云但是相对湿度却出现跃变的情况,说明神经网络模型对初始权值与阈值较为敏感,因此通过遗传算法(genetic algorithms,GA)对BPNN模型进行优化。经GA优化后的反演结果表明:BPNN0模型与C-BPNN模型具有明显优化效果,而Z-BPNN模型优化效果则不明显。

雷达转台液压系统可靠性影响评价预测研究

针对当前雷达液压系统可靠性合理建模及影响评价体系难以构建及评估的现状,以某典型机载雷达转台液压系统为研究对象,提出一种基于危害性分析的可靠性影响评价预测方法。利用模糊判断矩阵以获得指标的权重系数,对可靠性进行建模仿真分析,从而为准确预测系统的可靠性提供理论支撑。在此基础上,通过对转台液压系统故障原因进行全面梳理,构建了液压系统可靠性影响评价体系。运用Elman网络,结合实证分析对可靠性影响评价模型进行预测,为雷达转台液压系统故障的综合分析提供了整体解决方案。这也为进一步提高雷达液压系统的可靠性提供了合理思路。该研究可为雷达其他分系统可靠性分析提供参考。

基于改进离散模拟退火遗传算法的雷达网协同干扰资源分配模型

针对分布式干扰机掩护目标突防雷达网背景下的干扰资源分配问题,提出了一种引入随机密钥的改进离散模拟退火遗传算法(improved discrete simulated annealing genetic algorithm,IDSA-GA)对资源分配过程进行优化。基于雷达网融合检测概率构建干扰效果评估函数,利用IDSA-GA对函数寻优求解。IDSA-GA在模拟退火遗传算法(simulated annealing genetic algorithm,SA-GA)的基础上引入随机密钥,完成算法的离散化;并在迭代的过程中增加记忆功能,克服了过早收敛的现象。仿真结果表明,与GA相比,提出的IDSA-GA收敛迅速,寻优能力强,能有效解决干扰资源优化分配问题。

基于多初级假设的FM广播外辐射源雷达网航迹起始算法

基于调频(FM)广播信号的外辐射源雷达有着检测概率低、虚警率高、量测精度差的特点,这给组网目标跟踪带来了极大挑战。一方面,较高的虚警率使计算量增加,组网算法的实时性受到考验;另一方面,检测概率低、方位角精度差造成冗余信息缺乏,量测关联与航迹起始变得困难。为解决这些问题,该文提出初级假设点和初级假设航迹的概念,以及基于此概念的FM广播外辐射源雷达网航迹起始算法。首先构造可能的低维关联假设,并解算出与其对应的初级假设点;随后关联不同时刻的初级假设点,形成多条可能的初级假设航迹;最后联合多场雷达网数据进行假设航迹判决,真实目标对应的初级假设航迹会得到确认,错误关联导致的虚假初级假设航迹会被剔除。相比于已有算法,所提算法有着更低的计算量,更快的航迹起始速度,仿真与实测结果均验证了所提算法的有效性。