Synergy Decision for Radar and IRST Data Fusion

A new synergy decision method for radar and infrared search and track (IRST) data fusion is proposed, to solve such problems as how to decrease opportunities for radar suffering from being locked on by adverse electronic support measures (ESM), how to retrieve range information of the target during radar off, and how to detect the maneuver of the target. Firstly, polynomials used to predict target motion states are constructed. Secondly, a set of discriminants for detecting target maneuver are established by comparing the predicted values with the observations from IRST. Thirdly, a set of decisions are presented. Lastly, simulation is performed on the given scenario to test the validity of the method.

Data fusion of target characteristic in multistatic passive radar

Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.

Distributed Computation Models for Data Fusion System Simulation

An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.

Trunk detection based on laser radar and vision data fusion

Tree trunks detection and their location information are needed to perform effective production and management in forestry and fruit farming.A novel algorithm based on data fusion with a vision camera and a 2D laser scanner was developed to detect tree trunks accurately.The transformation was built from a laser coordinate system to an image coordinate system,and the model of a rectangle calibration plate with two inward concave regions was established to implement data alignment between two sensors data.Then,data fusion and decision with Dempster-Shafer theory were achieved through integration of decision level after designing and determining basic probability assignments of regions of interesting(RoIs)for laser and vision data respectively.Tree trunk width was calculated by using laser data to determine basic probability assignments of RoIs of laser data.And a stripping segmentation algorithm was presented to determine basic probability assignments of RoIs of vision data,by calculating the matching level of RoIs like tree trunks.A robot platform was used to acquire data from sensors and to perform the developed tree trunk detection algorithm.Combined calibration tests were conducted to calculate a conversion matrix transforming from the laser coordinate system to the image coordinate system,and then field experiments were carried out in a real pear orchard under sunny and cloudy conditions,with trunk width measurement of 120 trees and 40 images processed by the presented stripping segmentation algorithm.Results showed the algorithm was successful to detect tree trunks and data fusion improved the ability for tree trunk detection.This algorithm could provide a new method for tree trunk detection and accurate production and management in orchards.

Distributed Radar Target Tracking with Low Communication Cost

In distributed radar,most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy.However,as the filtering covariance matrix indicating positioning accuracy often occupies many bits,the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication chan-nels.This paper studies how to compress data for distributed tracking fusion algorithms.Based on the K-singular value decomposition(K-SVD)algorithm,a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix.Then the least square quantization(LSQ)algo-rithm is used to quantize the data according to the statistical characteristics of the sparse coeffi-cients.Quantized results are then coded with an arithmetic coding method which can further com-press data.Numerical results indicate that this tracking data compression algorithm drops the com-munication bandwidth to 4%at the cost of a 16%root mean squared error(RMSE)loss.

Fusion of Ground-Based and Spaceborne Radar Precipitation Based on Spatial Domain Regularization

High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L_(1) and L_(2) norms were more effective in constraining stratiform and convective precipitation,respectively.As a combination of L_(1) and L_(2) norms,the Huber norm is more suitable for mixed precipitation types.This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar(CDP)and dual-frequency precipitation radar(DPR)on the Global Precipitation Measurement(GPM)mission core satellite.Compared to single-source radar data,the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields.In 27 precipitation cases,the fusion results utilizing the Huber norm achieved a structural similarity index measure(SSIM)and a peak signal-to-noise ratio(PSNR)of 0.8378 and 30.9322,respectively,compared with the CDP data.The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation,with a reduction in the mean absolute error(MAE;16.1%and 22.6%,respectively)and root-mean-square error(RMSE;11.7%and 13.6%,respectively)compared to the 1-norm and 2-norm.Moreover,in contrast to the fusion results of scale recursive estimation(SRE),the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.

Comparison of Layer-stacking and Dempster-Shafer Theory-based Methods Using Sentinel-1 and Sentinel-2 Data Fusion in Urban Land Cover Mapping

Data fusion has shown potential to improve the accuracy of land cover mapping,and selection of the optimal fusion technique remains a challenge.This study investigated the performance of fusing Sentinel-1(S-1)and Sentinel-2(S-2)data,using layer-stacking method at the pixel level and Dempster-Shafer(D-S)theory-based approach at the decision level,for mapping six land cover classes in Thu Dau Mot City,Vietnam.At the pixel level,S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets(i.e.fused datasets).The datasets were categorized into two groups.One group included the datasets containing only spectral and backscattering bands,and the other group included the datasets consisting of these bands and their extracted features.The random forest(RF)classifier was then applied to the datasets within each group.At the decision level,the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory.Finally,the accuracy of the mapping results at both levels within each group was compared.The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group.The highest overall accuracy(OA)and Kappa coefficient of the map using D-S theory were 92.67%and 0.91,respectively.The decision-level fusion helped increase the OA of the map by 0.75%to 2.07%compared to that of corresponding S-2 products in the groups.Meanwhile,the data fusion at the pixel level delivered the mapping results,which yielded an OA of 4.88%to 6.58%lower than that of corresponding S-2 products in the groups.

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.

Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.

基于SAR卫星遥感技术的农田洪涝灾害信息提取技术

为提高农田洪涝灾害信息提取能力,探索了SAR卫星遥感影像水体及农田边界信息的自动提取方法。以江西丰城某次强降雨过程为例,采用阈值分割法、雷达及光学影像融合法,利用Sentinel-1卫星影像对灾前水体信息进行提取,巢湖一号卫星影像对灾中的水体信息进行提取,将二者提取信息进行叠加,得到本次强降水新增水体范围;利用Sentinel-2卫星影像,叠加天地图影像提取出研究区域的农田边界范围,将该边界与新增水体范围叠加,得到受本次强降雨影响农田洪涝灾害区域的范围。经评价,该方法可有效提高地物散射特征的分类精度,提取的11处受淹农田验证地块完整率均在80%以上。SAR遥感影像不受云雨天气影响,能够在洪涝灾害应急监测中提供有力的数据支撑,该分析方法有利于相关部门全面掌握农田灾情数据,迅速做出应急响应,提高洪灾的应急救助管理能力。

分布式雷达信号级融合检测的数据压缩与组网架构设计

分布式探测是雷达领域热点问题,信号级融合探测比数据级融合探测能力更强,但通常需要的通信带宽较大。为此文中针对分布式非相参信号级目标融合探测,提出了基于雷达压缩数据的信号级融合目标检测方法。所提方法通过可并行化计算的信号级融合算法实现不同雷达量测值之间的去耦,通过双门限检测避免传输局部低能量的噪声信号,通过二次量化对过门限信号进行再次压缩,最终实现以点迹通信带宽逼近信号级融合检测的能力。基于4雷达组网的数值仿真结果验证表明,通信带宽缩减至原来的1/1 000,信噪比损失不超过0.7 dB,并据此探索雷达组网的体系架构设计问题,可支撑不同场合下的信号级协同探测工程应用。

降雨监测与预报技术在防洪减灾中的应用进展

洪水灾害突发性强,成灾速度快,对人民生命和财产安全造成较大的威胁。降雨作为洪水灾害致灾因子,数据的精确度对防洪减灾具有重要意义。以降雨监测与预报技术为切入点,对雨量站点观测、天气雷达降雨估计及预报、降雨数值预报、卫星遥感反演的现状进行了总结,通过分析时空降尺度方法及多源数据融合技术在降雨监测与预报中的应用,揭示了其在提升降雨数据“量”与“型”准确度方面的效果。研究表明:降雨监测与预报技术在当前取得了显著进展,但在山丘区和城市环境空间的复杂地形方面仍面临分辨率受到限制及精确性、时效性不足的问题。多源数据融合能提高降雨数据精度、时空覆盖能力和预测准确性,优化算法模型、融合“空-天-地”多源数据形成高分辨率预报是未来的研究方向。

地基云雷达与FY-4A卫星云顶高度联合反演方法

基于地基毫米波云雷达与FY-4A卫星对云顶同步观测的特点,分析云雷达垂直顶空观测的云顶高度与FY-4A卫星AGRI载荷通道数据之间关系,提出了地基云雷达与FY-4A卫星云顶高度联合反演方法,实现云雷达安装点周边区域卫星云顶高度的融合反演,并对反演结果进行验证分析。结果表明,AGRI载荷的第11~14通道值与云雷达观测云顶高度呈线性相关,且卫星通道值与云雷达观测云顶高度比值呈现冬季最小、春秋季次之、夏季最大的季节性变化特点;星地融合反演云顶高度与云雷达观测云顶高度相关系数0.84,融合后比融合前均方根误差减小了0.7 km,提高了卫星云顶高度的反演精度。

基于主被动复合导引头的干扰态势构建

在导引头主被动信息融合过程中,存在多装备与多目标平台关联困难、无法有效提取目标特征信息,难以有效融合构建态势的问题。因此,本文提出一种将导引头主动雷达信息和被动侦察信息融合的数据关联方法。该方法先提取导引头干扰态势表征要素,然后以目标方位为中心,将平台与装备关联,构建干扰态势。本文通过主被动信息融合构建的态势,在原先分离的平台和设备间建立空间位置与搭载的关系,可以有效地消除欺骗式干扰和多径散射假目标,提高态势认知效率。

基于行为特征的雷达辐射源威胁评估

针对当前雷达辐射源威胁评估对精确侦察数据依赖性较大的问题,提出一种基于行为特征的雷达辐射源威胁评估算法。从辐射源目标行为特征和数据融合理论出发,建立基于行为特征的辐射源威胁评估体系,并采用模糊理论和Vague数据集对各子行为进行表示;考虑到指标间的耦合性和空战的高动态性,利用改进的区间灰色关联度修正初始权重,建立以距离为自变量的态势状态函数,为各子行为动态赋权;采用改进的雷达图法计算威胁目标的威胁程度。仿真结果表明:所提算法具有较好的准确性和适应性。

基于长短路融合及数据平衡的SAR船舶检测算法

针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。

一种主被动雷达目标跟踪测量数据融合方法

从主动雷达和被动雷达对空中目标跟踪数据融合的角度,通过一种数学近似处理与几何图形解析相结合的雷达测量误差定性分析方法,归纳空间离散分布主、被动雷达对同一目标的测量误差分布特征。在满足目标与主、被动雷达部署点所成张角较小时,提出一种新颖的主、被动雷达目标测量数据融合方法,通过优化加权系数分配准则以充分结合2种雷达各自的测量精度优势,对目标跟踪数据在地理球坐标系中进行融合处理以提高测量精度,最后采用扩展卡尔曼滤波算法对目标融合数据进行平滑处理。仿真结果表明,本方法可以结合主动雷达和被动雷达的目标测量精度优势,能够缩小目标数据融合后的测量误差椭圆,整体提高融合后的目标跟踪数据测量精度。

基于雷达点云的果树快速识别与分割方法研究

在果园自动化作业生产中,果树的精准识别是果园精准作业的重要前提。针对在复杂果园环境中单株果树难以精准快速识别的问题,基于激光雷达三维点云技术对果树识别进行研究,提出了一种精准、快速、高自动化的果树识别方法及装置。构建多维度传感融合识别装置对果树进行特征点云数据扫描,建立果树点云数据集,通过点云质量及有效角度滤波进行数据筛选。利用边界特征点识别算法对单株果树边界特征数据进行快速提取,利用边界区域特征构建果树有效扫描数据。该方法可适用于单株果树轮廓、特征的快速识别。为了验证该果树识别方法准确性,进行了果树树冠轮廓测量精度试验,以及果树群组识别试验,结果表明,该方法能够对果树进行有效识别,并且物理指标综合测量精度>97.125%,可为果园精准作业提供有效依据。

测风激光雷达与风廓线雷达的探测性能评估及数据融合

测风激光雷达和风廓线雷达作为L波段探空测风的有效补充,均可以提供高时空分辨率的大气风场信息,然而由于工作原理和适用条件存在明显差异,在探测性能上各有优缺点,单一设备的探测数据已不能满足精细化预报的要求。本研究使用2020年1—5月北京南郊观象台的L波段探空资料对同址观测的测风激光雷达和风廓线雷达进行了数据质量评估,结果表明测风激光雷达与探空的一致性较高,U、V分量的相关系数分别为0.97和0.98,均方根误差分别为1.1和0.95 m·s^(-1),然而在2 km以上数据获取率较低且偏差较大;风廓线雷达与探空相比,U、V分量的相关系数分别为0.94和0.93,均方根误差分别为2.94和2.91 m·s^(-1),风廓线雷达的探测距离虽然更远,但在0.5 km以下和6 km以上的测量偏差较大。考虑到两种测风雷达在不同探测高度上的性能优缺点,提出分段曲面拟合法对两者的水平风资料进行融合处理,并选取个例对融合效果进行验证,结果表明,融合后的风廓线与融合前相比,风向和风速的一致性均得到明显提升。

基于二维相控阵体制的探鸟雷达系统

随着环境的改善和航空事业的迅速发展,野生动物与航空器的冲突愈发尖锐,其中鸟击事件占野生动物袭击事件的90%以上。据统计,鸟击事件每年给全世界航空业带来超过20亿美元的损失,是影响世界航空业的头号危害。针对鸟击事件对航空器造成巨大危害的问题,必须对鸟情进行持续、全面的观察,其目的一方面是当观察到大量鸟类聚集活动时,可以及时指导飞行员进行规避、对鸟群进行驱赶;另一方面,通过长期数据积累,可以对鸟类的生物学和运动学特征进行研究,进而实施有针对性的防治措施。基于上述需求,雷达技术以其全天时、全天候、大范围、远距离、高精度等优势,在机场鸟情防治中得到了迅速的发展与应用,并发挥了重要作用。本文首先概要介绍了国内外鸟击事故整体情况及传统的鸟情探测手段,指出雷达技术在机场鸟击防范应用中的优势;其次,较为全面地介绍了探鸟雷达系统的发展历程,鸟情探测系统的体制、工作模式、探测性能等相应的发展情况;然后,对雷达在鸟情探测中的应用难点进行分析,并从系统体制、目标探测、目标识别等角度出发,对相应关键技术的研究进展进行说明;接着,对国内外典型探鸟雷达系统的技术状态和应用情况进行了对比,并对北京理工雷科电子信息技术有限公司的圆柱形二维有源相控阵探鸟雷达系统的研究进展进行了介绍;最后,对探鸟雷达技术的发展做出总结与展望。