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.

Sensor Registration in Asynchronous Data Fusion

To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused.

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.

Sensor Registration Based on Neural Network in Data Fusion

The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here the measurements from radar are transformed from the polar coordinate system to the Cartesian coordinate through a BP neural network. With this approach, the systematic errors are removed as well as the coordinate is transformed. The efficiency of this method is demonstrated by simulation, and the result show that this approach could remove the systematic errors effectively and the DAR are closer to real position than DBR.

Multi-view ladar data registration in obscure environment

Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.

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.

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.

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.

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.

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.

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.

Multimodality image registration and fusion using neural network

Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.

利用伯努利滤波的多目标机动雷达误差配准方法

传统的组网雷达多目标误差配准方法通常假设数据关联关系已知,但在平台机动的情况下,系统同时存在雷达测量偏差和平台姿态角偏差,且雷达观测过程中会受到杂波干扰,导致数据关联尤为困难。针对这一问题,该文提出一种基于伯努利滤波的多目标机动雷达误差配准方法。首先建立系统偏差的量测与状态方程,然后将系统偏差建模成伯努利随机有限集,利用公共坐标系下的原始量测可实现系统偏差在伯努利滤波框架下的递推估计,有效避免了数据关联问题。同时,为了充分利用多目标量测信息,提出一种修正的贪婪量测划分方法,在每个滤波时刻挑选出系统偏差对应的最优量测子集,利用量测子集中的多量测信息实现系统偏差的集中式融合估计,提高了系统偏差的估计精度和收敛速度。仿真实验表明,所提方法能够在数据关联未知的多目标多杂波场景下对雷达测量偏差和平台姿态角偏差进行有效估计,在平台姿态角变化率较低时,所提方法具有较强的适应性。

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。