Fixed-wing UAV guidance law for ground target over-flight tracking

This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law,and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.

Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle(UAV)swarms task planning and the key technology to improve autonomy.The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms,the target and environment state,and the high real-time allocation requirements.Hence,dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning.In this work,a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects:the establishment of an allocation model and the solution of the allocation model.First,the basic concept and trigger scenario are introduced.Second,the research status and the advantages and disadvantages of the two allocation models are analyzed.Third,the research status and the advantages and disadvantages of several common dynamic task allocation algorithms,such as the algorithm based on market mechanisms,intelligent optimization algorithm,and clustering algorithm,are evaluated.Finally,the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted,and future research directions are established.This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.

An unscented particle filter for ground maneuvering target tracking

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

Application of Road Information in Ground Moving Target Tracking

一个新算法被开发目标借助于使用道路信息追踪在移动扎根完成精确州的评价。交往是适应可变结构的有动态模型修正的多重模型评估者(DMM VS-IMM 为短) 。第一，道路信息被采用修改动态模型由过滤器使用了的目标，包括州的转变矩阵和过程噪音的修正。第二，道路信息被使用更新一个 VS-IMM 评估者的模型集合。预言的州的评价和道路信息被用来在模型集合在上被更新的道路网络定位目标，最后，过滤的 IMM 被实现。作为与传统的方法相比，州的评价的精确性为目标在一条单个道路上移动被改进不仅，而且通过交叉。蒙特卡罗模拟与中等计算负担表明建议算法的效率和坚韧性。

RADAR HRR PROFILING FOR GROUND MOVING TARGET USING PHASE-CODED AND HOPPED-FREQUENCY WAVEFORMS

To obtain the radar High Range Resolution (HRR) profile of the slowly moving ground target in strong clutter background, the Phase-Coded Hopped-Frequency (PCHF) waveform is proposed. By multiple-bursts coherent processing, the HRR profile synthesis, target velocity compensation and clutter compression can be accomplished simultaneously. The new waveform is shown to have good ability to suppress ground clutter and good Electronic Counter-CounterMeasures (ECCM) ability as well. The clutter compression performance of the proposed method is verified by the numerical results.

Estimation of detection threshold in multiple ship target situations with HF ground wave radar

A credible method of calculating the detection threshold is presented for the multiple target situations, which appear frequently in the lower Doppler velocity region during the surveillance of sea with HF ground wave radar. This method defines a whole-peak-outlier elimination (WPOE) criterion, which is based on in-peak-samples correlation of each target echo spectra, to trim off the target signals and abnormal disturbances with great amplitude from the complex spectra. Therefore, cleaned background noise samples are obtained to improve the accuracy and reliability of noise level estimation. When the background noise is nonhomogeneous, the detection samples are limited and often occupied heavily with outliers. In this case, the problem that the detection threshold is overvalued can be solved. In applications on experimental data, it is verified that this method can reduce the miss alarm rate of signal detection effectively in multiple target situations as well as make the adaptability of the detector better.

基于自适应KF的船载地波雷达目标跟踪方法

针对使用传统的卡尔曼滤波器进行多目标跟踪易发生状态估计不准确、航迹断裂的问题,提出了一种转换量测卡尔曼滤波增益自适应调整方法。结合船只目标的运动特性和历史船只航迹数据,采用线性最小二乘拟合提取出航迹参数的变化趋势。使用似然函数分别计算目标预测状态和量测状态与航迹的隶属度。利用预测状态和量测状态的隶属度值对滤波方式进行选择。利用仿真及实测船载地波雷达数据开展了目标跟踪实验,结果表明,使用该文方法能够得到平滑、持续的航迹输出,目标的跟踪距离远、跟踪精度高,优于现有的自适应滤波方法。

基于改进SBR技术的地面目标SAR图像快速生成技术

传统的弹跳射线(shooting and bouncing ray,SBR)技术主要适用于自由空间目标的电磁散射计算,当用于地面目标与背景复合场景的电磁散射计算和合成孔径雷达(synthetic aperture radar,SAR)图像建模与仿真时,在小擦地角下其计算误差显著增大。针对此问题,提出了一种基于“四路径”模型的改进SBR技术,其考虑了目标与邻域地面之间的多次反射效应以及目标与远区地面之间的多径散射效应对地面目标散射回波的影响。首先构建一个尺寸略大于目标包围盒的粗糙地面实体模型;然后结合“镜像法”改进射线追踪技术,分析电磁波在目标各部件间、目标同实体地面之间以及同虚拟半空间分界面之间的射线传播过程;最后结合高频渐近方法完成电磁散射计算。利用精确数值法计算结果对所提出技术进行了验证,并将其应用于地面目标的SAR图像高逼真度仿真,同实测SAR图像比对表明了所提技术具有良好的适用性。

基于NGG-YOLOv5的空对地UXO目标检测方法

为提高无人机在复杂环境下对地面未爆弹(UXO)目标的辨识精度,提出了一种改进YOLOv5的UXO目标检测方法。该方法在YOLOv5的基础上,改进原YOLOv5网络的损失函数以提高对UXO目标的识别精度,同时,通过添加注意力机制、改进马赛克数据增强、改进预测框筛选机制提高对UXO目标的识别效率,实现了空对地场景下对UXO目标的检测,并具有较好的精度和速度。实验选取多组不同复杂背景的UXO数据集进行标注并训练,得到UXO目标模型,然后从模型训练结果和目标检测结果的角度评估方法和模型的正确性。实验结果表明:NGG-YOLOv5所得模型检测准确性和检测速度对比原YOLOv5有明显的提升,准确率从78%提高至91%,平均精度均值(mAP)从50%提高至56%,在所用4种复杂背景下均可有效检测出UXO目标,且漏警率低。

基于FCOS算法的地下目标重建方法

针对从复杂多样的探地雷达(GPR)成像中检测和定位被埋藏的物体会耗费大量人力时间成本的问题,提出一种基于深度学习的方法。采用一阶全卷积目标检测算法(FCOS)对任意目标进行定量分析,然后对目标区域进行跟踪与聚类标记,曲线拟合获取地下目标的精确位置,重构被埋藏的地下目标信息。仿真结果表明,该方法避免了传统处理算法所需的对数据进行复杂的计算,能够快速检测到目标,并且能对目标的位置与介电属性进行高精确度估计,在深度上的定位误差不大于3 cm。该方法有效实现了地下场景重构目标的位置、深度和大小。

基于目标暴露区的地面防空兵力最佳配置区规划

利用远距离支援干扰存在的目标暴露区,通过优化配置地面防空兵力,可有效降低干扰对我方地面防空作战的不利影响。在“三点一线”干扰模式分析的基础上,利用雷达干扰方程得到目标暴露区,提出了基于目标暴露区的地面防空兵力可配置区及横向、纵向兵力配置间距计算方法,并根据雷达有效干扰扇面,得到地面防空兵力干扰威胁指数计算模型。在此基础上,构建以干扰威胁指数总量最小为目标函数,以兵力可配置区及兵力配置间距为约束条件的远距离支援干扰下地面防空兵力最佳配置规划模型。采用基于线性递减权重函数的改进萤火虫优化(improved glowworm swarm optimization,IGSO)算法对其求解并进行了有效性仿真验证,结果表明所提出的规划模型具有较好的理论价值。

基于路网仿射模型的地面编队目标跟踪算法

针对地面多目标跟踪命题,文章首先给出编队目标定义,总结并比较现有编队目标的跟踪方法,在此基础上考虑地面编队目标在路网上编队慢速行进的特点,建立地面编队目标仿射模型,并采用非线性滤波算法对编队整体平移、旋转和缩放行为进行跟踪,从而实现对编队目标的状态估计,进而得到编队目标航迹情报。经数学仿真和实验验证,所提算法具备有效性。

地面LIDAR在滑坡灾害三维实景建模中的应用

为快速获取邻近铁路滑坡体表面的三维坐标,实现对滑坡灾害快速响应和及时治理。以宝兰高铁上庄隧道出口滑坡为例,采用地面LIDAR技术,通过优化布设扫描站点,实现最大扫描范围并保证相邻站间的重叠度,以无靶标扫描方式获取滑坡体及周围地物的点云数据。在RiSCAN PRO软件中利用相邻扫描站间重叠点云进行拼接处理,点云拼接精度为6.9 mm,满足三维建模和地形图制作的精度要求。利用扫描站点坐标、测站全景影像,经过配准、坐标转换、纹理贴附、多边形拟合、曲面光滑等,建立可量测的三维实景模型,为辅助地形图制作和铁路滑坡灾害整治提供便利。研究表明,采用地面LIDAR技术以无靶标扫描方式快速建立铁路滑坡体三维实景模型,具有全天时、全天候、高精度、高密度、无接触的优势,打破了传统人工测量的局限性,提高作业效率,降低外业测量风险,可为同类型地质灾害数据获取及整治积累经验。

Target location with signal fitting and sub-aperture tracking for airborne multi-channel radar

The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with.The proposed approach is applied in two steps:first,the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method,and second,the unambiguous slant-range velocity is found using a track-based criterion.The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large.Besides,the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal,therefore,the location performance would not be sacrificed in order to suppress clutter and/or interference.The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.

REAL EXTENDED SCENE AND MOVING TARGET MULTI-CHANNEL SAR RAW SIGNAL SIMULATION

A real extended scene and moving targets multi-channel Synthetic Aperture Radar(SAR) raw signal simulator accounting for Inertial Navigation System(INS) errors and antenna patterns is presented in this paper. INS errors are obtained by solving INS error differential equations with Runge-Kutta method. A high resolution SAR image is used to estimate the complex reflectance of real extended scene. Extended scene and moving target are simulated separately and then are superposed in time domain. The simulated multi-channel SAR data can be used for development of multi-channel SAR Ground Moving Target Indication(SAR-GMTI) and also can be used for development of SAR motion compensation.

舰船目标运动状态光学卫星视频监测与估计

光学卫星视频能够进行动目标连续监视,已成为当前研究热点。但目前多数研究仅针对基于视频像方的目标检测、跟踪及轨迹提取等方面,没有充分利用卫星载荷成像几何模型进行物方运动状态定量估计和精度验证。因此,本文提出一种基于光学卫星视频的舰船目标运动状态定量估计方法,首先对卫星视频首帧进行精确定向;然后通过帧间稳像方法得到背景稳定的视频帧序列,在此基础上,针对正框目标跟踪方法定位精度低的问题,提出一种顾及旋转和尺度变化的舰船稳健跟踪方法进行目标跟踪;最后进行物方映射并估计目标运动轨迹、速度和方向。利用吉林一号光学视频卫星开展星海同步观测试验。实测结果表明,稀疏控制条件下的舰船目标几何定位精度为1.60 m,速度估计精度为0.18 m/s,方向估计精度为4.05°,相比同类方法具有显著提升,证明了本文方法的有效性和可行性。

合成孔径雷达图像的地面目标检测与伪装效果评估

地面目标伪装效果评估是基于目标在探测频段下伪装前后检测概率的变化,合成孔径雷达能够全天候工作,分辨率较高,已经成为对地侦查的主要手段。对于SAR图像的目标检测概率通常采用专家判读和特征模板匹配等方式获取,这些方法主观性较强或对算法设计的依赖度较高,因此需要一种科学客观的评判方法。随着神经网络在图像识别领域的快速发展,目标特征提取和识别结果愈加准确,现采用YOLO-V3(You Only Look Once)算法模型,对MSTAR目标背景合成图像进行机器学习,自动获取目标特征进行识别。训练后的模型在测试集的识别准确度为97.87%,用训练好的模型对未伪装样本与伪装后样本分别进行检测概率计算,通过比较检测概率的变化从而量化某类目标的伪装效果。

基于路侧激光雷达的障碍物目标检测方法

针对现有障碍物检测方法在复杂道路场景下存在地面分割欠精准、计算量大以及不同距离下的目标聚类困难问题,提出了一种基于路侧激光雷达的障碍物检测方法。在地平面分割方面,提出基于圆柱坐标系的改进扇形栅格模型以及最低点代表法优化种子点的选取,采用多地平面模型并通过随机采样一致性算法(RANSAC)实现地面拟合及分割。在障碍物聚类方面,构建KDTree加速聚类过程,提出划分区域及阈值自适应的方式改进欧氏聚类算法。实验结果表明,该方法在4种典型道路场景下对地面点的分割准确率均达到86%以上,且针对不同距离下的障碍物目标聚类准确率提升明显。

基于通道和空间注意力的机场道面地下目标自动检测

针对机场道面地下目标检测任务中,探地雷达(GPR)生成的B-scan图背景复杂、包含大量噪声,尤其是单个B-scan图不能反映地下目标的完整信息等问题,构建一种三维通道和空间注意力的UNet(3D-CSA-UNet)模型对地下目标进行自动检测。首先,设计三维通道和空间注意力并行模块(3D-CS-Block),使模型重点关注雷达C-scan中的地下目标信息,抑制背景和噪声的干扰;其次,设计多尺度的三维分割模型从雷达C-scan中提取不同大小的特征图,以增强3D-CS-Block提取目标特征的能力;最后,使用交叉熵损失函数计算每个尺度下特征图的损失值,从而提高模型的检测精度。在采集的实际机场道面地下目标数据集上,相较于3D-FCN、3D-UNet等模型,3D-CSA-UNet对于脱空、钢筋和钢筋平行目标预测的平均F1至少提高12.33、9.05、11.05个百分点。实验结果表明,3D-CSA-UNet可以较好地满足工程实际要求。