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.

Integrated Guidance and Control of Homing Missiles Against Ground Fixed Targets

This paper presents a scheme of integrated guidance and autopilot design for homing missiles against ground fixed targets. An integrated guidance and control model in the pitch plane is formulated and further changed into a normal form by nonlinear coordinate transformation. By adopting the sliding mode control approach, an adaptive nonlinear control law of the system is designed so that the missile can hit the target accurately with a desired impact attitude angle. The stability analysis of the closed-loop system is also conducted. The numerical simulation has confirmed the usefulness of the proposed design scheme.

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.

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.

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.

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.

基于深度学习的遥感激光图像特征定位技术

为提高对激光遥感图像特征点定位的准确度,提出基于深度学习算法的遥感激光图像特征定位技术。采用高光谱图模型参数融合和光谱波段图像检测方法,对遥感激光图像进行多频段检测和稀疏化表示,提取多光谱和点云数据,进行不同信息维度的特征重组;采用深度学习算法,动态迭代搜索目标物理和几何特征点。采用图像分割技术聚类处理特征点的复合特征;利用无向图建模特征定位的邻域关系,根据特征聚类和邻域输出表达结果,实现对遥感图像的特征点准确定位。仿真结果表明,采用该方法进行遥感激光图像特征定位的分辨率水平较高,能实现反模糊化的目标特征点定位,定位准确率最高为0.92,点云噪声干扰下,偏移量最高为6∗10^(-3)。

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。

面向复杂场景的多通道慢速动目标稳健检测算法

针对鲁棒主成分分析(RPCA)算法在多通道慢速地面动目标指示(GMTI)中存在的高虚警以及对通道误差敏感问题,该文提出一种数据重构与速度合成孔径雷达(VSAR)-RPCA联合处理的方法。首先,通过样本挑选与联合像素法完成通道间数据精确重构;然后结合VSAR检测模式提出一种新的RPCA优化模型,通过采用交替投影乘子法对其进行求解得到空间频域的稀疏矩阵,进一步利用动目标与强杂波残余在空间频域通道的分布特性差异实现强杂波残余剔除与动目标检测;最后采用沿航迹干涉算法估计目标径向速度完成动目标重定位。相较于传统RPCA算法,所提算法在非理想强杂波背景下的虚警率显著降低。理论分析与实测实验验证了所提算法的有效性。

太赫兹CSAR运动目标参数估计与重聚焦方法

为了提高太赫兹圆周模式下的动目标运动参数估计精确度和成像质量,提出了一种太赫兹圆周合成孔径雷达(CSAR)运动目标参数估计与重聚焦方法。首先对传统的谱峰测量法进行改进,并结合动目标阴影位置来估计方位速度。与现有方法相比,该方法结合了目标的真实位置对多普勒频移的影响,提高了运动目标方位速度估计值的准确性。利用分数阶傅里叶变换(FrFt)估计方位相位的多普勒调频率,结合方位速度可得到目标距离速度的估计值。利用估计结果构造一阶和二阶相位补偿函数进行方位相位的补偿,对于残余的二次及高次相位误差,利用相位梯度自聚焦算法(PGA)进行补偿。仿真和实测数据中动目标运动参数得到了精确估计,并实现了良好聚焦,证明了所提方法的有效性,大大提高了运动目标的成像质量。

无人机集群对地攻击时空协同控制策略

针对三维战场环境下无人机集群协同对地打击问题,提出了一种基于预设时间收敛的无人机集群时空协同控制方法.首先,对无人机集群对地攻击任务场景及其相关要素进行了分析,并建立了无人机集群、地面运动目标和攻击问题模型.在问题建模的基础上,考虑无人机集群的时空协同策略,设计三维空间无人机集群对地攻击的时空协同控制律.所提出的时空协同控制律能够控制无人机集群在指定的时间内到达期望的攻击位置,实现对地面运动目标的时空协同攻击.通过基于李雅普诺夫函数的理论分析,证明了所提出的控制律能够在预设时间条件下使攻击位置误差变量渐进稳定.仿真结果表明,在地面运动目标转弯运动的情况下,所提出的协同控制律能够实现无人机集群的时空协同,在指定的时间内完成对地面运动目标的多角度协同攻击.

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。

面向动态目标搜索与打击的空地协同自主任务分配方法

面向复杂城市环境中的运动目标搜索与打击任务,单域无人平台受视野范围和运动能力等限制,易出现目标遗漏和任务完成率低的问题。针对这些问题,提出一种面向动态目标搜索与打击的空地协同自主任务分配方法,通过结合无人机视野范围广以及无人车机动性强的特点,提升空地无人系统的任务执行效率和区域覆盖率。一方面,针对未知运动目标,提出一种基于数字信息素的目标搜索模型,以平台协同收益和区域覆盖率为优化指标,保证在尽可能短的时间周期内发现区域中的所有目标。另一方面,面向动态到达的打击任务,构建基于可行路径规划的任务分配模型,以平台能耗和任务完成时间为目标函数,在保证任务完成率的同时,提高空地协同系统的资源利用率。与现有方法相比,所提方法能够在最短的时间内发现所有目标,区域覆盖率达到55%以上,且资源利用率为84.4%。实验结果表明,所提方法具备较好的目标搜索和任务执行能力。

地基空间目标监视雷达大气折射误差修正方法研究

大气介质垂向非均匀分布会使穿行其中的电磁波发生折射现象,尤其是其中的对流层和电离层,可造成电磁波传播延迟及路径弯曲,进而导致地基空间目标监视雷达测距和测角误差。本文基于射线追踪法实现了大气折射误差建模,推导了雷达测距和测角误差关于大气折射指数垂向分布的精确表达式,并详细阐述了地基空间目标监视雷达大气折射误差修正原理及流程,相比于传统的误差近似模型及修正方法,本文所提出的误差模型更准确、修正方法更有效。基于对流层和电离层模型获取大气折射指数垂向分布特性曲线,进行大气折射误差数值仿真,结果表明,本文方法在大气分层不同间隔取值下的误差补偿精度更高、性能更稳健,特别是在低仰角情况下,其性能优势更加明显。

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。

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。

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。

机载高分辨CSSAR地面加速运动目标成像方法

机载圆轨迹条带合成孔径雷达(circular stripmap synthetic aperture radar,CSSAR)的圆轨迹运动导致目标信号特性复杂、目标距离方程高阶分量显著等问题,给运动目标成像带来很多困难。考虑到高分辨是未来的发展趋势,针对机载高分辨CSSAR,提出一种地面加速运动目标成像方法。首先,针对目标距离方程高阶分量显著的问题,建立了四阶Taylor近似距离模型,以保证成像质量。接着,推导了目标二维频域信号模型,提出通过二维频域相位相乘来高效地实现目标聚焦,并利用差分进化算法高效地实现对距离模型各参数的精确搜索。最后,通过实验验证了所提方法的有效性。