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.

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.

天空地一体化多目标跟踪算法研究综述

为实现全时全域“泛在连接”,构建天空地一体化网络已成为国家重大需求,而基于天空地一体化网络下跨域协同系统进行多目标跟踪是其中一个重要的发展方向,其在军民用领域都极具应用价值。本文详细阐述了天空地一体化网络背景下多目标跟踪方法研究进展。首先,介绍了天空地一体化跨域协同多目标跟踪的研究背景与意义。其次,从基于视觉的多目标跟踪、基于模型的多目标跟踪和基于多模态融合的多目标跟踪三个方面概述了当前的代表性研究方法:在基于视觉的多目标跟踪算法方面,介绍了单摄像头和多摄像头融合的多目标跟踪方法;对于基于模型的多目标跟踪,先介绍了单传感器多目标跟踪方法,以及在多种复杂场景下的改进,然后介绍了多传感器融合方法;在基于多模态信息融合的目标跟踪方面,在对多传感器时空配准方法和有代表性的多模态信息融合方法介绍的基础上,概括了基于多模态融合的多目标跟踪算法。最后探讨了当前存在的问题和未来发展方向:无论基于视觉的还是基于模型的多目标跟踪方法都有不少问题有待解决,特别是两种方法的结合值得深入研究;在面临复杂干扰时,基于多传感器信息融合的多目标跟踪由于能实现信息的互补,成为未来的主流发展方向;此外,跨域协同系统,由于能利用更多的资源和信息,其多目标跟踪问题研究极具价值,不过其中通信安全问题和多目标跟踪模型轻量化问题值得探讨。本文对从事目标跟踪及空天地一体化协同控制相关理论与技术研究的科研工作者具有重要参考价值。

滚仰式光电吊舱对地目标的平移运动补偿

由于挂载于某无人飞行平台上的滚仰式光电吊舱具有极大的速高比,因此在对地观测时,会在光电传感器的曝光时间内产生较大像移,使图像变得模糊不清,从而影响成像质量;同时,快速刷新的视频场景使操作手很难发现感兴趣的目标,也给跟踪捕获带来困难。针对此情况,本文提出利用惯导及光电吊舱信息求取相对目标运动的角速度,并通过设备的反向运动补偿来消除载体平移运动的不利影响。同时,本文还给出了补偿的应用条件限制及误差分析,并就目标距离、补偿角速度与观测角度、飞行高度的关系进行了仿真,接着对惯导测量误差及框架角误差对补偿角速度误差的影响进行了仿真分析。经过实际的挂飞试验验证表明:在不考虑光电吊舱运动范围受限情况下,光电视轴可长时间持续稳定地指向任一目标区域,输出清晰稳定的图像而不受载体平移运动的影响,从而给操作手的观测及操作带来极大便利。

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.

基于双重YOLOv8-pose模型的探地雷达双曲线关键点检测与目标定位

探地雷达(GPR)是一种可用于地下目标识别的无损检测方法。针对现有方法存在不同尺度目标兼容性差、复杂图像识别难度大、无法精确定位等问题,该文提出一种基于双重YOLO姿态模型(YOLOv8-pose)的GPR双曲线关键点检测与目标定位,命名为双重YOLO关键点定位方法(DYKL),用于地下目标的检测与精确定位。所提模型架构包含两个阶段:首先,第1阶段是基于YOLOv8-pose模型的GPR目标检测,以确定候选目标的位置;接着,第1阶段的部分训练权重被共享并传递到第2阶段,后者以此为基础继续训练YOLOv8-pose网络,用于候选目标特征的关键点检测及获取,从而实现地下目标的自动化定位。通过与级联区域卷积网络(Cascade R-CNN)、更快的区域卷积网络(Faster R-CNN)、实时对象检测模型(RTMDet)以及“你只看一次”人脸模型(YOLOv7-face)4种先进的深度模型进行比较,所提模型平均识别准确率达到98.8%,性能优于其他模型。结果表明所提DYKL模型具有较高的识别准确性与较强的鲁棒性,可以为地下目标的精确定位提供参考。

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。