Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

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.

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.

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.

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.

Selection and modification of ground motion records using Newmark-Hall spectrum as target spectrum for long-period structures

Input ground motions have significant impacts on the uncertainty of structural responses in time-history analysis.In this study,records were selected and scaled for the evaluation of mean structural responses according to the target spectrum.The Newmark-Hall spectrum is closely related to seismic response of short,medium and long-period structures,so it was taken as the target spectrum here.The nonlinear time-history analyses of 9-story and 20-story steel moment-resisting frame structures were carried out as examples.They represent medium and long-period buildings,respectively.Three target spectra with risk of 50%,10%and 2%probabilities for exceedance in 50 years were calculated by the average Newmark-Hall spectrum method for three ground motion sets developed in the SAC Steel Project.The predicted structural mean responses of these Newmark-Hall spectra were compared with those calculated by the average spectral acceleration method for the same record set.It is found that both methods have similar accuracy for estimating the structural mean response.However,the method proposed herein is more effective in reducing the variability of the structural responses.Also,the proposed method is more advantageous for the time-history analysis of long-period structures or structures with more severe nonlinear responses under strong seismic excitations.

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.

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.

Color-polarization synergistic target detection method considering shadow interference

Aiming at the problem of shadow interference in UAV's ground reconnaissance,a color and polarization synergistic target detection method is proposed for anti-shadow interference,based on the influence of two physical characteristics(wavelength and polarization)under different illuminations.A valid fusion strategy is proposed via integrating two separate detection results on color and polarization images.Moreover,a local enhancement and recognition module(LER)is introduced to boost the performance.Based on our built dataset,experimental results show that our method achieves mAPof 87.76%,and12.37%higher than that of color image and 14.68%higher than that of polarization image.

基于模态分解的地基雷达慢速弱目标检测方法

在复杂地物场景中使用地基雷达探测地面动目标时,慢速弱目标与地杂波的频谱通常难以区分,传统的目标检测手段性能受限。针对此问题,提出一种基于模态分解的地基雷达慢速弱目标检测方法。首先设置较低的门限进行单元平均-恒虚警率(cell average-constant false alarm rate,CA-CFAR)检测,初步筛选目标;接着对慢时间信号进行复经验模态分解(complex empirical mode decomposition,CEMD),分离地杂波和动目标;最后利用地杂波和动目标多普勒频谱的对称性差异检测目标。将该检测方法分别与CA-CFAR和正交投影-奇异值分解-恒虚警率(orthogonal projection-singular value decomposition-constant false alarm rate,OP-SVD-CFAR)检测的性能进行比较,并通过仿真数据和两组不同目标的实测数据进行验证。结果表明,所提方法能够提升目标的信杂噪比;在检测概率相同的条件下,其虚警率比CA-CFAR显著降低;在检测概率相同且较高的情况下,所提方法比OP-SVD-CFAR的虚警率也更低,证明了所提方法的有效性。

增强-检测级联SAR地面目标检测网络

在合成孔径雷达地面目标检测任务中,传统检测方法因为在处理过程中采用固定模型假设而导致性能严重下降。卷积神经网络作为一种基于数据驱动的方法,在拥有足够的训练集时可以显著提高目标检测的准确性,但在检测陆地背景下的微小目标时性能仍不稳定。为了应对这些挑战,提出了一种先增强后检测的地面目标检测框架。其中包括以Transformer为骨干网络的增强网络、增强目标特征区分度的跨特征空间注意力模块以及具有多尺度特征的检测网络。形成一个级联的目标检测网络架构,以实现更好的推理性能。使用MSTAR基准数据集对提出的网络进行实验,证明提出的级联网络在各项指标上超过其他现有方法,其精度最高可以达到93.6%。

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.

Deep convolutional neural network for meteorology target detection in airborne weather radar images

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.

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。

三维探地雷达体素化局部熵特征提取地下目标

针对目前三维探地雷达未充分挖掘三维空间信息,数据处理主要基于对二维切片图像进行分析解译的问题,本文提出了一种三维探地雷达数据体素化后基于离散点云的局部熵提取地下目标的方法。首先,将获取的三维探地雷达数据体素化为离散三维点云;然后,计算整个区域体素化后点云的局部熵,通过支持向量机(SVM)对土壤背景和地下目标从多个尺寸进行区分;最后,以城市道路地下环境为研究对象进行试验分析。试验结果表明,该方法提取地下目标的准确率高达84.2%,遗漏地下目标的漏测率低至9.8%。该方法准确有效,为三维探地雷达提取地下目标提供了一种新的方法。