Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Group Target Tracking in WSN Based on Convex Hulls Merging

When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group target. In order to decrease the amount of energy spent on active sensing and communications, a flexible boundary detecting model for group target tracking in WSN is proposed, in which, the number of sensors involved in target tracking is adjustable. Unlike traditional one or multiple individual targets, the group target usually occupies a large area. To obtain global estimated position of group target, a divide-merge algorithm using convex hull is designed. In this algorithm, group target’s boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed by a cluster of boundary sensors. Then, the information of these small convex hulls is sent back to a sink. Finally, big convex hull merged from these small ones is considered as the group target’s contour. According to our metric of precision evaluation, the simulation experiments confirm the efficiency and accuracy of this algorithm.

Simultaneous ISAR imaging of group targets flying in formation

This paper proposes a novel inverse synthetic aperture radar（ISAR） imaging method based on second-order keystone transform（KT） and Sandglass transform for group targets flying in a formation with constant accelerated rectilinear motion in the same radar beam. First, range curvature and range walk of each sub-target among group targets are corrected by the second-order KT combined with the quadratic phase term compensation. After range alignment, the signals in each range frequency cell can be modelled as multiple chirp signals and then the Sandglass transform is utilized to cross-range imaging, which transforms the time–frequency distribution of the signals in each range frequency cell into beelines parallel to the slow time axis simultaneously. Finally, cross-range profiles of group targets in each range frequency cell are obtained via a projection of the perk of every scatterer in the two-dimensional accumulation plane onto the frequency axis. The advantage of the proposed method is that it can align range profiles of each sub-target simultaneously and image cross-range profiles directly without separating the returned signals, which simplifies the operation procedure. Simulation results are used to demonstrate the effectiveness of the proposed method.

FAST DETECTING TARGET GROUPS IN SAR IMAGES

A successful algorithm for detecting target groups is presented. Firstly, A global Constant False Alarm Rate (CFAR) detector is utilized to locate the potential target regions, and then the features are com- puted for target discrimination based on voting mechanism. Finally, Target groups are extracted. The results of experiments show the validity of this algorithm.

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

A target group tracking algorithm based on a hybrid sensor network

Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

Creating International Brands——Target for Hubao Group

The Jiangsu Hubao Group, now very active in the garment sector, used to be a small shirt factory with a loaned capital of only a few hundred thousand yuan(RMB). Since its founding in 1989, the group has been aiming at the international first-class level, and has formulated and implemented international famous brand strategy, with quality products occupying the market. After

基于联合GLMB滤波器的可分辨群目标跟踪

针对联合广义标签多伯努利(joint generalized labeled multi-Bernoulli, J-GLMB)滤波算法中群目标之间距离较近、容易关联错误的问题,结合超图匹配(hypergraph matching, HGM)提出一种基于HGM-J-GLMB滤波器的可分辨群目标跟踪算法。首先,采用J-GLMB滤波器估计群内各目标的状态、数目及轨迹信息,并利用HGM结果提升量测与预测状态之间的关联性能。其次,通过图理论计算邻接矩阵,获取群结构信息和子群数目。随后,利用群结构信息估计协作噪声,进而校正目标的预测状态。最后,通过平滑算法改善滤波效果,并设置轨迹长度阈值,使其在平滑状态达到消除短轨迹的目的。仿真实验表明,所提算法在线性系统下能有效提升群目标跟踪性能。

社会建构、反馈效应与延迟退休年龄政策停滞

党的十八届三中全会以来,我国屡次提出研究和制定“渐进式延迟退休年龄政策”的方针策略,然时至今日,全国性的延迟退休政策尚未出台。以社会建构与政策设计理论为理论基础与分析框架,以1951—2021年54份中央政府文件为样本,考察目标群体的社会建构如何通过反馈效应影响延迟退休年龄政策进程,研究发现:干部和高级专业技术人员被正面建构且因政策受益,产生正反馈,政策长期仅适用于该群体,普通劳动者被正面建构但因政策受损,产生负反馈,以致全国性政策无法出台,政策陷入停滞;而正反馈效应长期积累促使社会建构转向,社会环境因素改变政策认知,进而推动政策进程。因此,政策制定者坚定决心,不断优化政策设计,回应目标群体的社会建构转向;党和中央政府以积极应对人口老龄化战略吸纳延迟退休年龄政策,塑造公众政策认知;地方政府积极开展政策试验,总结经验教训,这些措施都有助于加快政策进程。

城市户外劳动者服务站点空间分布评价:以厦门市厦门岛为例

基于人本主义理念,从城市户外劳动者服务站点主要服务群体的需求出发,从需求目标、模型评价、反馈评价3个层面,构建交通便捷度、道路旁站点供需指数、用地内站点供需指数等评价指标及定量化评价模型,以厦门市厦门岛爱心驿站为例,研究城市户外劳动者服务站点空间分布的合理性,并通过主要服务群体反馈评价服务站点对服务群体需求的满足程度。结果表明:厦门岛服务站点交通便捷度空间分异显著;分布在城市支路和主干道/快速路旁的服务站点数量供需基本平衡,次干道旁的站点呈轻度供不应求;居住、商业设施、商务设施、交通设施四类用地内服务站点的供需匹配度相对较好,教育科研、文化设施和体育、医疗卫生等用地的供需匹配度相对较差,服务群体对各类用地满意度评价与供需匹配状态基本契合。建议从制定规划建设标准、统筹站点规划布局、盘活存量更新改造等方面优化城市户外劳动者服务站点布局。

频率分组编码信号的相参积累算法

针对捷变频信号的相参积累处理进行研究,提出了一种基于频率分组编码信号的相参积累算法。以线性调频(Linear frequency modulation,LFM)信号为基础,构建了频率分组编码信号,使得发射信号的脉冲载频序列具有一定随机性,保证了波形的抗干扰能力。同时针对频率捷变信号相位非相参带来的主瓣展宽和旁瓣抬高问题,设计了对应编码信号的相参处理方法。首先对回波脉压后的信号进行高分辨距离补偿,然后通过速度遍历插值和距离一致性校正实现脉组内相参处理,最后利用编码信号载频序列的优势实现脉组间相参处理。在仿真实验中,对构建的编码信号相较于LFM信号在抗干扰方面的优越性进行了验证,同时验证了所提相参处理方法相较于基于压缩感知的稀疏重构算法的有效性。

基于群结构集成势均衡多目标多伯努利滤波器的群目标跟踪算法

群目标跟踪已被广泛应用于作战对抗、自动驾驶、低空防御等各个军民领域。群目标是指由多个单目标组成,并以相同的速度或方向进行移动的一组或多组目标集群。鉴于随机有限集(Random Finite Set,RFS)滤波器在处理多目标数据关联方面的优势,现有的群目标跟踪算法大多基于RFS滤波器。然而,这些滤波器在对群目标进行跟踪时大多忽略了各目标之间的关联、依赖关系等问题,为此,提出了一种基于群结构集成的势均衡多目标多伯努利(Cardinality Balanced Multi-target Multi-Bernoulli,CBMeMBer)滤波器的群目标跟踪算法。具体而言,首先通过邻接矩阵对目标群结构进行估计,即将群目标看作无向图,利用各目标之间的距离来估计群目标的邻接矩阵,进而将群目标划分为多个子群。并根据各子群中目标的运动状态,将其划分为群中心和群成员两种类别,分别建立了运动方程。而后在预测步骤中,利用估计的群结构来对目标状态进行预测。特别地,在滤波器的高斯混合实现步骤中,多个高斯分量被用来拟合相对应的各伯努利分量,但过多高斯分量的存在会降低对群目标状态估计的准确性,从而降低群结构估计的准确性,因此,在状态提取阶段,本文所提算法对高斯分量进行修剪,即对更新步骤后的每个伯努利分量中所包含的高斯分量进行筛选,只保留一个权重最大的高斯分量。最后,仿真结果表明,本文所提算法实现了对群目标的稳定跟踪,且跟踪性能优于传统的CBMeMBer滤波器。

基于IPSO-LSTM的井下动目标位置预测实验研究

提升井下人员定位精度能够加强矿山安全监测,最大程度保障井下人员的生命安全。针对现有测距类算法受现场环境影响致使定位精度不足的问题,提出一种基于IPSO-LSTM的定位模型,应用于井下动目标的位置预测。采用LSTM构建指纹定位模型,通过UWB无线模块采集距离信息以构建距离-位置指纹关系数据库,利用数据库对PSO-LSTM模型进行训练,最后将训练好的模型进行目标轨迹预测。为比较不同改进策略对PSO的提升效果,对比了混沌映射随机初始化种群位置、非线性惯性权重递减、非对称优化学习因子和适应度函数优化4种改进策略,实验证明改进的PSO优化算法收敛速度快、鲁棒性好。为验证IPSO-LSTM的定位效果,以平均定位误差作为评价指标,将IPSO-LSTM模型与Chan算法、PSO-LSTM模型、LSTM神经网络、SSA-LSTM模型和GWO-LSTM进行对比,结果显示,IPSO-LSTM定位模型的平均定位误差为30 mm,相对传统Chan算法、LSTM、PSO-LSTM模型分别提升了76%、49%、24%。为降低局部误差偏大的现象,采用中值滤波对输入信息处理,进一步提升了定位精度。研究对进一步提高现有井下动目标定位系统的精度和稳定性具有重要意义和参考价值。

宽带跳频TDMA辐射源网群的定位及网群划分方法

为了解决现有方法难以对宽带跳频时分多址(frequency hopping-time division multiple access,FH-TDMA)辐射源网群定位的问题,提出一种仅利用波达方向(direction of arrival,DOA)信息的多站定位及网群划分方法。首先通过模拟滤波对各站接收到的信号进行频段划分,通过基于多相滤波的数字信道化方法把信号输出到多个信道中;然后通过直接定位(direct position determination,DPD)算法计算得出各时隙辐射源的位置;最后先通过不同参数的基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法选择出最佳的辐射源定位结果,再根据提出的基于先验信息的改进K-means聚类算法进行网群划分。所提方法实现了采样率和信号处理速率的降低,且无需站间严格的时间同步。结果表明,所提方法有效估计出了辐射源的数量、位置,以及网群划分情况。

“双减”政策下农村学校课后服务供给的优化——基于重庆市的调查数据分析

通过对农村学校学生和家长关于课后服务需求进行调查,结果表明,学生和家长对当下课后服务处于较为满意的状态。同时,学生和家长对课后服务还存在如要求活动场地从封闭性走向开放性、活动内容从单一性转为多样化、师资水平从低水平提到高质量、服务管理从“一刀切”转向个性化等优化意愿。通过对政策文本、目标群体、教育资源和管理监督的成因分析,认为需要通过在政策上增强适切、细化操作;在资源上积极开发、有效利用;在师资上提高质量、提升效能;在管理上完善措施、强化督导并争取目标群体的正确理解与积极支持等路径,实现农村学校课后服务的优化。

非饱和打击场景下考虑附带毁伤的火力分配方法

针对弹药资源不足的非饱和攻击存在或无法确保重要目标达到毁伤预期,或效率过低不适合工程应用等问题,提出一种考虑附带毁伤和毁伤约束的火力分配方法。建立集群目标分组策略从而考虑目标的附带毁伤,提高毁伤概率计算的合理性;为提高火力分配的求解效率,将火力分配问题等价转换为1维整数规划。通过2个仿真算例验证的结果表明:该方法具备有效性,且随着问题规模的增加火力分配耗时增加缓慢,可满足工程应用要求。

基于随机有限集滤波器的可分辨群目标跟踪技术研究综述

群目标跟踪在救灾搜救、海陆防御和战场作战等领域展现出广阔的应用前景。与传统多目标跟踪不同,群目标跟踪不仅涉及对多个个体目标的跟踪,还涉及对一个群体目标的跟踪。在群体中各个子目标需要同步运动以避免碰撞,同时群体中的子目标数量和群结构还将随着时间推移而改变。根据子目标数量和传感器分辨率的不同,群目标跟踪问题包含可分辨、不可分辨、部分可分辨以及部分不可分辨群目标跟踪等多个类别。其中,可分辨群目标跟踪问题需要同时对群体结构、群内子目标交互和数目进行估计。现有研究主要关注基于传统数据关联和随机有限集滤波器的可分辨群目标跟踪方法,其中,基于随机有限集滤波器的方法通过将多个目标状态联合建模成随机有限集,缓解了数据关联问题,从而可更好地适应跟踪场景。为更好地展示群目标跟踪方法的研究进展,综述了近年来基于随机有限集滤波器的可分辨群目标跟踪的若干代表性方法,包括基于多目标多伯努利滤波器、基于标签随机有限集滤波器和基于泊松多伯努利混合滤波器的群目标跟踪方法。这些方法在处理可分辨群目标跟踪问题时展示出了显著的优势。最后,探讨了存在的问题和未来的发展方向。

DEGREE:一种基于Delaunay三角的任意群目标外形识别方法

集群目标相比单一甚至多目标表现出复杂时变集群特性,其外形估计与评价颇具挑战性。针对任意形状的集群目标外形估计与评价难题,本文提出了一种基于数据驱动的多传感器集群目标群形状建模与识别方法,以及一种群目标外形拟合度评判指标。所提算法由三个部分组成:首先,采用信息洪泛(Flooding)方法实现强连接的多传感器对视场中目标信息的采集与传播;其次,采用密度峰值聚类实现观测数据的聚类;最后,采用改进Delaunay三角网络算法实现群目标外形的拟合。所提群外形拟合度指标可用于对群目标外形估计准确度定量评价。通过与超曲面、随机矩阵等经典方法进行比较,证实了所提出算法的有效性和可靠性。

高频地波雷达编队目标分辨方法

由于方位分辨性能受限和多普勒模糊问题,高频地波雷达(HFSWR)难以分辨编队内的子目标。该文将逆合成孔径原理应用于HFSWR,提出横向距离成像方法。该方法首先在运动补偿处理中消除径向运动分量、保留旋转运动,实现聚焦的多普勒谱,并分辨多个子目标。然后,横向距离定标处理将频率坐标转化为横向距离,进而估计子目标间的间距。其中,由最小熵准则来估计径向速度和径向加速度,由分段数字波束形成来估计旋转角度。为了在有限积累时间条件下提升分辨效果,采用基于数据复用方式的重叠相干积累来增加扫频周期数。仿真实验表明,所提方法的理论分辨率在几百米至几千米范围内,运动补偿和横向距离定标实现了单纵队的分辨及其间距估计,重叠积累具有缩窄主峰和降低旁瓣水平的效果。

昆虫Ⅱ家族几丁质酶的生理功能、结构特征及其抑制剂研究进展

昆虫几丁质酶可以降解昆虫体壁和围食膜中的几丁质,在昆虫蜕皮等生命活动中发挥重要功能。作为参与昆虫几丁质降解系统的关键酶,Ⅱ家族几丁质酶(ChtⅡ)是少见的多结构域、性质复杂的几丁质酶。对昆虫中编码ChtⅡ的基因沉默会导致昆虫蜕皮失败及死亡率增加。对ChtⅡ的生化性质及晶体结构分析表明,其在几丁质降解系统中起到了“先锋及攻坚”的作用。因此,ChtⅡ有望成为新的杀虫剂靶标。本文综述了近年来关于ChtⅡ的生理功能、结构特征及其抑制剂的研究成果,可为基于昆虫几丁质酶的新杀虫剂创制提供参考。