KUJAWSKI A , DUDEK T . Analysis and visualization of data obtained from camera mounted on unmanned aerial vehicle used in areas of urban transport [J ] . Sustainable Cities and Society , 2021 ,72:103004.

吴岸平 , 郭正 , 侯中喜 , 等 . 不确定环境下无人机区域目标搜索及载荷参数影响 [J ] . 国防科技大学学报 , 2020 , 42 ( 4 ): 35 - 42 .

WU A P , GUO Z , HOU Z X , et al. Regional target search and load parameter impact of UAVs in uncertain environments [J ] . Journal of National University of Defense Technology , 2020 , 42 ( 4 ): 35 - 42 . (in Chinese)

王东振 , 张岳 , 赵宇 , 等 . 基于RRT-Dubins的无人机航迹优化方法 [J ] . 兵工学报 , 2024 , 45 ( 8 ): 2761 - 2773 . DOI: 10.12382/bgxb.2023.0611 http://doi.org/10.12382/bgxb.2023.0611 针对多障碍物环境下考虑无人机(Unmanned Aerial Vehicle,UAV)始末位姿、转弯半径和航迹长度的1阶光滑约束的UAV航迹规划问题,提出一种基于快速搜索随机树(Rapidly-exploring Random Trees,RRT)算法和Dubins曲线以局部最优逼近全局最优的UAV航迹优化方法。利用RRT算法和基于贪心算法的剪枝优化方法,在二维任务空间中规划出满足避障要求的可行离散航路点。采用多条Dubins曲线平滑连接航路点,根据UAV始末位姿确定首尾曲线端点,基于UAV性能、障碍物和飞行参数的约束关系,建立多约束的航迹优化数学模型。通过粒子群优化算法确定曲线类型,同时优化曲线连接处位姿和曲线半径,获得最短航迹。仿真结果表明:所提方法得到的航迹与其他方法相比,在不同障碍物数量和始末位姿的多种场景中,平均长度缩短了11.48%,在避开障碍物的同时,满足UAV动力学约束。

WANG D Z , ZHANG Y , ZHAO Y , et al. A UAV trajectory optimization method based on RRT-Dubins [J ] . Acta Armamentarii , 2024 , 45 ( 8 ): 2761 - 2773 . (in Chinese) DOI: 10.12382/bgxb.2023.0611 http://doi.org/10.12382/bgxb.2023.0611 A unmanned aerial vehicle (UAV) trajectory optimization method based on the rapidly-exploring random trees (RRT) algorithm and Dubins curves is proposed to address the problem of UAV trajectory planning in multi-obstacle environments. The initial and final poses, turning radius, and trajectory length, and first-order smoothness constraint of UAV are considered in the trajectory planning. The RRT algorithm and a pruning optimization method based on a greedy algorithm are utilized to plan the feasible discrete waypoints that satisfy the obstacle avoidance requirements in a two-dimensional task space. Multiple Dubins curves are employed to smoothly connect the waypoints. A multi-constraint trajectory optimization mathematical model is established based on the UAV's initial and final poses, and the constraints related to the UAV's performance and obstacles. The particle swarm optimization (PSO) algorithm is employed to determine the curve types and optimize the poses at the curve connections and the curve radii, thereby obtaining the shortest trajectory. Simulated results demonstrate that the proposed method reduces the average trajectory length by 11.48% in various scenarios with different numbers of obstacles and varying initial and final positions, while satisfying the UAV's kinematic constraints and avoiding obstacles compared to other methods.

任双 , 周洁 , 高嵩 , 等 . 基于注意力机制的无人机集群协同分群控制算法 [J ] . 电子学报 , 2023 , 51 ( 7 ): 1898 - 1905 . DOI: 10.12263/DZXB.20221378 http://doi.org/10.12263/DZXB.20221378 针对基于避碰、组队和聚集规则的无人机集群无法响应部分感知引起的多重刺激问题，本文提出一种基于注意力机制的无人机集群协同分群控制算法.为确保无人机对邻居信息的高效选择，文中考虑视线遮挡因素设计感知规则，然后引入注意力机制计算交互邻居中无人机对局部群体有序性的贡献；为解决多重刺激下无人机决策冲突的问题，文中设计一种基于注意力机制的状态转换模型，特别地设计亚激活状态计算刺激源的动态权重，以提高集群响应多重刺激的灵敏度；为实现无人机对目标持续准确的跟踪，基于无人机的运动状态确定运动策略，然后调整运动模型；通过仿真分析集群响应多重刺激时运动轨迹和运动方向的变化过程，并利用应激精度、子群序参量和分群耗时对分群运动过程进行分析.仿真结果表明：所提算法使无人机集群在5 s左右即可完成分群运动，实现对刺激快速准确的跟踪.

REN S , ZHOU J , GAO S , et al. UAV swarm cooperative clustering control algorithm based on attention mechanism [J ] . Acta Electronica Sinica , 2023 , 51 ( 7 ): 1898 - 1905 . (in Chinese)

宫远强 , 张业鹏 , 马万鹏 , 等 . 无人机蜂群中的群体智能涌现机理 [J ] . 兵工学报 , 2023 , 44 ( 9 ): 2661 - 2671 . DOI: 10.12382/bgxb.2022.1181 http://doi.org/10.12382/bgxb.2022.1181 针对无人机蜂群中自主协同行为涌现机理难以解释的问题,提出一种多Agent系统中的自主协同行为涌现的分析方法,对系统的微观个体层、中观结构层和宏观网络层三个层面展开分析,自底向上地量化分析系统动态演化过程,揭示系统从微观到宏观的内在逻辑以及系统演变中的一些问题。通过计算实验方法构建无人机蜂群的计算实验模型,根据蜂群作战的关键特征设计无人机蜂群社团信息网络,引入公共物品博弈机制构建蜂群合作演化模型,并给出社团网络上蜂群的演化动力学过程。通过数值模拟,从无人机蜂群系统的不同层面量化分析蜂群协同行为的涌现现象,从而认识蜂群自主协同行为的涌现机理,并为无人机蜂群协同机制的优化提供决策支持。

GONG Y Q , ZHANG Y P , MA W P , et al. Emergence mechanism of collective intelligence in UAV swarms [J ] . Acta Armamentarii , 2023 , 44 ( 9 ): 2661 - 2671 . (in Chinese)

朱惠民 , 贾正荣 , 王航宇 , 等 . 针对不同视场辅助信标的无人机目标定位方法 [J ] . 国防科技大学学报 , 2019 , 41 ( 3 ): 125 - 136 .

ZHU H M , JIA Z R , WANG H Y , et al. UAV target localization method based on different field-of-view auxiliary beacons [J ] . Journal of National University of Defense Technology , 2019 , 41 ( 3 ): 125 - 136 . (in Chinese)

XU C , HUANG D Q , LIU J Y . Target location of unmanned aerial vehicles based on the electro-optical stabilization and tracking platform [J ] . Measurement , 2019 , 147 : 106848 .

陈晨 , 关棒磊 , 尚洋 , 等 . 受限观测条件下光电对地定位的全局最优化方法 [J ] . 光学学报 , 2023 , 43 ( 12 ): 144 - 152 .

CHEN C , GUAN B L , SHANG Y , et al. Global optimization method for electro-optical ground positioning under constrained observation conditions [J ] . Acta Optica Sinica , 2023 , 43 ( 12 ): 144 - 152 . (in Chinese)

CHANG H H , WU G L , CHIANG M H . Remote sensing image registration based on modified SIFT and feature slope grouping [J ] . IEEE Geoscience and Remote Sensing Letters , 2019 , 16 ( 9 ): 1363 - 1367 .

YANG H , LI X R , ZHAO L Y , et al. A novel coarse-to-fine scheme for remote sensing image registration based on SIFT and phase correlation [J ] . Remote Sensing , 2019 , 11 ( 15 ): 1833 .

PAUL S , UDAYSANKAR D , NAIDU Y , et al. An efficient SIFT-based matching algorithm for optical remote sensing images [J ] . Remote Sensing Letters , 2022 , 13 ( 11 ): 1069 - 1079 .

MOHAMMADI N , SEDAGHAT A , JODEIRI RAD M . Rotation-invariant self-similarity descriptor for multi-temporal remote sensing image registration [J ] . The Photogrammetric Record , 2022 , 37 ( 177 ): 6 - 34 .

YAO Y X , ZHANG Y J , WAN Y , et al. Multi-modal remote sensing image matching considering co-occurrence filter [J ] . IEEE Transactions on Image Processing , 2022 , 31 : 2584 - 2597 . DOI: 10.1109/TIP.2022.3157450 http://doi.org/10.1109/TIP.2022.3157450 Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps: (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horizontal and vertical directions to optimize the matching distance function. The optimization results then are rematched, and the outliers are eliminated using a fast sample consensus algorithm. We performed comparison experiments on our CoFSM method with the scale-invariant feature transform (SIFT), upright-SIFT, PSO-SIFT, and radiation-variation insensitive feature transform (RIFT) methods using a multi-modal image dataset. The algorithms of each method were comprehensively evaluated both qualitatively and quantitatively. Our experimental results show that our proposed CoFSM method can obtain satisfactory results both in the number of corresponding points and the accuracy of its root mean square error. The average number of obtained matches is namely 489.52 of CoFSM, and 412.52 of RIFT. As mentioned earlier, the matching effect of the proposed method was significantly greater than the three state-of-art methods. Our proposed CoFSM method achieved good effectiveness and robustness. Executable programs of CoFSM and MRSI datasets are published: https://skyearth.org/publication/project/CoFSM/.

田丹 , 陈钰坤 . 基于改进ORB算法的遥感图像匹配 [J ] . 沈阳大学学报(自然科学版) , 2024 , 36 ( 1 ): 34 - 39 .

TIAN D , CHEN Y K . Remote sensing image matching based on improved ORB algorithm [J ] . Journal of Shenyang University(Natural Science Edition) , 2024 , 36 ( 1 ): 34 - 39 . (in Chinese)

唐大全 , 柳向阳 , 邓伟栋 , 等 . 基于迭代无迹卡尔曼滤波的小型无人机目标定位方法 [J ] . 指挥控制与仿真 , 2019 , 41 ( 1 ): 104 - 108 . DOI: 10.3969/j.issn.1673-3819.2019.01.021 http://doi.org/10.3969/j.issn.1673-3819.2019.01.021 为了提高无人机地面目标跟踪定位的准确性,用无人机光电成像平台将目标锁定在视场中心,根据坐标转换将视场角度转换为地理坐标系下的角度,建立目标跟踪的系统方程。无迹卡尔曼滤波用来解决跟踪定位的非线性估计,由于容易出现跟踪速度慢和发散的问题,迭代无无迹卡尔曼滤波由极大似然估计法确定迭代条件,增加滤波的精度和时间。仿真分析表明,迭代无迹卡尔曼滤波能够明显改善滤波准确度和滤波的速度的问题,具有一定的实用价值。

TANG D Q , LIU X Y , DENG W D , et al. Target localization method for small UAVs based on iterative unscented Kalman filter [J ] . Command Control & Simulation , 2019 , 41 ( 1 ): 104 - 108 . (in Chinese)

陈丹琪 , 金国栋 , 谭力宁 , 等 . 基于非线性最小二乘法的无人机机载光电平台目标定位 [J ] . 光电工程 , 2019 , 46 ( 9 ): 84 - 92 .

CHEN D Q , JIN G D , TAN L N , et al. Target localization of UAV airborne electro-optical platform based on nonlinear least squares method [J ] . Opto-Electronic Engineering , 2019 , 46 ( 9 ): 84 - 92 . (in Chinese)

白冠冰 , 宋悦铭 , 左羽佳 , 等 . 机载光电平台的对地多目标定位 [J ] . 光学精密工程 , 2020 , 28 ( 10 ): 2323 - 2336 .

BAI G B , SONG Y M , ZUO Y J , et al. Multi-target ground positioning of airborne electro-optical platform [J ] . Optics and Precision Engineering , 2020 , 28 ( 10 ): 2323 - 2336 . (in Chinese)

CUOMO S , DE ROSA M , PICCIALLI F , et al. Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy [J ] . Mathematics and Computers in Simulation , 2024 , 223 : 368 - 379 .

ZHANG Z Y , LI Y , LIU Z C , et al. Enhancing the reliability of shipborne INS/GNSS integrated navigation system during abnormal sampling periods using Bi-LSTM and robust CKF [J ] . Ocean Engineering , 2023 , 288 : 115934 .

SHINDE G J , GIRI D K . UKF based relative attitude estimation of two spacecrafts flying in formation [J ] . IFAC-PapersOnLine , 2024 , 57 : 385 - 390 .

吴学礼 , 高锋 , 甄然 . 一种基于改进萤火虫算法优化粒子滤波的轨迹预测的方法:201910587699.9 [P ] .2019-07-02.

WU X L , GAO F , ZHEN R . A trajectory prediction method based onan improved mutant firefly algorithm optimized particle filter:201910587699.9 [P ] .2019-07-02. (in Chinese)

李美红 , 尹健 , 徐劲祥 . 基于EKF的机载光电吊舱目标定位研究 [J ] . 弹箭与制导学报 , 2016 , 36 ( 6 ): 157 - 161 .

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文尚胜 , 丘志强 , 许函铭 , 等 . 基于差分算法优化的自复位粒子滤波算法 [J ] . 华南理工大学学报(自然科学版) , 2023 , 51 ( 3 ): 133 - 145 . DOI: 10.12141/j.issn.1000-565X.220368 http://doi.org/10.12141/j.issn.1000-565X.220368 粒子滤波器作为常用的非高斯非线性的滤波方法，已成功地应用于各种工程领域。然而传统的重采样方法导致了粒子贫化的问题，严重降低了滤波估计的精度与鲁棒性。文中提出一种结合跟踪失败检测与改进差分优化融合的自复位粒子滤波方法。首先通过跟踪失败识别方法对滤波估计值进行初步检验，在正常跟踪时不启用优化策略，算法性能与标准粒子滤波无异；在跟踪失败时，通过差分算法对粒子集进行复位，复位过程中设置了粒子置信区间的上下界以防粒子过度集中，并结合检验指示值规避对粒子的多次优化，以缩短算法的估计时间。仿真结果表明，文中算法通过动态调节方式继承了标准粒子滤波和差分进化粒子滤波的优点，有效提高了滤波估计的鲁棒性与估计精度，可在滤波成功时避免启用优化策略以降低算法的整体时间复杂度，并在滤波失败时启用差分优化策略进行自我复位以提高算法估计精度；且在相同定位精度下，其所需粒子数较标准粒子滤波更少，整体时耗较差分进化粒子滤波更低，在建模不确定时也可表现出良好的效果。

WEN S S , QIU Z Q , XU H M , et al. Self-resetting particle filter algorithm optimized by differential algorithm [J ] . Journal of South China University of Technology (Natural Science Edition) , 2023 , 51 ( 3 ): 133 - 145 . (in Chinese)

陈志敏 , 田梦楚 , 吴盘龙 , 等 . 基于蝙蝠算法的粒子滤波法研究 [J ] . 物理学报 , 2017 , 66 ( 5 ): 47 - 56 .

CHEN Z M , TIAN M C , WU P L , et al. Research on particle filter method based on bat algorithm [J ] . Acta Physica Sinica , 2017 , 66 ( 5 ): 47 - 56 . (in Chinese)

田梦楚 , 柳林燕 , 陈志敏 , 等 . 基于弹性机制的萤火虫优化粒子滤波算法 [J ] . 控制与决策 , 2024 , 39 ( 2 ): 420 - 428 .

TIAN M C , LIU L Y , CHEN Z M , et al. Firefly optimization particle filter algorithm based on elastic mechanism [J ] . Control and Decision , 2024 , 39 ( 2 ): 420 - 428 . (in Chinese)