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CHEN W J , ZOU Q , TU W J . Research on intelligent development trend and application of air-to-surface weapons [J ] . Helicopter Technique , 2021 ( e2 ): 69 - 72 . (in Chinese)

郝菁 . 基于惯导/数据链协同的无人机集群导航定位算法研究 [D ] . 北京 : 中国电子科技集团公司电子科学研究院 , 2019 .

HAO J . INS/Intra flight datalink cooperative positioning algorithm for UAVs [D ] . Beijing : China Academic of Electronics and Information Technology , 2019 . (in Chinese)

裴凌 , 李涛 , 花彤 , 等 . 多源融合定位算法综述 [J ] . 南京信息工程大学学报(自然科学版) , 2022 , 14 ( 6 ): 635 - 648 .

PEI L , LI T , HUA T , et al. A survey of multi-source fusion positioning algorithms [J ] . Journal of Nanjing University of Information Science & Technology(Natural Science Edition) , 2022 , 14 ( 6 ): 635 - 648 . (in Chinese)

ZHUANG Y , SUN X , LI Y , et al. Multi-sensor integrated navigation/positioning systems using data fusion: from analytics-based to learning-based approaches [J ] . Information Fusion , 2023 , 95 : 62 - 90 . DOI: 10.1016/j.inffus.2023.01.025 http://doi.org/10.1016/j.inffus.2023.01.025 https://linkinghub.elsevier.com/retrieve/pii/S1566253523000398 https://linkinghub.elsevier.com/retrieve/pii/S1566253523000398

韩煜 , 宋韬 , 郑多 , 等 . 基于冲突触发避碰机制的无人飞行器集群协同制导技术 [J ] . 兵工学报 , 2023 , 44 ( 7 ): 1881 - 1895 . DOI : 10.12382/bgxb.2022.0152 http://doi.org/10.12382/bgxb.2022.0152 协同打击是提升飞行器集群整体作战效能的重要模式。针对密集飞行器集群协同打击目标过程中存在的相互碰撞或彼此干扰问题,建立典型的多目标协同打击的任务场景模型,揭示飞行器集群协同运动过程中的机动规律;基于效用函数最优理论研究避免碰撞的飞行器集群协调最优机动策略,保证飞行器之间避免碰撞。考虑飞行器集群协同制导中的时间/空间同步到达约束,采用基于时间管控的多约束协同制导策略,同时满足到达时间和脱靶量要求;引入基于冲突触发的协同避碰机制,提出兼顾避碰和协同打击的协同调整策略,求得满足避免碰撞与时空同步的协同制导方法。仿真结果表明,新提出的考虑避碰约束的飞行器集群协同制导方法能够保证飞行器间的安全距离约束,具有较强的协同时间管控能力和较高的制导精度,且仅需较少迭代计算,降低了对机载处理器的要求,为飞行器集群协同打击中的避碰问题提供了一种解决方法,具有较强的工程应用价值。

HAN Y , SONG T , ZHENG D , et al. Unmanned aerial vehicle cluster cooperative guidance technology based on conflict trigger mechanism [J ] . Acta Armamentarii , 2023 , 44 ( 7 ): 1881 - 1895 . (in Chinese) DOI: 10.12382/bgxb.2022.0152 http://doi.org/10.12382/bgxb.2022.0152 Cooperative strike is an important mode to improve the overall combat effectiveness of aircraft clusters. To address the problem of mutual collision or interference in the process of cooperative attack on targets by dense aircraft clusters, a typical mission scenario model of multi-target cooperative attack is established, and the maneuver law in the process of cooperative movement of aircraft cluster is revealed. Then, based on the optimal theory of utility function, the coordinated optimal maneuver strategy of aircraft cluster to avoid collision is studied, ensuring collision avoidance between aircrafts. Considering the time/space synchronous arrival constraint in aircraft cluster cooperative guidance, a multi-constraint cooperative guidance strategy based on time control is adopted, which meets the requirements of arrival time and miss distance at the same time. On this basis, a cooperative collision avoidance mechanism based on conflict triggering is introduced, and a cooperative adjustment strategy that considers collision avoidance and cooperative attack is proposed. And a cooperative guidance method satisfying collision avoidance and spatio-temporal synchronization is obtained. The simulation results show that the proposed aircraft cluster cooperative guidance method considering collision avoidance constraints ensures safe distance constraints between aircrafts and has strong aircraft cluster cooperative time control ability and high guidance accuracy. It requires less iterative calculation, reduces the requirements for airborne processors, and provides a solution to the collision avoidance problem in aircraft cluster cooperative attack. This method has strong engineering application value.

PENG J , ZHANG P , ZHENG L X , et al. UAV positioning based on multi-sensor fusion [J ] . IEEE Access , 2020 , 8 : 34455 - 34467 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

LU S Y , ZHI Y S , ZHANG S M , et al. Semi-direct monocular SLAM with three levels of parallel optimizations [J ] . IEEE Access , 2021 , 9 : 86801 - 86810 . DOI: 10.1109/ACCESS.2021.3071921 http://doi.org/10.1109/ACCESS.2021.3071921 https://ieeexplore.ieee.org/document/9398863/ https://ieeexplore.ieee.org/document/9398863/

MUNGUIA R , TRUJILLO J C , GUERRA E , et al. A hybrid visual-based SLAM architecture: local filter-based SLAM with keyframe-based global mapping [J ] . Sensors , 2021 , 22 ( 1 ): 210 . DOI: 10.3390/s22010210 http://doi.org/10.3390/s22010210 https://www.mdpi.com/1424-8220/22/1/210 https://www.mdpi.com/1424-8220/22/1/210 This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

WANG H C , WANG Z , ZHANG L , et al. A highly stable fusion positioning system of smartphone under NLOS acoustic indoor environment [J ] . ACM Transactions on Internet Technology , 2023 , 23 ( 2 ): 1 - 19 .

XU C , HE J , LI Y Y , et al. Optimal estimation and fundamental limits for target localization using IMU/TOA fusion method [J ] . IEEE Access , 2019 , 7 : 28124 - 28136 . DOI: 10.1109/ACCESS.2019.2902127 http://doi.org/10.1109/ACCESS.2019.2902127 https://ieeexplore.ieee.org/document/8654628/ https://ieeexplore.ieee.org/document/8654628/

ZOU A R , HU W W , LUO Y H , et al. An improved UWB/IMU tightly coupled positioning algorithm study [J ] . Sensors , 2023 , 23 ( 13 ): 5918 . DOI: 10.3390/s23135918 http://doi.org/10.3390/s23135918 https://www.mdpi.com/1424-8220/23/13/5918 https://www.mdpi.com/1424-8220/23/13/5918 The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

LI B B , HAO Z J , DANG X C . An indoor location algorithm based on kalman filter fusion of ultra-wide band and inertial measurement unit [J ] . AIP Advances , 2019 , 9 ( 8 ): 085210 . DOI: 10.1063/1.5117341 http://doi.org/10.1063/1.5117341 https://pubs.aip.org/adv/article/9/8/085210/128346/An-indoor-location-algorithm-based-on-Kalman https://pubs.aip.org/adv/article/9/8/085210/128346/An-indoor-location-algorithm-based-on-Kalman Indoor positioning technology has been widely used in today’s life, but due to the influence of multipath effect, the positioning signal is attenuated or even interrupted seriously, resulting in obvious reduction or even failure of positioning accuracy. Therefore, the emerging multi-sensor joint positioning has become the general trend of the development of positioning technology, in which Ultra-Wide Band (UWB) and Inertial Measurement Unit (IMU) have their own features in positioning and navigation. So this paper combines the advantages of UWB and IMU to achieve accurate positioning in complex environment. Firstly, the signal transmission law in complex environment is obtained by distinguishing Line of Sight (LOS) from NLOS (Non Line of Sight) environment. Secondly, the maximum likelihood estimation algorithm is used to eliminate the influence of NLOS on the transmitted signal, and then the extended Kalman filter information fusion strategy is used. The ranging information of UWB and the angle information of IMU are fused to realize the accurate positioning of UWB in complex environment. Finally, the experimental results show that the performance of the joint positioning proposed in this paper is obviously better than that of a single sensor compared with single UWB and single IMU positioning. It provides more solutions for accurate indoor positioning of multi-sensor fusion.

夏楠 , 邱天爽 , 李景春 , 等 . 一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法 [J ] . 电子学报 , 2013 , 41 ( 1 ): 148 - 152 . DOI: 10.3969/j.issn.0372-2112.2013.01.026 http://doi.org/10.3969/j.issn.0372-2112.2013.01.026 提出一种基于卡尔曼滤波与粒子滤波的非线性滤波算法.这种方法对于状态变量服从线性变化而观测方程为非线性的动态系统模型具有显著的效果.首先使用粒子滤波对状态变量进行初估计,然后对估计结果进行卡尔曼滤波,另外推导出该系统模型下状态变量估计误差的克拉美劳下界.通过计算复杂度分析及仿真实验验证,表明新方法与标准粒子滤波算法复杂度相当,但参数估计精度要高于标准粒子滤波以及扩展卡尔曼滤波算法,估计误差甚至要低于系统模型的克拉美劳下界.

XIA N , QIU T S , LI J C , et al. A nonlinear filtering algorithm combining the kalman filter and the particle filter [J ] . Acta Electronica Sinica , 2013 , 41 ( 1 ): 148 - 152 . (in Chinese) DOI: 10.3969/j.issn.0372-2112.2013.01.026 http://doi.org/10.3969/j.issn.0372-2112.2013.01.026 A nonlinear filtering algorithm is proposed based on the Kalman filter and the particle filter.The method can provide significant performance for dynamic nonlinear system which is consist of linear state equation and nonlinear measurement equation.Firstly,the particle filter is utilized for initial estimation of the state variables,and then the Kalman filter is performed.The Cramer-Rao Bound is derived for the nonlinear model.Computation complexity analysis and numerical simulations demonstrate that the proposed algorithm has the same complexity as the standard particle filter,but the estimation accuracy is higher than the standard particle filter and the extended Kalman filter.The estimation error is even lower than the Cramer-Rao Bound of the system model.

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苏佳 , 杨泽超 , 易卿武 , 等 . 基于遗传算法优化BP神经网络的GNSS干扰源定位技术 [J/OL ] . 无线电工程 , 2023 ( 2023-08-29 )[ 2023-12-06 ] . http://kns.cnki.net/kcms/detail/13.1097.TN.20230829.1520.006.html http://kns.cnki.net/kcms/detail/13.1097.TN.20230829.1520.006.html http://kns.cnki.net/kcms/detail/13.1097.TN.20230829.1520.006.html.

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TANG C K , WANG C , ZHANG L L , et al. Geometric-manifold-assisted distributed navigation probabilistic information fusion cooperative positioning algorithm [J ] . Remote Sensing , 2021 , 13 ( 24 ): 4987 . DOI: 10.3390/rs13244987 http://doi.org/10.3390/rs13244987 https://www.mdpi.com/2072-4292/13/24/4987 https://www.mdpi.com/2072-4292/13/24/4987 Positioning information is the cornerstone of a new generation of electronic information technology applications represented by the Internet of Things and smart city. However, due to various environmental electromagnetic interference, building shielding, and other factors, the positioning source can fail. Cooperative positioning technology can realize the sharing of positioning information and make up for the invalid positioning source. When one node in the cooperative positioning network has error, the positioning stability of all nodes in the whole cooperative network will be significantly reduced, but the positioning probability information technology can effectively reduce the impact of mutation error. Based on this idea, this paper proposes an information-geometry-assisted distributed algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information. The position information of different types of navigation sources is utilized to establish a probability density model, which effectively reduces the influence of a single position error on the whole cooperative position network. Combined with the nonlinear fitting characteristics of the information geometric manifold, mapping and fusion of the ranging information between cooperative nodes on the geometric manifold surface are conducted to achieve cooperative positioning, which can effectively improve the stability of the positioning results. The proposed algorithm is simulated and analyzed in terms of the node positioning error, ranging error, convergence speed, and distribution of the cooperative positioning network. The simulation results show that our proposed cooperative positioning algorithm can effectively improve the positioning stability and display better positioning performance.

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闵海根 , 李尧 , 汪建球 , 等 . 考虑通信时延的智能网联汽车协同定位方法 [J ] . 中国公路学报 , 2023 , 36 ( 6 ): 220 - 234 . DOI: 10.19721/j.cnki.1001-7372.2023.06.019 http://doi.org/10.19721/j.cnki.1001-7372.2023.06.019 为了满足网联环境下自动驾驶车辆安全行驶的需求，必须实现车辆全时空高精度定位。针对单车定位(Single Vehicle Localization, SVL)方法的不足，提出了一种基于双层滤波结构的智能网联汽车协同定位框架。首先，基于卡尔曼滤波对各车辆状态进行修正;然后设计基于联邦卡尔曼滤波的协同定位估计方法，通过构建一个主滤波器和多个局部滤波器，将本车状态与修正后的邻车状态进行融合；使用多种数据拟合方法，基于真实数据构建传输时延概率模型，基于高斯分布构建处理时延概率模型；此外，提出一种通信时延误差补偿方法，并融入协同定位框架;最后，设计了5组仿真试验，评估SVL、未进行通信时延误差补偿的协同定位方法(CLWC)和基于通信时延误差补偿的协同定位方法(CLC)的定位性能，并深入分析了速度和行驶方向对定位结果的影响。研究结果表明：在城市道路环境下，CLWC相较于SVL，精度提高了15%~23%;在空旷道路环境下，通信时延较小情况时，CLWC优于SVL，CLC在CLWC基础上将精度进一步提高了5%~13%。在长直道、弯道、隧道等场景，CLC能够保证定位轨迹平滑，精度明显高于SVL，同时进一步验证了存在通信时延情况下，车辆速度对协同定位的影响。所提方法不仅克服了SVL误差累积的缺陷，同时有效降低了通信时延的影响，可为车辆提供连续稳定的高精度位置。

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CHENG J , REN P , DENG T X . A novel ranging and IMU-based method for relative positioning of two-MAV formation in GNSS-denied environments [J ] . Sensors (Basel) , 2023 , 23 ( 9 ): 4366 . DOI: 10.3390/s23094366 http://doi.org/10.3390/s23094366 https://www.mdpi.com/1424-8220/23/9/4366 https://www.mdpi.com/1424-8220/23/9/4366 Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

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