甄子洋 . 舰载无人机自主着舰回收制导与控制研究进展 [J ] . 自动化学报 , 2019 , 45 ( 4 ): 669 - 681 .

ZHEN Z Y . Research development in autonomous carrier-landing/ship-recovery guidance and control of unmanned aerial vehicles [J ] . Acta Automatica Sinica , 2019 , 45 ( 4 ): 669 - 681 . (in Chinese)

GONZÁLEZ-GARCÍAJ , SWENSON R L , GÓMEZESPINOSA A . Real-time kinematics applied at unmanned aerial vehicles positioning for orthophotography in precision agriculture [J ] . Computers and Electronics in Agriculture , 2020 , 177 : 105695 . DOI: 10.1016/j.compag.2020.105695 http://doi.org/10.1016/j.compag.2020.105695 https://linkinghub.elsevier.com/retrieve/pii/S0168169920312011 https://linkinghub.elsevier.com/retrieve/pii/S0168169920312011

CHEN L Z T , LIU Z C , FANG J C . A novel hybrid observation prediction methodology for bridging GNSS outages in INS/GNSS systems [J ] . The Journal of Navigation , 2022 , 75 : 1206 - 1225 . DOI: 10.1017/S037346332200025X http://doi.org/10.1017/S037346332200025X https://www.cambridge.org/core/product/identifier/S037346332200025X/type/journal_article https://www.cambridge.org/core/product/identifier/S037346332200025X/type/journal_article The integration of the inertial navigation system (INS) and global navigation satellite system (GNSS) is suited for localisation and navigation applications, such as aircrafts, land vehicles and ships. The primary challenge is for navigation system to achieve accurate and reliable navigation solution during GNSS outages. This paper presents an observation prediction methodology for INS/GNSS bridging GNSS outages, which combines partial least squares regression (PLSR) and Gaussian process regression (GPR) to model the INS/GNSS observations and enable a Kalman filter to estimate INS errors. The performance of proposed PLSR/GPR prediction methodology was validated through four GNSS outages taken on flight experiment data, including diverse manoeuvre conditions. The experiment results demonstrate that remarkable performance enhancements are achieved through applying the proposed PLSR/GPR prediction methodology into INS/GNSS integration.

SHANG X Y , SUN F P , LIU B D , et al. GNSS spoofing mitigation with a multi-correlator estimator in the tightly coupled INS/GNSS integration [J ] . IEEE Transactions on Instrumentation and Measurment , 2023 , 72 : 8500112 .

PINTO NETO J B , GOMES L C , ORTIZ F M , et al. An accurate cooperative positioning system for vehicular safety applications [J ] . Computers & Electrical Engineering , 2020 , 83 : 106591 .

范博洋 , 赵高鹏 , 薄煜明 , 等 . 多目标空地异构无人系统协同任务分配方法 [J ] . 兵工学报 , 2023 , 44 ( 6 ): 1564 - 1575 . DOI: 10.12382/bgxb.2022.0095 http://doi.org/10.12382/bgxb.2022.0095 针对由地面无人车与多无人机组成的空地异构无人系统面向大范围、多目标的协同任务分配问题,以无人系统完成任务时间为优化目标,同时考虑无人机收放、续航能力以及任务时序等约束条件,建立空地异构无人系统的任务分配模型,提出一种多目标空地异构无人系统任务分配方法。结合密度值最大聚类和混合粒子群优化算法,对空地异构无人系统的任务分配问题进行求解,从而得到满足约束条件的全局任务分配结果;通过仿真实验对所提方法进行验证。实验结果表明,该方法能够有效地求解在不同作战环境中的空地异构无人系统的任务分配问题。

FAN B Y , ZHAO G P , BO Y M , et al. Collaborative task allocation method for multi-target air-ground heterogeneous unmanned system [J ] . Acta Armamentarii , 2023 , 44 ( 6 ): 1564 - 1575 . (in Chinese) DOI: 10.12382/bgxb.2022.0095 http://doi.org/10.12382/bgxb.2022.0095 To address the collaborative task allocation problem of air-ground heterogeneous unmanned systems composed of ground unmanned vehicles (UGV) and unmanned aerial vehicles (UAVs) facing large ranges and multiple targets, the task allocation model of air-ground heterogeneous unmanned systems is established with the completion time of the unmanned systems as the optimization goal and the constraints of the UAV launch and recovery, endurance and task sequence taken in account. A task allocation method for multi-target air-ground heterogeneous unmanned systems is proposed, which combines density peak clustering and hybrid particle swarm optimization algorithm (hybrid-PSO) to solve the task allocation problem of air-ground heterogeneous unmanned systems, so as to obtain the global task allocation results that satisfy the constraints. The proposed method is verified by simulation experiments, and the results show that the method can effectively solve the task allocation problem of air-ground heterogeneous unmanned systems in different operational environments.

王孟阳 , 张栋 , 唐硕 , 等 . 基于动态联盟策略的无人机集群在线任务规划方法 [J ] . 兵工学报 , 2023 , 44 ( 8 ): 2207 - 2223 . DOI: 10.12382/bgxb.2022.0247 http://doi.org/10.12382/bgxb.2022.0247 针对复杂战场环境下无人集群任务规划所面临的高动态性、强不确定性以及多约束问题,提出一种基于动态联盟策略的分布式在线任务规划方法。描述无人机集群动态任务规划的典型场景,建立了异构无人机集群的多约束分布式任务规划数学模型;设计考虑集群动态拓扑约束的任务联盟组建策略,提出耦合Dubins航迹规划的改进蚁群算法,实现多约束强不确定动态任务规划问题的在线求解;构建异构无人机集群察打评一体的任务仿真场景,通过数字仿真以及虚实结合的半实物仿真技术验证所提出的策略和算法的有效性。研究结果表明:所提方法在动态任务规划过程中能够在损失较少任务完成时间的前提下可获得较优的系统效能,对于后续研究工作进一步走向工程化应用具有一定意义。

WANG M Y , ZHANG D , TANG S , et al. UAV swarm on-line mission planning method based on dynamic allocation strategy [J ] . Acta Armamentarii , 2023 , 44 ( 8 ): 2207 - 2223 . (in Chinese) DOI: 10.12382/bgxb.2022.0247 http://doi.org/10.12382/bgxb.2022.0247 A distributed online mission planning method based on a dynamic alliance strategy is proposed to deal withthe complex problems of high dynamics, strong uncertainty, and multiple constraints of UAV swarm mission planning in a complex battlefield environment. Firstly, the typical scenarios of UAV swarm dynamic mission planning are described, and the mathematical model of multi-constraint distributed mission planning ofthe heterogeneous UAV swarm is established. Secondly, a task alliance formation strategy considering the dynamic topological constraints of the UAV swarm is designed, and an improved ant colony algorithm coupled with Dubins path planning is proposed to realize the online solution of dynamic mission planning with multiple constraints and strong uncertainties. Finally, typical task simulation scenarios of theheterogeneous UAV swarm is constructed, and the effectiveness of the proposed strategy and algorithm is verified by digital simulations and virtual-real semi-physical simulations.The results show that the proposed method can achieve better system performance with less loss of mission completion time in the dynamic mission planning process, which is of some significance for further research work towards engineering applications.

ALAJAMI A A , MORENO G , POUS R . Design of a UAV for autonomous RFID-based dynamic inventories using stigmergy for mapless indoor environments [J ] . Drones , 2022 , 6 ( 8 ): 208 . DOI: 10.3390/drones6080208 http://doi.org/10.3390/drones6080208 https://www.mdpi.com/2504-446X/6/8/208 https://www.mdpi.com/2504-446X/6/8/208 Unmanned aerial vehicles (UAVs) and radio frequency identification (RFID) technology are becoming very popular in the era of Industry 4.0, especially for retail, logistics, and warehouse management. However, the autonomous navigation for UAVs in indoor map-less environments while performing an inventory mission is, to this day, an open issue for researchers. This article examines the method of leveraging RFID technology with UAVs for the problem of the design of a fully autonomous UAV used for inventory in indoor spaces. This work also proposes a solution for increasing the performance of the autonomous exploration of inventory zones using a UAV in unexplored warehouse spaces. The main idea is to design an indoor UAV equipped with an onboard autonomous navigation system called RFID-based stigmergic and obstacle avoidance navigation system (RFID-SOAN). RFID-SOAN is composed of a computationally low cost obstacle avoidance (OA) algorithm and a stigmergy-based path planning and navigation algorithm. It uses the same RFID tags that retailers add to their products in a warehouse for navigation purposes by using them as digital pheromones or environmental clues. Using RFID-SOAN, the UAV computes its new path and direction of movement based on an RFID density-oriented attraction function, which estimates the optimal path through sensing the density of previously unread RFID tags in various directions relative to the pose of the UAV. We present the results of the tests of the proposed RFID-SOAN system in various scenarios. In these scenarios, we replicate different typical warehouse layouts with different tag densities, and we illustrate the performance of the RFID-SOAN by comparing it with a dead reckoning navigation technique while taking inventory. We prove by the experiments results that the proposed UAV manages to adequately estimate the amount of time it needs to read up-to 99.33% of the RFID tags on its path while exploring and navigating toward new zones of high populations of tags. We also illustrate how the UAV manages to cover only the areas where RFID tags exist, not the whole map, making it very efficient, compared to the traditional map/way-points-based navigation.

CAO Y , ZHANG X F , HUANG X J , et al. Research on key technologies of multi-rotor UAV high-precision positioning and navigation system [J ] . Journal of Physics: Conference Series , 2020 , 1650 ( 3 ): 032085 . DOI: 10.1088/1742-6596/1650/3/032085 http://doi.org/10.1088/1742-6596/1650/3/032085 For the continuous high-precision navigation and positioning needs of rotorcraft UAVs based on industry applications, based on low-cost GNSS single-frequency positioning modules and MEMS inertial navigation, the design high-precision GNSS / INS integrated navigation module. At the same time using computer vision algorithms. Aiming at the requirement of high precision, the original data acquisition scheme that fully guarantees the accuracy of MEMS inertial navigation is designed. No one against the rotor for the high-frequency vibration problem of the mobile platform, the effect of IMU white noise parameters on the performance of MEMS inertial navigation and integrated navigation was analysed. The test results show that the IMU white noise parameters can effectively improve the combined navigation performance under high-frequency vibration conditions; the positioning accuracy of the module can reach centimetre level and be continuous and stable.

GUO J W , ZHOU Y L , ZHAO S , et al. A new GNSS outlier mitigation method for GNSS/INS integrated system [J ] . Measurement Science and Technology , 2023 , 34 ( 10 ): 105118 . DOI: 10.1088/1361-6501/ace19b http://doi.org/10.1088/1361-6501/ace19b High-precision positioning with global navigation satellite systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective fault detection and exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines autoregressive integrated moving average (ARIMA) model and multilayer perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle’s location for several future steps. Then, a dissimilarity-based method of dynamic time warping measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.

WU J J , JIANG J G , ZHANG C , et al. A Novel optimal robust adaptive scheme for accurate GNSS RTK/INS tightly coupled integration in urban environments [J ] . Remote Sensing , 2023 , 15 ( 15 ): 3725 . DOI: 10.3390/rs15153725 http://doi.org/10.3390/rs15153725 https://www.mdpi.com/2072-4292/15/15/3725 https://www.mdpi.com/2072-4292/15/15/3725 Modern navigation systems are inseparable from an integrated solution consisting of a global navigation satellite system (GNSS) and an inertial navigation system (INS) since they serve as an important cornerstone of national comprehensive positioning, navigation, and timing (PNT) technology and can provide position, velocity, and attitude information at higher accuracy and better reliability. A robust adaptive method utilizes the observation information of both systems to optimize the filtering system, overcoming the shortcomings of the Kalman filter (KF) in complex urban environments. We propose a novel robust adaptive scheme based on a multi-condition decision model suitable for tightly coupled real-time kinematic (RTK)/INS architecture, which can reasonably determine whether the filtering system performs robust estimation (TCRKF) or adaptive filtering (TCAKF), improving the robust estimation method of two factors considering ambiguity variance for RTK-related observations. The performance of the proposed robust adaptive algorithm was evaluated through two sets of real vehicle tests. Compared with the TCAKF and TCRKF algorithms, the new robust adaptive scheme improves the average three-dimensional (3D) position root mean square (RMS) by 31% and 18.88%, respectively. It provides better accuracy and reliability for position, velocity, and attitude simultaneously.

ZHOU Y , CHENG N , LU N , et al. Multi-UAV-aided networks: Aerial-ground cooperative vehicular networking architecture [J ] . IEEE Vehicular Technology Magazine , 2015 , 10 ( 4 ): 36 - 44 . DOI: 10.1109/MVT.2015.2481560 http://doi.org/10.1109/MVT.2015.2481560 http://ieeexplore.ieee.org/document/7317860/ http://ieeexplore.ieee.org/document/7317860/

郑磊 , 陈志敏 , 贾宇轩 . 基于广域部署智能反射面的无人机集群跟踪方法 [J ] . 兵工学报 , 2023 , 44 ( 6 ): 1837 - 1845 . DOI: 10.12382/bgxb.2022.0217 http://doi.org/10.12382/bgxb.2022.0217 无人机在民用和军事领域中都发挥着重要作用,其体积小、数量多、速度快,更会给国防安全带来严重的安全威胁,有效跟踪定位无人机是保障低空安全的关键问题之一。针对典型城市环境中的多目标跟踪问题,提出一种高效费比的多目标跟踪算法。通过广域部署低成本智能反射面,对多目标进行数据融合;同时提出一种改进的数据关联算法,通过特征辅助的模糊数据关联,利用一部分历史数据作为筛选最优观测数据的特征阈值,得到最接近真实值的量测数据。采用卡尔曼滤波进行状态估计,实现对多目标的低成本高精度跟踪。仿真对比新算法与传统概率密度数据关联算法性能。仿真结果表明:新算法相比传统算法在位置和速度方面均方根误差更小,跟踪精度约为1.7m,传统算法约为6.6m,实验结果表明新算法能够有效提高目标关联精度和跟踪性能。

ZHENG L , CHEN Z M , JIA Y X . UAV swarm tracking method based on wide-area deployment of intelligent reflecting surfaces [J ] . Acta Armamentarii , 2023 , 44 ( 6 ): 1837 - 1845 . (in Chinese) DOI: 10.12382/bgxb.2022.0217 http://doi.org/10.12382/bgxb.2022.0217 Unmanned Aerial Vehicles (UAVs) play an important role in both civil and military domains. However, their small size, large quantity, and high speed pose significant security threats to national defense. Ensuring low-altitude safety requires effective tracking and locating of UAVs. A cost-effective target tracking method is thus proposed for tracking multiple targets. By deploying low-cost intelligent reflectors across a wide area, data fusion of multiple targets is performed. An improved data association method is proposed. Through feature-assisted fuzzy data association, a part of historical data is used as the feature threshold to screen the optimal observation data, and the measured data that is closest to the real value is obtained. Finally, Kalman filter is used for state estimation to realize the tracking of multiple targets with low cost and high precision. The performance of the proposed method is compared with that of the traditional probability density data association algorithm. The results show that the proposed algorithm achieves smaller root mean square error in position and speed, with a tracking accuracy of around 1.7m, while the traditional algorithm is about 6.6m. Experimental results verify that the proposed method can effectively improve the target association accuracy and tracking performance.

SIVANERI V O , GROSS J N . UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments [J ] . Aerospace Science and Technology , 2017 , 71 : 245 - 255 . DOI: 10.1016/j.ast.2017.09.024 http://doi.org/10.1016/j.ast.2017.09.024 https://linkinghub.elsevier.com/retrieve/pii/S1270963817307472 https://linkinghub.elsevier.com/retrieve/pii/S1270963817307472

XIONG J , XIONG Z , CHEONG J W , et al. Cooperative positioning for low-cost close formation flight based on relative estimation and belief propagation [J ] . Aerospace Science and Technology , 2020 , 106 : 106068 . DOI: 10.1016/j.ast.2020.106068 http://doi.org/10.1016/j.ast.2020.106068 https://linkinghub.elsevier.com/retrieve/pii/S1270963820307501 https://linkinghub.elsevier.com/retrieve/pii/S1270963820307501

SAVIC V , ZAZO S . Cooperative localization in mobile networks using nonparametric variants of belief propagation [J ] . Ad Hoc Networks , 2013 , 11 ( 1 ): 138 - 150 . DOI: 10.1016/j.adhoc.2012.04.012 http://doi.org/10.1016/j.adhoc.2012.04.012 https://linkinghub.elsevier.com/retrieve/pii/S1570870512000844 https://linkinghub.elsevier.com/retrieve/pii/S1570870512000844

MEYER F , HLINKA O , HLAWATSCH F , et al. Sigma point belief propagation [J ] . IEEE Signal Processing Letters , 2014 , 21 ( 2 ): 145 - 149 . DOI: 10.1109/LSP.2013.2290192 http://doi.org/10.1109/LSP.2013.2290192 http://ieeexplore.ieee.org/document/6661389/ http://ieeexplore.ieee.org/document/6661389/

LYU X , HU B Q , WANG Z , et al. A SINS/GNSS/VDM integrated navigation fault-tolerant mechanism based on adaptive information sharing factor [J ] . IEEE Transactions on Instrumentation and Measurement , 2022 , 71 : 1 - 13 .

李松 , 刘哲 , 唐小妹 , 等 . 基于固定点迭代的Huber鲁棒容积卡尔曼滤波算法 [J ] . 计算机工程与应用 , 2021 , 57 ( 16 ): 90 - 96 . DOI: 10.3778/j.issn.1002-8331.2007-0105 http://doi.org/10.3778/j.issn.1002-8331.2007-0105 对于非线性系统而言，容积卡尔曼滤波（Cubature Kalman Filter，CKF）算法是处理状态估计问题的一种有效方法，并且其在高斯噪声下可以获得良好的估计性能。然而，当噪声被重尾噪声污染时，其性能通常会急剧下降。为解决此问题，将Huber方法应用于CKF框架中，取代了传统的最小均方误差（Minimum Mean Square Error，MMSE）准则，以提高算法的鲁棒性。在所提算法中，通过将量测方程线性化构造了线性回归模型，并采用固定点迭代的方法求解基于Huber方法的最小化问题。因此，推导了基于固定点迭代的Huber鲁棒CKF（FP-IHCKF）算法，在该算法中先验信息和量测信息通过Huber方法进行了重构。通过对再入目标跟踪问题进行仿真，验证了所提算法的有效性和鲁棒性。

LI S , LIU Z , TANG X M , et al. Fixed-point interred Huber-based robust cubature Kalman filter [J ] . Computer Engineering and Applicationgs , 2021 , 57 ( 16 ): 90 - 96 . (in Chinese)

宁静 , 陈俊 , 吴麒 . 一种基于期望模型的自适应Singer模型滤波算法 [J ] . 电讯技术 , 2022 , 62 ( 10 ): 1464 - 1469 .

NING J , CHEN J , WU Q . An adaptive filtering algorithm for Singer model based on expectation model [J ] . Telecommunication Engineering , 2022 , 62 ( 10 ): 1464 - 1469 . (in Chinese)

郝顺义 , 卢航 , 魏翔 , 等 . 简化高阶强跟踪容积卡尔曼滤波及其在组合导航中的应用 [J ] . 控制与决策 , 2019 , 34 ( 10 ): 2105 - 2114 .

HAO S Y , LU H , WEI X , et al. Reduced high-degree strong tracking cubature Kalman filter and its application in integrated navigation system [J ] . Control and Decision , 2019 , 34 ( 10 ): 2105 - 2114 . (in Chinese)

XIONG H L , BIAN R C , LI Y J , et al. Fault-tolerant GNSS/SINS/DVL/CNS integrated navigation and positioning mechanism based on adaptive information sharing factors [J ] . IEEE Systems Journal , 2020 , 14 ( 3 ): 3744 - 3754 .

SU K , JIN S G . A novel GNSS single-frequency PPP approach to estimate the ionospheric TEC and satellite pseudorange observable-specific signal bias [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2021 , 60 : 5801712 .

LI B X , CHEN G W , SI Y B , et al. GNSS/INS integration based on machine learning lightgbm model for vehicle navigation [J ] . Applied Sciences , 2022 , 12 ( 11 ): 5565 . DOI: 10.3390/app12115565 http://doi.org/10.3390/app12115565 https://www.mdpi.com/2076-3417/12/11/5565 https://www.mdpi.com/2076-3417/12/11/5565 To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. First, a GNSS/INS integration methodology for land vehicle navigation based on position update architecture (PUA) using LightGBM regression for predicting the position of a vehicle during a GNSS outage is presented. It uses LightGBM to model the relationship between INS data and vehicle position changes. On-board INS and GNSS data are collected when the GNSS signal is available and are used to train the PUA-LightGBM model; in the event of a GNSS outage, INS data are used as the input to the PUA-LightGBM to predict the change in vehicle position. Second, a vehicle navigation data acquisition system was designed for model validation. This included a self-developed GNSS/INS integrated navigation system and a Novatel pwrpak7-e1 GNSS/INS integrated navigation system for data acquisition on six road segments. Finally, the collected data were used for machine learning training of the PUA-LightGBM model and the existing PUA-RandomForest model. As a result, the PUA-LightGBM predicts the vehicle position with less error in the event of a GNSS outage and takes less time to train. It was also demonstrated that by allowing the model to be dynamically trained or updated while the vehicle is moving the PUA-LightGBM could adapt perfectly to the predictions of vehicle position changes in different complex road segments.

LIU Y H , LUO Q S , ZHOU Y M . Deep learning-enabled fusion to bridge GPS outages for INS/GPS integrated navigation [J ] . IEEE Sensors Journal , 2022 , 22 ( 9 ): 8974 - 8985 . DOI: 10.1109/JSEN.2022.3155166 http://doi.org/10.1109/JSEN.2022.3155166 https://ieeexplore.ieee.org/document/9722837/ https://ieeexplore.ieee.org/document/9722837/

WU X J , SU Z , LI L , et al. Improved adaptive federated Kalman filtering for INS/GNSS/VNS integrated navigation algorithm [J ] . Applied Sciences , 2023 , 13 ( 9 ): 5790 . DOI: 10.3390/app13095790 http://doi.org/10.3390/app13095790 https://www.mdpi.com/2076-3417/13/9/5790 https://www.mdpi.com/2076-3417/13/9/5790 To address the issue of low positioning accuracy in unmanned vehicles navigating in obstructed spaces due to easily contaminated navigation measurement information, an improved adaptive federated Kalman filtering INS/GNSS/VNS integrated navigation algorithm is proposed. In this algorithm, an inertial navigation system (INS) serves as the common reference system, and, together with the global navigation satellite system (GNSS) and visual navigation system (VNS), they form the subsystems that together make up the main system. In the event of faulty measurement values in the subsystems, a combination of the residual chi-square and sliding-window averaging methods are used for fault detection to improve the fault tolerance of the integrated navigation algorithm. Additionally, an adaptive sharing factor is proposed to adjust the accuracy of the integrated navigation algorithm based on the accuracy of the sub-filters. Simulation experiments demonstrated that, compared with classic federated Kalman filtering, the proposed algorithm reduced the root mean square errors (RMSEs) of the three-dimensional position by 56.4%, 54.8%, and 43.4% and the root mean square errors of the three-dimensional velocity by 71.0%, 72.1%, and 28.4% in the event of sub-filter faults, effectively solving the problem of low positioning accuracy for unmanned vehicles in obstructed spaces while ensuring the real-time performance of the system.

万芯炜 , 王晶 , 杨辉 , 等 . BP神经网络结合粒子群优化卡尔曼滤波的MEMS陀螺随机误差补偿方法 [J ] . 兵工学报 , 2023 , 44 ( 2 ): 556 - 565 . DOI: 10.12382/bgxb.2022.0110 http://doi.org/10.12382/bgxb.2022.0110 针对微机电系统(MEMS)陀螺仪随机误差相对较大、影响其精度这一问题,提出一种基于BP神经网络结合具有量子行为的粒子群优化(QPSO)算法优化卡尔曼滤波(KF)的补偿方法。采集MEMS陀螺和转台数据作为样本,采用BP神经网络进行训练,建立误差模型;利用训练好的模型对MEMS陀螺进行误差补偿;利用QPSO算法优化KF,以达到更好的降噪效果。实验结果表明,该方法较BP神经网络优化KF、QPSO优化KF与变分模态分解结合小波阈值去噪等方法去噪处理后的平均绝对误差(MAE)和均方误差(MSE)更小,具有更好的降噪效果。

WAN X W , WANG J , YANG H , et al. A random error compensation method of MEMS gyroscope based on BP neural network combined with PSO-optimized Kalman filter [J ] . Acta Armamentarii , 2023 , 44 ( 2 ): 556 - 565 . (in Chinese) DOI: 10.12382/bgxb.2022.0110 http://doi.org/10.12382/bgxb.2022.0110 To deal with the large random error of the micro-electro-mechanical-system (MEMS) gyroscope that affects its accuracy, an error compensation method based on BP neural network combined with Quantum-behaved Particle Swarm Optimization (QPSO)-optimized Kalman Filter (KF) is proposed. First, the MEMS gyroscope and turntable data are collected as samples, and the BP neural network is employed for training to establish the error model; then the error of the MEMS gyroscope is compensated by the model; finally, the QPSO algorithm is used to optimize KF to achieve better noise reduction effect. The experimental results show that compared with other methods like BP-KF, QPSO-KF and VMD-WTD, this method has better denoising effect, and the MAE and MSE values of the denoised data are smaller.

XIA X , EHSAN H , XIONG L , et al. Autonomous vehicle kinematics and dynamics synthesis for sideslip angle estimation based on consensus Kalman filter [J ] . IEEE Transactions on Control Systems Technology , 2023 , 31 ( 1 ): 179 - 192 . DOI: 10.1109/TCST.2022.3174511 http://doi.org/10.1109/TCST.2022.3174511 https://ieeexplore.ieee.org/document/9782724/ https://ieeexplore.ieee.org/document/9782724/

WANG J , JIANG W P , LI Z , et al. A new multi-scale sliding window lstm framework (Mssw-lstm): a case study for gnss time-series prediction [J ] . Remote Sensing , 2021 , 13 ( 16 ): 3328 . DOI: 10.3390/rs13163328 http://doi.org/10.3390/rs13163328 https://www.mdpi.com/2072-4292/13/16/3328 https://www.mdpi.com/2072-4292/13/16/3328 GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep learning is a state-of-the-art approach for extracting high-level abstract features from big data without any prior knowledge. Moreover, long short-term memory (LSTM) networks are a form of recurrent neural networks that have significant potential for processing time series. In this study, a novel prediction framework was proposed by combining a multi-scale sliding window (MSSW) with LSTM. Specifically, MSSW was applied for data preprocessing to effectively extract the feature relationship at different scales and simultaneously mine the deep characteristics of the dataset. Then, multiple LSTM neural networks were used to predict and obtain the final result by weighting. To verify the performance of MSSW-LSTM, 1000 daily solutions of the XJSS station in the Up component were selected for prediction experiments. Compared with the traditional LSTM method, our results of three groups of controlled experiments showed that the RMSE value was reduced by 2.1%, 23.7%, and 20.1%, and MAE was decreased by 1.6%, 21.1%, and 22.2%, respectively. Our results showed that the MSSW-LSTM algorithm can achieve higher prediction accuracy and smaller error, and can be applied to GNSS time-series prediction.

BLAZQUEZ-GARCIAA , CONDEA , MORI U , et al. A review on outlier/anomaly detection in time series data [J ] . ACM Computing Surveys , 2021 , 54 ( 3 ): 1 - 33 .

LV J , GAO Z Z , XU Q Z , et al. Assessment of real-time GPS/BDS-2/BDS-3 single-frequency PPP and INS tight integration using different RTS products [J ] . Remote Sensing , 2022 , 14 ( 17 ): 4367 . DOI: 10.3390/rs14174367 http://doi.org/10.3390/rs14174367 https://www.mdpi.com/2072-4292/14/17/4367 https://www.mdpi.com/2072-4292/14/17/4367 Due to the virtues of low-cost and high positioning accuracy, Single-Frequency Precise Point Positioning (SF-PPP) is becoming a prospective technique. However, SF-PPP is not as widely used as dual-frequency and triple-frequency PPP at present, owing to the effect of ionospheric delay residuals after model rectification. In recent years, with the evolution of multi-constellation Global Navigation Satellite Systems (multi-GNSS, i.e., GPS, BDS-2, and BDS-3), it has become possible to obtain credible and continuous positioning results using SF-PPP. However, such performance would be significantly degraded in challenging environments (i.e., boulevards, tunnels, and tall buildings). Under these circumstances, GNSS signals are obstructed, and it is difficult to provide sufficient observations for SF-PPP. Therefore, the Inertial Navigation System (INS) is employed to promote the positioning performance of SF-PPP. The PPP/INS integration is regarded as one of the most efficient approaches in GNSS-denied environments. To satisfy the request of supplying real-time positioning information, the Real-Time Services (RTS) of the International GNSS Service (IGS) provide real-time precise orbit and clock products for globally distributed users through the internet. In this paper, a real-time GPS/BDS-2/BDS-3 SF-PPP and INS tight integration model is proposed, and it is assessed using the data gathered by vehicle and real-time products afforded by CAS (Chinese Academy of Sciences), GFZ (Deutsche GeoForschungsZentrum), and WHU (Wuhan University). The outcomes illustrate the following: (1) GPS + BDS SF-PPP/INS can provide more accurate and continuous positioning solutions compared with those of GPS + BDS SF-PPP, with improvements of 52.8%, 31.1%, and 42.8% in the north, east, and vertical components, respectively. (2) In general, the orbit and clock products’ accuracies in terms of GPS afforded by the three analysis centers are consistent with each other. For BDS, the orbit product from WHU is more accurate compared to those of CAS and GFZ. However, the accuracy of the clock product afforded by WHU is lower compared with those provided by the other two centers, especially for BDS-2 satellites. (3) The positioning accuracy in terms of Root Mean Square (RMS) values based on GFZ products are much higher than the results based on CAS and WHU products in the three directions.