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Keywords = federal Kalman filter

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19 pages, 55351 KiB  
Article
Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion
by Chen Meng, Haoyang Yang, Cuicui Jiang, Qinglei Hu and Dongyu Li
Remote Sens. 2025, 17(13), 2176; https://doi.org/10.3390/rs17132176 - 25 Jun 2025
Viewed by 395
Abstract
Accurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these vulnerabilities, this paper presents an [...] Read more.
Accurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these vulnerabilities, this paper presents an integrated framework combining sensor anomaly detection with a Dynamic Adaptive Extended Kalman Filter (DAEKF) and federated filtering algorithms to bolster navigation resilience and accuracy for UAV remote sensing. Specifically, mathematical models for prevalent UAV sensor attacks were established. The proposed framework employs adaptive thresholding and residual consistency checks for the real-time identification and isolation of anomalous sensor measurements. Based on these detection outcomes, the DAEKF dynamically adjusts its sensor fusion strategies and noise covariance matrices. To further enhance the fault tolerance, a federated filtering architecture was implemented, utilizing adaptively weighted sub-filters based on assessed trustworthiness to effectively isolate faults. The efficacy of this framework was validated through rigorous experiments that involved real-world flight data and software-defined radio (SDR)-based Global Positioning System (GPS) spoofing, alongside simulated attacks. The results demonstrate exceptional performance, where the average anomaly detection accuracy exceeded 99% and the precision surpassed 98%. Notably, when benchmarked against traditional methods, the proposed system reduced navigation errors by a factor of approximately 2-3 under attack scenarios, which substantially enhanced the operational stability of the UAVs in challenging environments. Full article
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36 pages, 2990 KiB  
Review
Advances in Multi-Source Navigation Data Fusion Processing Methods
by Xiaping Ma, Peimin Zhou and Xiaoxing He
Mathematics 2025, 13(9), 1485; https://doi.org/10.3390/math13091485 - 30 Apr 2025
Cited by 1 | Viewed by 736
Abstract
In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering [...] Read more.
In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering (KF), Particle Filtering (PF), and Federated Filtering (FF), have been continuously optimized, enabling effective handling of non-linear and non-Gaussian noise issues. On the other hand, the introduction of AI technologies like deep learning and reinforcement learning has provided new solutions for multi-source data fusion, particularly enhancing adaptive capabilities in complex and dynamic environments. Additionally, methods based on Factor Graph Optimization (FGO) have also demonstrated advantages in multi-source data fusion, offering better handling of global consistency problems. In the future, with the widespread adoption of technologies such as 5G, the Internet of Things, and edge computing, multi-source navigation data fusion is expected to evolve towards real-time processing, intelligence, and distributed systems. So far, fusion methods mainly include optimal estimation methods, filtering methods, uncertain reasoning methods, Multiple Model Estimation (MME), AI, and so on. To analyze the performance of these methods and provide a reliable theoretical reference and basis for the design and development of a multi-source data fusion system, this paper summarizes the characteristics of these fusion methods and their corresponding application scenarios. These results can provide references for theoretical research, system development, and application in the fields of autonomous driving, unmanned vehicle navigation, and intelligent navigation. Full article
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17 pages, 2863 KiB  
Article
Adaptive Distributed Student’s T Extended Kalman Filter Employing Allan Variance for UWB Localization
by Yanli Gao, Maosheng Yang, Xin Zang, Lei Deng, Manman Li, Yuan Xu and Mingxu Sun
Sensors 2025, 25(6), 1883; https://doi.org/10.3390/s25061883 - 18 Mar 2025
Cited by 1 | Viewed by 515
Abstract
This study proposes an adaptive distributed Student’s t extended Kalman filter (EKF) using Allan variance for ultrawide-band (UWB) localization. First of all, we model the state equation using the target’s position and velocity in east and north directions and the measurement equation by [...] Read more.
This study proposes an adaptive distributed Student’s t extended Kalman filter (EKF) using Allan variance for ultrawide-band (UWB) localization. First of all, we model the state equation using the target’s position and velocity in east and north directions and the measurement equation by using distance between the UWB base station (BS) and the target object. Then, the adaptive distributed filter employs a federation structure: A local t EKF is designed to estimate the target’s position by fusing the distance between the UWB base station and the target object. The main filter fuses the local filter’s outputs and computes the final output. For the local t EKF, in order to overcome the problem that noise in the Kalman method is assumed to be white noise and difficult to adapt to practical application environments, the t distribution is used to model noise. Meanwhile, Allan variance is calculated to assist the local filter, which improves the adaptive ability. Experimental results show that the proposed method effectively enhances navigation accuracy compared to the distributed EKF. Full article
(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)
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28 pages, 11251 KiB  
Article
In-Motion Initial Alignment Method Based on Multi-Source Information Fusion for Special Vehicles
by Zhenjun Chang, Zhili Zhang, Zhaofa Zhou, Xinyu Li, Shiwen Hao and Huadong Sun
Entropy 2025, 27(3), 237; https://doi.org/10.3390/e27030237 - 25 Feb 2025
Viewed by 690
Abstract
To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information [...] Read more.
To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information fusion. First, a federal Kalman filter-based multi-sensor fusion architecture is established to effectively integrate odometer, laser Doppler velocimeter, and SINS data, resolving the challenge of autonomous navigation parameter calculation under GNSS-denied conditions. Second, a dual-mode fault diagnosis and isolation mechanism is developed to enable rapid identification of sensor failures and system reconfiguration. Finally, an environmentally adaptive dynamic alignment strategy is proposed, which intelligently selects optimal alignment modes by real-time evaluation of motion characteristics and environmental disturbances, significantly enhancing system adaptability in complex operational scenarios. The experimental results show that the method proposed in this paper can effectively improve the accuracy of vehicle-mounted alignment in motion, achieve accurate identification, effective isolation, and reconstruction of random incidental faults, and improve the adaptability and robustness of the system. This research provides an innovative solution for the rapid deployment of special-purpose vehicles in GNSS-denied environments, while its fault-tolerant mechanisms and adaptive strategies offer critical insights for engineering applications of next-generation intelligent navigation systems. Full article
(This article belongs to the Section Multidisciplinary Applications)
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27 pages, 8840 KiB  
Article
Adaptive Federated Kalman Filtering with Dimensional Isolation for Unmanned Aerial Vehicle Navigation in Degraded Industrial Environments
by Quanxi Zhan, Runjie Shen, Yedong Mao, Yihang Shu, Lu Shen, Linchuan Yang, Junrui Zhang, Chenyang Sun, Fenghe Guo and Yan Lu
Drones 2025, 9(3), 168; https://doi.org/10.3390/drones9030168 - 24 Feb 2025
Cited by 1 | Viewed by 1253
Abstract
Unmanned aerial vehicle (UAV) navigation systems face significant challenges in complex environments, such as sensor degradation and signal loss. This study proposes the NSDDI-AFF (Normalized Single-Dimensional Degradation Isolation with Adaptive Federated Filtering) method to address these issues. By integrating adaptive thresholding, degradation isolation, [...] Read more.
Unmanned aerial vehicle (UAV) navigation systems face significant challenges in complex environments, such as sensor degradation and signal loss. This study proposes the NSDDI-AFF (Normalized Single-Dimensional Degradation Isolation with Adaptive Federated Filtering) method to address these issues. By integrating adaptive thresholding, degradation isolation, and multi-sensor fusion, the method dynamically identifies degraded channels and ensures robust state estimation. Evaluated in a semi-enclosed coal yard and in a hydropower pipeline, NSDDI-AFF reduced positional errors by 97.5% and 95.7% compared to LIO-SAM and Fast-LIO2, achieving a position RMSE of 0.05 m and orientation RMSE of 0.5°. The method detected degradation early (120 s) and maintained mapping accuracy with geometric errors below 0.5%. These results demonstrate that NSDDI-AFF significantly enhances UAV positioning accuracy, fault tolerance, and mapping reliability, making it a robust solution for challenging industrial applications. Full article
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25 pages, 6785 KiB  
Article
Intelligent QLFEKF Integrated Navigation for the SSBE Cruise Phase Based on X-Ray Pulsar/Solar and Target Planetary Doppler Information Fusion
by Wenjian Tao, Jinxiu Zhang, Jianing Song, Qin Lin, Zebin Chen, Hui Wang, Jikun Yang and Jihe Wang
Remote Sens. 2024, 16(23), 4465; https://doi.org/10.3390/rs16234465 - 28 Nov 2024
Cited by 3 | Viewed by 982
Abstract
The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in [...] Read more.
The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in orbit, and a significant communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. Traditional navigation schemes have been unable to provide high-accuracy autonomous intelligent navigation for the probe independent of the ground. Therefore, high-accuracy intelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE probe. The probe of the SSBE is disturbed by multiple sources of solar light pressure and a complex, unknown environment during its long cruise operation while in orbit. In order to ensure the high-accuracy position state and velocity state error estimation for the probe in the cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar/solar and target planetary Doppler velocity measurements is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of state and observation noise error covariance parameters. The federated extended Kalman filter (FEKF) based on the Q-learning (QLFEKF) navigation algorithm is proposed to achieve high-accuracy state estimations of the autonomous intelligence navigation system for the SSBE probe cruise phase. The main advantage of the QLFEKF is that Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time and obtain high position and velocity state estimation (PVSE) accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSE navigation accuracy of the federated filter integrated based the Q-learning navigation algorithm is improved by 55.84% and 37.04%, respectively, demonstrating the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher navigation accuracy and is able to satisfy the demands of autonomous high accuracy for the SSBE cruise phase. Full article
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16 pages, 939 KiB  
Article
State-of-Charge and State-of-Health Estimation in Li-Ion Batteries Using Cascade Electrochemical Model-Based Sliding-Mode Observers
by Yong Feng, Chen Xue, Fengling Han, Zhenwei Cao and Rebecca Jing Yang
Batteries 2024, 10(8), 290; https://doi.org/10.3390/batteries10080290 - 15 Aug 2024
Cited by 4 | Viewed by 1613
Abstract
This paper proposes a cascade approach based on a sliding mode observer (SMO) for estimating the state of charge (SoC) and state of health (SoH) of lithium-ion (Li-ion) batteries using a single particle model (SPM). After convergence, the observation error signal of the [...] Read more.
This paper proposes a cascade approach based on a sliding mode observer (SMO) for estimating the state of charge (SoC) and state of health (SoH) of lithium-ion (Li-ion) batteries using a single particle model (SPM). After convergence, the observation error signal of the current node in the cascade observer is generated from the output injection signal of the previous node’s observer. The current node’s observer generates its output injection signal, leading to its convergence. This sequential process accurately determines the observed values of each node using only the battery’s current and voltage. Subsequently, the SoC and SoH are estimated using observations of lithium-ion concentrations on the surface and inside the battery anode. The accuracy of this approach is validated using Dynamic Stress Test (DST) and Federal Urban Driving Scheme (FUDS) experimental data. A comparative analysis with conventional SMO and Extended Kalman Filter (EKF) algorithms demonstrates the approach’s effectiveness and superior performance, confirming its practical applicability. Full article
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19 pages, 5136 KiB  
Article
Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
by Xuetao Wang, Yijun Gao, Dawei Lu, Yanbo Li, Kai Du and Weiyu Liu
Appl. Sci. 2024, 14(13), 5868; https://doi.org/10.3390/app14135868 - 4 Jul 2024
Cited by 7 | Viewed by 2625
Abstract
With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used [...] Read more.
With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. Full article
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26 pages, 3589 KiB  
Article
Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle
by Zhixin Chen, Gang Li, Zhihua Zhang and Ruolan Fan
World Electr. Veh. J. 2024, 15(6), 249; https://doi.org/10.3390/wevj15060249 - 7 Jun 2024
Cited by 1 | Viewed by 1526
Abstract
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal [...] Read more.
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation. Full article
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23 pages, 8365 KiB  
Article
Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture
by Sorin Andrei Negru, Patrick Geragersian, Ivan Petrunin and Weisi Guo
Sensors 2024, 24(3), 981; https://doi.org/10.3390/s24030981 - 2 Feb 2024
Cited by 9 | Viewed by 4251
Abstract
Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations [...] Read more.
Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario. Full article
(This article belongs to the Special Issue New Methods and Applications for UAVs)
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15 pages, 4288 KiB  
Proceeding Paper
Research on INS/GNSS/UWB Adaptive Robust ESKF Kinematic and Static Filtering Based on Cost Function
by Zongbin Ren, Songlin Liu, Jing Liu, Jun Dai and Yunzhu Lv
Eng. Proc. 2024, 60(1), 8; https://doi.org/10.3390/engproc2024060008 - 9 Jan 2024
Cited by 1 | Viewed by 1064
Abstract
Multi-source autonomous navigation-dependable decision making has a crucial impact on the overall performance of navigation systems. To solve the problem of overall system robustness caused by the intelligent-dependable decision making difficulties of navigation systems from different sources, on an unmanned ground vehicle (UGV) [...] Read more.
Multi-source autonomous navigation-dependable decision making has a crucial impact on the overall performance of navigation systems. To solve the problem of overall system robustness caused by the intelligent-dependable decision making difficulties of navigation systems from different sources, on an unmanned ground vehicle (UGV) as the carrier, a new multi-source fusion algorithm based on cost function is proposed in this paper. The algorithm uses INS/GNSS/UWB as the sensor data source and is solved by using error-state Kalman filter (ESKF)-based kinematic and static multi-source filtering. After the RSS, positioning residual and positioning stability are selected as parameters and weighted, and the cost function is constructed. The structure of the filtering can be adapted according to the cost function in complex environments. Through mathematical simulation and comparative experiments, the positioning accuracy of the algorithm is improved by 75.9% and 74.44%, respectively, compared to federated filter and traditional ESKF-based kinematic and static filtering. It also improves the reliability, decision-making ability, and robustness of multi-source autonomous navigation system. Full article
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27 pages, 7516 KiB  
Article
Integrating GRU with a Kalman Filter to Enhance Visual Inertial Odometry Performance in Complex Environments
by Tarafder Elmi Tabassum, Zhengjia Xu, Ivan Petrunin and Zeeshan A. Rana
Aerospace 2023, 10(11), 923; https://doi.org/10.3390/aerospace10110923 - 29 Oct 2023
Cited by 6 | Viewed by 3404
Abstract
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions [...] Read more.
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors. Full article
(This article belongs to the Special Issue UAV Path Planning and Navigation)
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19 pages, 4062 KiB  
Article
Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure
by Bo Yang, Feng Liu, Liang Xue and Bin Shan
Entropy 2023, 25(10), 1412; https://doi.org/10.3390/e25101412 - 3 Oct 2023
Cited by 6 | Viewed by 1557
Abstract
To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the [...] Read more.
To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system. Full article
(This article belongs to the Section Signal and Data Analysis)
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18 pages, 4898 KiB  
Article
Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV
by Tao Chen and Qi Qi
Sensors 2023, 23(18), 7865; https://doi.org/10.3390/s23187865 - 13 Sep 2023
Cited by 3 | Viewed by 1694
Abstract
This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the [...] Read more.
This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle’s (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking. Full article
(This article belongs to the Section Navigation and Positioning)
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20 pages, 7637 KiB  
Article
State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter
by Ruolan Fan, Gang Li and Yanan Wu
Sustainability 2023, 15(18), 13446; https://doi.org/10.3390/su151813446 - 7 Sep 2023
Cited by 8 | Viewed by 1832
Abstract
As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for [...] Read more.
As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for vehicle active safety control. Acquiring accurate and real-time parameters such as vehicle speed and side slip angle is a prerequisite for vehicle active safety control. Therefore, relying on the National Natural Science Foundation of China, this paper takes the distributed drive electric vehicle in the form of four-wheel independent drive and steering as the research object. Taking the measurement data of low-cost vehicle sensors as input and adaptive Kalman filtering as theoretical support, the sub-filter of federal Kalman filtering adds a fuzzy controller on the basis of volumetric Kalman filtering, and designs the vehicle driving state estimation algorithm to realize the accurate estimation of driving state information. Finally, the typical experimental conditions are selected, and the designed algorithm is verified by the co-simulation of MATLAB/Simulink and CarSim. At the same time, the algorithm is further verified based on the driving simulator hardware-in-the-loop experimental platform. The results show that the designed estimation algorithm has good effects in terms of accuracy, stability, and real-time performance. Full article
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