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Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established...
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... federated filter is often seen as a two-stage filter structure, as is shown in Figure 2. In this paper, the reference system in Figure 2 is the SINS; the output of which is given to the main filter on the one hand, and is also given to the local filter as the measurement value on the other. ...
Context 2
... federated filter is often seen as a two-stage filter structure, as is shown in Figure 2. In this paper, the reference system in Figure 2 is the SINS; the output of which is given to the main filter on the one hand, and is also given to the local filter as the measurement value on the other. Then, the local state estimation and covariance are fed together with the main filter to obtain the global optimal estimation. ...
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Unmanned aerial vehicles (UAVs) have been developed for commercial use around the world. These aircraft have a wide range of capabilities such as structural surveying and medical supply delivery. However, UAVs experience an array of technical failures, e.g. fixed-position control surface failures or payload delivery failures, that may yield catastr...
Citations
... UAV attitude calculation can be divided into two algorithms: single-sensor and multi-sensor data fusion (Yang Y et al., 2020). The performance of single sensor solution is poor, and there are some shortcomings, such as gyroscope solution with error drift (Feng X et al., 2018), accelerometer solution with high-frequency noise, and magnetometer susceptible to magnetic field (Liu X et al., 2021). ...
When the UAV is in accelerated motion, the non-gravitational acceleration and body vibration lead to high-frequency errors in the accelerometer acquisition data, which affects the accuracy of the attitude calculation of the UAV. To solve the above problems, a UAV attitude solution fusion algorithm based on gradient descent-improved Extended Kalman Filter (EKF) was designed. Firstly, the gradient descent method is used to solve the sensor data to reduce the error caused by the accelerometer and magnetometer. Secondly, the noise covariance matrix and the non-gravity acceleration function are established to improve the shortcomings of the traditional noise matrix that cannot suppress the interference of non-gravity acceleration. Finally, the gradient descent solution value is used as the measured value of the improved EKF to improve the accuracy and filtering effect of the measured value and realize the attitude update. The experimental results show that in the static experiment, the error of the UAV roll angle is 0.017°. Compared with the gyroscope solving algorithm, EKF algorithm, and complementary filtering algorithm, the accuracy is improved by 0.116°, 0.023°, and 0.058°, respectively. In the dynamic experiment, the attitude error angle of the UAV is less than 0.1°. Compared with the EKF and complementary filtering algorithms, the accuracy is improved by an order of magnitude. The UAV attitude fusion algorithm based on gradient descent-improved EKF can effectively suppress the influencing factors such as non-gravitational acceleration interference and high-frequency vibration of the airframe, and improve the attitude-solving accuracy of the UAV.
... In this method, the system is constrained by the rotational speed information obtained by the geomagnetic sensor. GNSS and SINS operate independently, and the two groups of parameters obtained are fused by Extended Kalman Filter (EKF) [9] to achieve accurate acquisition of navigation parameters. 3. Large-scale differential tail-control improvised guided munitions principles ...
... In [25], an adaptive FKF method was designed to automatically update the information sharing factor. In [26], an improved federal extended Kalman filter (EKF) was applied to the near-ground short-range navigation of small unmanned aerial vehicle (UAV) to obtain better attitude information. In [27], the federal EKF algorithm was used to fuse navigation data in the UAV monitoring problem. ...
As a common integrated navigation system, the strapdown inertial navigation system (SINS)/global positioning system (GPS) can estimate velocity and position errors well. Many auxiliary attitude measurement systems can be used to improve the accuracy of attitude angle errors. In this paper, the in-flight alignment problem of the integrated SINS/GPS/Polarization/Geomagnetic navigation system is discussed. Firstly, the SINS/Geomagnetic subsystem is constructed to improve the estimation accuracy of horizontal attitude angles. Secondly, the polarization sensor is used to improve the estimation accuracy of heading angle. Then, a federal unscented Kalman filter (FUKF) with non-reset structure is applied to fuse the navigation data. Finally, simulation results for the integrated navigation system are provided based on experimental data. It can be shown that the proposed approach can improve not only the speed and position, but also the attitude error effectively.
Micro Position and Orientation System (POS) is the key motion compensation equipment of low-cost airborne remote sensing system, whose precision significantly constrains the imaging performance of the remote sensing. However, MPOS suffers from harsh environments such as electromagnetic interference and signal obstruction, which leads to measurement outliers and severely affects the motion measurement accuracy. To solve this problem, an adaptive fault-tolerant federated filter based on fuzzy logic is proposed. Firstly, the comprehensive performance evaluation factor is designed based on the fuzzy logic to accurately reflect the credibility of measurement information. Then, the adaptive fault-tolerant federated filter algorithm is developed based on the comprehensive performance evaluation factor to dynamically adjust the data fusion weights, allowing for the sequential isolation of outliers and the improved utilization of reliable measurements. The innovation of the proposed method lies in the integration of fuzzy logic into the federated filter. Compared to traditional methods, it significantly improves the utilization of measurement data while effectively isolating outliers. Finally, the experimental results indicate that the proposed method can enhance fault tolerance and have higher motion measurement accuracy compared to traditional methods.
div>This article addresses the essential task of understanding vibrations produced by vehicles to enhance the design of authentic laboratory tests. The article focuses on two primary sources of vibrations: those arising from vehicle–road surface interaction, which is largely random, and those emanating from the drivetrain, characterized as a summation of harmonics with a time-varying fundamental frequency. The method involves the application of the extended Kalman filter (EKF) paired with robust nonlinear least-squares (NLS) initialization to isolate the harmonic components effectively. Through a comprehensive analysis involving mean-square-error (MSE) evaluation via Monte Carlo simulation, considering additive white Gaussian noise (AWGN) and a two-degrees-of-freedom quarter-car model’s simulation response to the road, the research demonstrates the EKF’s proficiency. The results indicate the EKF’s capability to accommodate AWGN with a signal-to-noise ratio (SNR) up to 0 dB and road-induced random background vibrations up to an SNR of −3 dB, maintaining an MSE order of approximately 10−3.</div
In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.
Aiming at the problem of the low cooperative positioning accuracy and robustness of multi-UAV formation, a cooperative positioning method of a multi-UAV based on an adaptive fault-tolerant federated filter is proposed. Combined with the position of the follower UAV and leader UAV, and the relative range between them, a cooperative positioning model of the follower UAV is established. On this basis, an adaptive fault-tolerant federated filter is designed. Fault detection and isolation technology are added to improve the positioning accuracy of the follower UAV and the fault tolerance performance of the filter. Meanwhile, the measurement noise matrix is adjusted by the adaptive information allocation coefficient to reduce the impact of undetected fault information on the sub-filter and global estimation accuracy. The simulation results show that the adaptive fault-tolerant federated algorithm can greatly improve the positioning accuracy, which is 83.4% higher than that of the absolute positioning accuracy of a single UAV. In the case of a gradual fault, the method has a stronger fault-tolerant performance and reconstruction performance.
In recent years, unmanned aircraft systems (UASs) have played an increasingly significant role in the military and civil fields. The flight control system, as the "hub" of an unmanned aerial vehicle (UAV), is responsible for the key function of autonomous flight, while a reliable and stable navigation system provides important information such as position status for flight control and represents the "sensory" function of the UAV. A highly autonomous and credible UAV requires a navigation system that meets specific requirements for accuracy, integrity, and continuity, resulting in a multitude of sensors on-board the UAV that are heterogeneous, redundant, and multi-source, creating a highly complex navigation system. In this paper, we review multi-sensor fusion technology for small UAVs over the last 20 years and provide an overview of three typical multi-source fusion architectures based on filtering, factor graph optimization, and data-driven, focusing on inductive identification of key technologies for multi-source information fusion state estimation systems, including calibration techniques to improve data quality, observability analysis to provide theoretical support, additional model constraint correction using aircraft and resilient fusion management techniques across all sources. Finally, we propose future directions for UAS navigation systems to address the limitations of the existing systems.
The Inertial Navigation System/Global Positioning System (INS/GPS) fusion navigation technology commonly used by unmanned ground vehicles is prone to GPS interruption in complex environments, resulting in the inability of the system to locate accurately. This paper designs an INS/GPS/Odometer/Vision/Magnetometer fusion navigation system in response to this problem. Add odometer, vision, and magnetometer to the INS/GPS integrated navigation system, use odometer dead reckoning to suppress the position offset caused by GPS interruption, reduce the cumulative error of the system through visual positioning, and correct the course of INS with the help of magnetometer orientation Angle, improve the positioning ability of the system in the GPS interruption environment. In order to further enhance the positioning accuracy of the system, the Federal Filter algorithm (FKF) was improved, and the Federal Robust Cubature Kalman Filter algorithm (FRCKF) was proposed. A Robust Cubature Kalman Filter algorithm is introduced into the framework of the federated filter algorithm, which improves the processing ability of the FKF algorithm for nonlinear systems and reduces the positioning error of the system. Finally, a prototype of the vehicle navigation system was developed, and the effectiveness of the multi-source fusion navigation system was verified through sports car tests. The test results show that the multi-source fusion navigation system designed in this paper can effectively reduce the impact of GPS interruption on the INS/GPS integrated navigation system and improve the positioning accuracy of unmanned ground vehicles.
