December 2024
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2 Reads
Mathematics and Computers in Simulation
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December 2024
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2 Reads
Mathematics and Computers in Simulation
November 2024
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6 Reads
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1 Citation
International Journal of Adaptive Control and Signal Processing
The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent Kalman filtering solutions. This paper proposes a novel method of H∞ filtering‐based on adaptive random weighted estimation to address this issue. It combines the H∞ filter with random weighted concept to estimate system noise statistics. Random weighting theories are established based on the state estimate and state error covariance of the H∞ filter to estimate both process noise statistics and measurement noise statistics. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. Simulation and experimental results show that the proposed method can effectively estimate system noise statistics, leading to improved accuracy for system state estimation.
November 2024
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7 Reads
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1 Citation
Aerospace Science and Technology
October 2024
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11 Reads
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5 Citations
IEEE Transactions on Aerospace and Electronic Systems
Cubature Kalman filter (CKF) is widely used for nonlinear dynamic estimations due to its high accuracy and numerical stability. However, CKF experiences a substantial degradation in performance when confronted with measurement uncertainty. This paper proposes an indirect fuzzy robust CKF based on a multi-input multi-output fuzzy inference system (FIS) to address this issue. Firstly, the input parameters for FIS are normalized to avoid the inconsistency between the domains of pre-defined fuzzy set and actual inputs, ensuring that the measurement uncertainty can be veraciously communicated to FIS. Subsequently, triangular input/output membership functions are designed such that the fuzzy inference can be carried out to generate FIS outputs. Then a scaling diagonal matrix, which is determined in an indirect manner via an artfully constructed transfer function based on FIS outputs, is introduced to CKF for filtering correction. Since the proposed method enable to track the actual change of measurements with rapidity and further facilitate the convergence of state estimation, it not only improves the CKF robustness against measurement uncertainty, but also overcomes the limitations of the existing fuzzy logic based robust filters. Monte Carlo simulations on the ballistic target reentry model have demonstrated that the proposed method exhibits excellent performance.
June 2024
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7 Reads
Sensors
The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation.
June 2024
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1 Read
Journal of the Mechanical Behavior of Biomedical Materials
February 2024
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20 Reads
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1 Citation
February 2024
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22 Reads
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4 Citations
Sensors
In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints are incorporated in the importance sampling process of the traditional cubature particle filter to enhance the accuracy of the importance density function. Subsequently, the Euclidean distance is employed to optimize the resampling process by adjusting particle weights to avoid particle degradation. Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number N → ∞. Our experimental results and the results of a comparative analysis regarding GNSS/DR (Global Navigation Satellite System/Dead Reckoning)-integrated vehicle navigation demonstrate that the proposed CCPF can effectively estimate system state under constrained conditions, leading to higher estimation accuracy than the traditional particle filter and cubature particle filter.
January 2024
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33 Reads
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9 Citations
IEEE Transactions on Instrumentation and Measurement
Factor graph optimization (FGO) provides a new means for asynchronous data fusion of integrated underwater vehicle navigation in a plug-and-play unified framework. However, in complex underwater environments, FGO suffers from observation anomalies, leading to deteriorated navigation solutions. This paper proposes an improved factor graph with anomaly detection using Mahalanobis distance to overcome the above issue for INS/DVL/USBL (inertial navigation system/Doppler velocity log/ultra-short base line) integrated underwater vehicle navigation. This method constructs a new factor graph model embedded with the node of anomaly detection for INS/DVL/USBL integration. Since the standard FGO computational load is increased with the number of the factor nodes, a sliding window technique is established to restrict the factor node number to improve the FGO computational efficiency. Based on above, a scheme of anomaly detection and regulation is presented for handling the disturbance of observation anomaly on system state estimation via the concept of Mahalanobis distance. Results of simulation and ground test experimentation show that the proposed methodology not only has the real-time performance, but also has a strong robustness against observation anomaly for INS/DVL/USBL integrated navigation of underwater vehicles.
November 2023
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20 Reads
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6 Citations
Applied Mathematical Modelling
... This paper aims to enhance the positioning accuracy of each USV by calculating and fusing the position information of different USVs. Consequently, only the location state of USVs will be analyzed [37]. The state vector of the system can be defined as ...
November 2024
Aerospace Science and Technology
... Hu et al. presented a novel direct filtering approach to INS/GNSS (Inertial Navigation System / Global Navigation Satellite System) integration [15]. Cubature-Kalman filter is widely used for nonlinear dynamic estimations due to its high accuracy and numerical stability [16][17][18][19][20]. Adaptive Unscented Kalman Filter (AUKF) is an effective system error estimation method that can suppress the interference of inaccurate system noise statistics on the filtering results. ...
October 2024
IEEE Transactions on Aerospace and Electronic Systems
... Among the available game engines, Unity stands out as one of the most widely used, finding applications not only in game development but also in various simulation domains. For instance, Xu et al. [37] utilized Unity and the PhysX engine to construct a model to simulate the backdraft phenomena model, while Mohan et al. [38] implemented VR-based surgical simulations using Unity, effectively applying deformable object algorithms to simulate human organs and animal models. These examples demonstrate the versatility of game engines, highlighting their potential for facilitating the implementation of deformable object algorithms across a wide range of fields. ...
February 2024
... Guo et al. combined the estimation algorithm of unscented Kalman filter with the estimation algorithm of lightweight convolutional neural network, and proposed a confidence based fusion strategy of vision and vehicle dynamics, which achieved the estimation of road adhesion coefficient [4]. Compared with EKF, the UKF can be applied to nonlinear distributed systems, achieving higher computational accuracy [5][6][7][8][9][10]. However, prior knowledge of system noise statistics significantly affects the performance of UKF. ...
January 2024
IEEE Transactions on Instrumentation and Measurement
... Building on the strengths of both INS and SRS, the autonomous INS/SRS integrated navigation system has been developed [25][26][27][28][29]. Despite its innovative approach, the INS/SRS system encounters significant challenges: it does not fully utilize spectral redshift and direction vector information, leading to reliance on integrated velocity data for position measurements. ...
August 2023
Science China Technological Sciences
... Guo et al. combined the estimation algorithm of unscented Kalman filter with the estimation algorithm of lightweight convolutional neural network, and proposed a confidence based fusion strategy of vision and vehicle dynamics, which achieved the estimation of road adhesion coefficient [4]. Compared with EKF, the UKF can be applied to nonlinear distributed systems, achieving higher computational accuracy [5][6][7][8][9][10]. However, prior knowledge of system noise statistics significantly affects the performance of UKF. ...
January 2023
IEEE Transactions on Instrumentation and Measurement
... (18). The measurement innovation is defined as the error between the actual value of the measurement variable and its predicted value [37][38][39]: ...
January 2023
IEEE Transactions on Instrumentation and Measurement
... Online soft tissue parameter identification is created using the extended Kalman filter (EKF). It based on the nonlinear Hunt-Crossley (H -C) contact model being dynamically linearized with respect to the system state [17,18]. A technique for realistic modelling of human soft tissue, incorporating dynamic soft tissue characterisation was introduced by Song et al. [19]. ...
May 2023
International Journal of Robust and Nonlinear Control
... To improve stability in complex systems, adapted UKF and higher-order filters, such as the Cubature Kalman Filter (CKF), are considered promising solutions (Arasaratnam et al., 2010). Due to these advantages, CKF has been widely used in high non-linearity systems, such as unnamed aerial vehicle navigation Gao et al. (2021), battery charge state estimation (Li et al., 2022), and global navigation satellite systems (Gao et al., 2023a). ...
December 2022
Chinese Journal of Aeronautics
... The integration of haptic feedback into teleoperated robotic surgical systems poses a notable difficulty since system communication time delays induce a trade-off between transparency and stability. By integrating an environment estimation and force prediction methodology into an experimental robotic minimally invasive surgical system, these time delays are reduced [6]. ...
December 2022
Sensors