Showing papers by "Yuan Gao published in 2022"

Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

Abstract: For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross‐covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time‐varying and steady‐state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real‐time applications. A simulation applied to the two‐mass spring damper mechanical system shows their effectiveness.

Sequential Fast Covariance Intersection Fusion Kalman Filter for Networked System with Fading Measurements and Correlated Noises

Abstract: For the networked systems with fading measurements, multiplicative noises and correlated noises, the model transformation technique with the fictitious noises and Sequential Fast Covariance Intersection (SFCI) fusion Kalman filter are presented. These algorithms can compensate the mixed random uncertainties with the fictitious noises and decrease the large computational burden. Furthermore, the SFCI fusion algorithm overcomes the drawback of the Sequential Covariance Intersection fusion, whose fusion result is sensitive to the fusion orders, and improves the accuracy and efficiency. The accuracy of the presented SFCI fusion Kalman filter is higher than that of each local estimator, less but close to that of the information fusion Kalman filter weighted by matrices. Finally, the effectiveness of the SFCI fusion Kalman algorithm is verified by the simulation example.

Sequential Fast Covariance Intersection Fusion Kalman Filter For Multi-Sensor Networked Systems with Correlated Noises and Random Uncertainties

Abstract: For the multi-sensor systems with correlated noises and random uncertainties, a new augmented state approach with fictitious noises and a Sequential Fast Covariance Intersection (SFCI) fusion Kalman filter are presented. These algorithms can compensate the mixed random uncertainties with the fictitious noises, which include the random one-step measurements and missing measurements, and decrease the large computational burden. Furthermore, the SFCI fusion algorithm overcomes the drawback of the Sequential Covariance Intersection (SCI) fusion, whose fusion result is sensitive to the fusion orders, and improves the accuracy and efficiency. The accuracy of the presented SFCI fusion Kalman filter is higher than that of each local estimator, less but close to that of the information fusion Kalman estimator weighted by matrices. Finally, the effectiveness of the SFCI fusion Kalman algorithm is verified by the simulation example.

lncRNA 1700101O22Rik and NONMMUG030480.1 Are Not Essential for Spermatogenesis in Mice

Abstract: Many testis-specific lncRNAs are highly expressed in late spermatogenesis, especially in spermiogenesis. However, their functions and the underlying mechanisms in male fertility are largely unknown. Here, we screened two highly expressed lncRNAs, 1700101O22Rik (O22Rik) and NONMMUG030480.1 (NM480) in testes, to investigate the roles in spermatogenesis using lncRNA knockout (KO) mouse generated by CRISPER/Cas9 technology. Both testis-specific lncRNAs were mainly expressed from secondary spermatocytes to round spermatids, suggesting that they might be involved in spermiogenesis. Phenotypic analysis showed that the deletion of O22Rik or NM480 did not affect the development of testis and epididymis or spermatogenesis. These results were confirmed in both young and middle-aged male mice. In addition, there was no significant difference in sperm morphology and other parameters including concentration and motility between wild type (WT) and KO mice. Fertility tests showed that litter size was significantly lower in O22Rik KO mice compared with WT controls. Although O22Rik did not exert dramatic roles in spermatogenesis, on molecular levels, its surrounding gene expression was disturbed significantly. Gm32773 was decreased; however, Gm32828 was increased in KO mice. In conclusion, lncRNA O22Rik and NM480 are not individually essential for spermatogenesis in mice.

Robust time-varying Kalman predictor for descriptor system with random one-step measurement delay and packet dropout

Abstract: This paper investigates a Kalman predictor with robustness is applied to descriptor systems with uncertainty of noises variances, random packet loss and measurement delay within one-step is addressed. First of all, taking singular value decomposition (SVD) method to reduce the original descriptor system, then the information which has been lost due to the poor network is compensated by enlarging the states and leading in fictitious noises, and then the system with a singular matrix is transformed into a standard system. With the use of robust minimax estimation principle, a robust predictor under the case of poor communication network is proposed. Under the condition that noise variances is unknown, the actual filtering error variance could be guaranteed within a minimal scope, and take Lyapunov equation to prove the robustness. To verify the conclusion presented in this paper, a simulation example has been shown at the end.

Event-triggered sequential fusion filters for multi-sensor systems with multiplicative noises

Abstract: This paper combines an event-triggered mechanism with a multiplicative noise system to reduce the communication congestion and the calculation workload of the networked fusion center, and proposes an event-triggered Sequence Covariance Intersection and Sequence Inverse Covariance Intersection fusion algorithm based on the above system. It is mentioned that the event-triggered fusion algorithms are robust with respect to the unknown cross-covariances, and have higher fault tolerance and stability than the previous fusers. Validity of the proposed algorithm is demonstrated by studying arithmetic examples.