scholarly journals Multi‐layered PCB distributed filter

2021 ◽  
Author(s):  
S. Bulja ◽  
D. Kozlov
Keyword(s):  



Author(s):  
Jingbo Wu ◽  
Li Li ◽  
Valery Ugrinovskii ◽  
Frank Allgower


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3678 ◽  
Author(s):  
Jinran Wang ◽  
Peng Dong ◽  
Zhongliang Jing ◽  
Jin Cheng

Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods.



2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Long Zhao ◽  
Qing Yun Wang

A development procedure for a low-cost attitude and heading reference system (AHRS) based on the distributed filter has been proposed. The AHRS consists of three single-axis accelerometers, three single-axis gyroscopes, and one 3-axis digital compass. The initial attitude estimation is readily accomplished by using the complementary filtering. The attitude estimation for UAV flying in the real time is realized by using the three low orders EKF. The validation results show that the estimated orientations of the developed AHRS are within the acceptable region, and AHRS can give a stabilized attitude and heading information for a long time.



Author(s):  
Verónica Bolón-Canedo ◽  
Noelia Sánchez-Maroño ◽  
Joana Cerviño-Rabuñal


Author(s):  
Lingling Wu ◽  
Derui Ding ◽  
Yamei Ju ◽  
Xiaojian Yi

This paper investigates the distributed recursive filtering issue of a class of stochastic parameter systems with randomly occurring faults. An event-triggered scheme with an adaptive threshold is designed to better reduce the communication load by considering dynamic changes of measurement sequences. In the framework of Kalman filtering, a distributed filter is constructed to simultaneously estimate both system states and faults. Then, the upper bound of filtering error covariance is derived with the help of stochastic analysis combined with basis matrix inequalities. The obtained condition with a recursive feature is dependent on the statistical characteristic of stochastic parameter matrices as well as the time-varying threshold. Furthermore, the desired filter gain is derived by minimizing the trace of the obtained upper bound. Finally, two simulation examples are conducted to demonstrate the effectiveness and feasibility of the proposed filtering method.



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