Event-triggered optimal Kalman Consensus Filter with Upper Bound of Error Covariance

2021 ◽  
pp. 108175
Author(s):  
Zeming Li ◽  
Yonggui Liu ◽  
Xiaoqing Hu ◽  
Wenfeng Dai
Keyword(s):  
Upper Bound ◽  
Error Covariance ◽  
Consensus Filter ◽  
Event Triggered

Adaptive event-triggered distributed recursive filtering with stochastic parameters and faults

2021 ◽  
pp. 014233122110379
Author(s):  
Lingling Wu ◽  
Derui Ding ◽  
Yamei Ju ◽  
Xiaojian Yi
Keyword(s):  
Upper Bound ◽  
Basis Matrix ◽  
Dynamic Changes ◽  
Recursive Filtering ◽  
Error Covariance ◽  
Stochastic Parameter ◽  
Stochastic Parameters ◽  
System States ◽  
Distributed Filter ◽  
Event Triggered

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.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks

2017 ◽  
Vol 71 ◽  
pp. 103-111 ◽  
Author(s):  
Housheng Su ◽  
Zhenghao Li ◽  
Yanyan Ye
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Consensus Filter ◽  
Event Triggered

scholarly journals UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinghua Liu ◽  
Dandan Bai ◽  
Yunling Lv ◽  
Rui Jiang ◽  
Shuzhi Sam Ge
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Upper Bound ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Attack Model ◽  
Error Covariance

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

Event-triggered Kalman consensus filter over sensor networks

2016 ◽  
Vol 10 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Wenling Li ◽  
Junping Du ◽  
Yingmin Jia
Keyword(s):  
Sensor Networks ◽  
Consensus Filter ◽  
Event Triggered

scholarly journals Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

2021 ◽  
Vol 9 ◽  
Author(s):  
Xingzhen Bai ◽  
Xinlei Zheng ◽  
Leijiao Ge ◽  
Feiyu Qin ◽  
Yuanliang Li
Keyword(s):  
State Estimation ◽  
Data Transmission ◽  
Distribution System ◽  
Unscented Kalman Filter ◽  
Test System ◽  
The State ◽  
Distributed Generations ◽  
Error Covariance ◽  
Active Distribution System ◽  
Event Triggered

In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to the estimator. It can efficiently reduce the amount of data transmission while guaranteeing the performance of system state estimation. Second, an event-triggered unscented Kalman filter (ET-UKF) algorithm is proposed to conduct the state estimation of the ADS with mixed measurements. To this end, the unscented transform (UT) technique is employed to approximate the probability distribution of the state variable after nonlinear transformation, which can reach more than second order, and then, an upper bound of the filtering error covariance is derived and subsequently minimized at each iteration. The gain of the desired filter is obtained recursively by following a certain set of recursions. Finally, the effectiveness of the proposed method is demonstrated by using the IEEE-34 distribution test system.

scholarly journals Recursive State and Random Fault Estimation for Linear Discrete Systems under Dynamic Event-Based Mechanism and Missing Measurements

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xuegang Tian ◽  
Shaoying Wang
Keyword(s):  
Upper Bound ◽  
Estimation Error ◽  
Discrete Systems ◽  
Fault Estimation ◽  
Missing Measurements ◽  
Bernoulli Random Variables ◽  
Dynamic Event ◽  
Error Covariance ◽  
Event Based ◽  
Linear Discrete Systems

This paper is concerned with the event-based state and fault estimation problem for a class of linear discrete systems with randomly occurring faults (ROFs) and missing measurements. Different from the static event-based transmission mechanism (SETM) with a constant threshold, a dynamic event-based mechanism (DETM) is exploited here to regulate the threshold parameter, thus further reducing the amount of data transmission. Some mutually independent Bernoulli random variables are used to characterize the phenomena of ROFs and missing measurements. In order to simultaneously estimate the system state and the fault signals, the main attention of this paper is paid to the design of recursive filter; for example, for all DETM, ROFs, and missing measurements, an upper bound for the estimation error covariance is ensured and the relevant filter gain matrix is designed by minimizing the obtained upper bound. Moreover, the rigorous mathematical analysis is carried out for the exponential boundedness of the estimation error. It is clear that the developed algorithms are dependent on the threshold parameters and the upper bound together with the probabilities of missing measurements and ROFs. Finally, a numerical example is provided to indicate the effectiveness of the presented estimation schemes.

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