Data-driven Distributed Consensus Filter for a Discrete-time Nonlinear Sensor Network
- Resource Type
- Conference
- Authors
- Bai, Chuandong; Ji, Honghai; Wei, Yuzhou; Pang, Zhonghua; Hou, Zhongsheng
- Source
- 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Data Driven Control and Learning Systems Conference (DDCLS), 2020 IEEE 9th. :965-970 Nov, 2020
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Heuristic algorithms
State estimation
System identification
Filtering algorithms
Robot sensing systems
Kalman filters
Filtering theory
Dynamic Linearization Technique
System Identification
State Estimation
Distributed Consensus Filter
Sensor Network
- Language
In this work, a novel data-driven distributed consensus filter (DD-DCF) is proposed based on the dynamic linearization technique (DLT) for a discrete-time nonlinear sensor network. Compared with conventional model-based consensus filters, the proposed method is data-driven merely depending on the input and output (I/O) data from measurements. Both the data-driven system identification (DD-SI) algorithm and the distributed consensus filter state estimation (DCF-SE) algorithm are investigated for a nonlinear sensor network. The theoretical analysis shows the main result of the DD-DCF algorithm in the sensor network. The simulation results verify the effectiveness of the designed approach.