Average information-weighted consensus filter for target tracking in distributed sensor networks with naivety issues

2018 ◽  
Vol 32 (5) ◽  
pp. 681-699 ◽  
Author(s):  
Peng Yao ◽  
Gang Liu ◽  
Yanfei Liu
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Consensus Filter ◽  
Average Information

scholarly journals Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3678 ◽  
Author(s):  
Jinran Wang ◽  
Peng Dong ◽  
Zhongliang Jing ◽  
Jin Cheng
Keyword(s):  
Sensor Networks ◽  
The State ◽  
Measurement Noise ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Distributed State Estimation ◽  
Consensus Filter ◽  
Simulation Results ◽  
Augmented State ◽  
Distributed Filter

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.

Target Tracking in Sensor Networks Using Additive Divided Difference Information Filtering Method

2013 ◽  
Vol 433-435 ◽  
pp. 503-509
Author(s):  
Deok Jin Lee ◽  
Kil To Chong ◽  
Dong Pyo Hong
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Information Fusion ◽  
Divided Difference ◽  
Information Filtering ◽  
Nonlinear Estimation ◽  
Fusion Algorithm ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Information Filter

This paper represents a new multiple sensor information fusion algorithm in distributed sensor networks using an additive divided difference information filter for nonlinear estimation and tracking applications. The newly proposed multi-sensor fusion algorithm is derived by utilizing an efficient new additive divided difference filtering algorithm with embedding statistical error propagation method into an information filtering architecture. The new additive divided difference information filter achieves not only the accurate nonlinear estimation solution, but also the flexibility of multiple information fusion in distributed sensor networks. Performance comparison of the proposed filter with the nonlinear information filters is demonstrated through a target-tracking application.

scholarly journals Distributed Filter with Consensus Strategies for Sensor Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xie Li ◽  
Huang Caimou ◽  
Hu Haoji
Keyword(s):  
Sensor Networks ◽  
Research Problem ◽  
Consensus Algorithm ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Consensus Filter ◽  
Information Consensus ◽  
Distributed Filter ◽  
Important Research Problem ◽  
Better Than

Consensus algorithm for networked dynamic systems is an important research problem for data fusion in sensor networks. In this paper, the distributed filter with consensus strategies known as Kalman consensus filter and information consensus filter is investigated for state estimation of distributed sensor networks. Firstly, an in-depth comparison analysis between Kalman consensus filter and information consensus filter is given, and the result shows that the information consensus filter performs better than the Kalman consensus filter. Secondly, a novel optimization process to update the consensus weights is proposed based on the information consensus filter. Finally, some numerical simulations are given, and the experiment results show that the proposed method achieves better performance than the existing consensus filter strategies.

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