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Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers.
IEEE Transactions on Cybernetics
|July 17, 2018
Summary
This study introduces a robust filter for distributed sensor networks, effectively handling measurement outliers. The new method improves state estimation accuracy by modeling sensor data using a Student-t process, outperforming traditional filters.
Area of Science:
- Signal Processing
- Distributed Systems
- Statistical Inference
Background:
- Traditional consensus-based filters in distributed sensor networks are susceptible to divergence when encountering outliers.
- Robust state estimation is crucial for reliable operation of sensor networks in the presence of noisy or erroneous measurements.
Purpose of the Study:
- To propose a robust consensus nonlinear information filter for distributed state estimation that effectively handles measurement outliers.
- To develop a method that overcomes the limitations of Gaussian assumptions in traditional filters when dealing with non-ideal data.
Main Methods:
- Modeling sensor node measurements as a multivariate Student-t process with unknown parameters.
- Employing variational Bayesian inference for joint estimation of state and parameters.
- Utilizing fixed-point iteration to solve the coupled state and parameter update equations.
- Developing a centralized robust information filter and extending it to a distributed architecture.
Main Results:
- The proposed filter demonstrates effectiveness in mitigating the impact of outliers on state estimation.
- The consensus mechanism integrates both likelihoods and prior distributions for robust information fusion.
- Convergence analysis confirms the stability and reliability of the proposed robust consensus nonlinear information filter.
- Simulation results validate the superior performance compared to traditional consensus filters in outlier scenarios.
Conclusions:
- The developed robust consensus nonlinear information filter provides a significant advancement for distributed state estimation in sensor networks with outliers.
- The Student-t process modeling and variational Bayesian approach offer a powerful framework for robust statistical inference.
- The distributed implementation enables scalable and effective information fusion across interconnected sensor nodes.
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