Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Abstract

In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavytailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering

“…The PDF of the Student’s t noise is [ 29 ]: where n is the degree of freedom, denotes the Gamma function.…”

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

“…In vehicular applications and others, the sensors' measurement noise in each CAVs, which is unknown and timevarying, can cause a bad estimation. The existing methods for handling the noise aim either at the use of advanced statistical models [22]- [24] or at a better calibration process as part of the manufacturing process. Both methods are not suitable for the sensor data fusion in vehicular scenarios.…”

Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range

“…Recently, the problem of distributed filtering has been studied for a class of uncertain systems with uncertain noise variances. 16–19…”

“…Recently, the problem of distributed filtering has been studied for a class of uncertain systems with uncertain noise variances. [16][17][18][19] In the majority of the existing distributed filters, however, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range (LSR).…”