Improving robustness of distributed filtering for sensor networks using FIR filtering

Robustness is required from an estimator to provide better performance if a wireless sensor network (WSN) operates under harsh conditions with incomplete information about noise. This paper shows that robustness of the WSN can be improved by using the distributed unbiased finite impulse response (UFIR) filter rather than the traditional distributed Kalman filter (KF), both based on the average consensus. Unlike the KF, the UFIR filter completely ignores the noise statistics and initial values which are typically not well known. As an example, we consider a vehicle travelling along a circular trajectory under unpredictable impacts and errors in the noise statistics. A case of impulsive noise generated by manufacturing process is also considered.