Nonlinear estimation with a network of heterogenous algorithms

Centralized state-estimation algorithms, such as the original Kalman filter, are no longer feasible in large scale sensor networks, due to practical limitations on communication bandwidth and spatial distribution of resources. To cope with these limitations, various distributed estimation algorithms have been proposed that estimate the state of a process in each sensor node using local measurements. State fusion of this local estimate with the estimates obtained in neighboring nodes ensures that the difference between local estimates is reduced. A common perspective in distributed state-estimation is that each individual node performs the same algorithm locally. This paper investigates whether it is beneficial to have some nodes that can perform a different, more accurate estimation method, i.e., heterogenous. To that extent, a networked system where each node employs the same local state-estimator is compared to a similar system where different nodes can perform different types of local estimation algorithms. Their performances are assessed on a Van-der-Pol oscillator and on a benchmark application to estimate speed profiles in traffic shockwaves. The results of these examples encourage further investigation of heterogeneous, distributed state-estimation.