  This paper studies the event-triggered distributed fusion estimation problems
for a class of nonlinear networked multisensor fusion systems without noise
statistical characteristics. When considering the limited resource problems of
two kinds of communication channels (i.e., sensor-to-remote estimator channel
and smart sensor-to-fusion center channel), an event-triggered strategy and a
dimensionality reduction strategy are introduced in a unified networked
framework to lighten the communication burden. Then, two kinds of compensation
strategies in terms of a unified model are designed to restructure the
untransmitted information, and the local/fusion estimators are proposed based
on the compensation information. Furthermore, the linearization errors caused
by the Taylor expansion are modeled by the state-dependent matrices with
uncertain parameters when establishing estimation error systems, and then
different robust recursive optimization problems are constructed to determine
the estimator gains and the fusion criteria. Meanwhile, the stability
conditions are also proposed such that the square errors of the designed
nonlinear estimators are bounded. Finally, a vehicle localization system is
employed to demonstrate the effectiveness and advantages of the proposed
methods.
