Robust CPHD Fusion for Distributed Multitarget Tracking Using
Asynchronous Sensors
Abstract
This paper concentrates on tracking multiple targets using an
asynchronous network of sensors with different sampling rates. First, a
timely fusion approach is proposed for handling measurements from
asynchronous sensors. In the proposed approach, the arithmetic average
fusion of the estimates provided by local cardinalized probability
hypothesis density filters is recursively carried out according to the
network-wide sampling time sequence. The corresponding intersensor
communication is conducted by a partial flooding protocol, in which
either cardinality distributions or intensity functions pertinent to
local posteriors are disseminated among sensors. Moreover, both feedback
and non-feedback fusion-filtering modes are provided to meet the
performance and real-time requirements, respectively. Second, an
extension of the timely fusion approach referred to as robust bootstrap
approach is presented, which can deal with unknown clutter and detection
parameters by exploiting a local bootstrap filtering scheme. Finally,
numerical simulations are performed to test the proposed approaches.