Aerospace and Electronic Systems Magazine September 2017 - 5

performance and optimality are provided. In [15], decentralized
filtering for redundancy management in multisensor navigation
systems is discussed. The filtering architecture presented consists
of a bank of parallel filters. Each filter processes the output of a
common IMU and an aiding sensor that is unique and dedicated
to that particular filter. The state vector for each parallel filter contains a common set of states consisting of the vehicle's position,
velocity, and attitude. In addition, it contains error states unique to
the particular aiding sensor assigned to that parallel filter. Because
each parallel filter uses the same IMU, the states of each parallel
filter are correlated, but the details for handling this correlation are
not discussed in depth. The decentralized architecture from [15] is
considered in [16]. In [16], however, the existence of correlations
between parallel filter states (because they are all driven by the
same IMU process noise) is acknowledged. It bypasses the need to
maintain knowledge of this correlation between the banks of filters
by computing and using an upper bound on the process noise matrix. A decentralized filter for rapidly detecting and isolating global
positioning system (GPS) sensor faults and signal in space faults is
presented in [18]. The system in [18] is structured in such a way as
to avoid correlations between parallel filters. In [19], an approach
that uses a multiple hypothesis filter (inspired by [20]) to fuse the
outputs of a bank of decentralized filters is presented.
In this article, a decentralized filtering that fuses the output of a
bank of parallel and potentially correlated filters is presented. Furthermore, unlike [15] and [16], the state of each parallel filter does
not necessarily contain all of the vehicle's kinematic states. Thus,
it is more flexible than [15] or [16]. A detailed derivation of the
filter equations is given in Appendix A of this article. In addition,
the performance of the filter is validated using sensor data from a
flight test of a small UAV. The ideas that are the basis for the work
reported in this article were initially presented in [21] and [22].

chitecture, the sensor measurements are sent to a data bus. The
navigation filter takes the data it needs from the data bus (denoted
as y if it is used as a measurement or u if it is an input [23]) to
generate an estimate of the navigation state vector (denoted xˆ ) and
its estimation error covariance (denoted P). In this architecture, a
failure that affects y or u can potentially affect or corrupt the entire
state vector estimate.
By contrast, Figure 2 illustrates a generic depiction of the decentralized filter architecture described in this article. In this architecture, a bank of parallel filters selects the information they need

Figure 1.

Centralized filter architecture.

FILTER ARCHITECTURE
Small UAV FCSs use estimates of the vehicle's navigation state
vector for guidance and control functions. The state vector is estimated by a navigation filter. The state vector to be estimated, x,
is composed of the attitude vector xa (as a triad of Euler angles,
quaternion, or direction cosine matrix), the position vector xp, and
the velocity vector xv.
Figure 1 shows the generic centralized filter architecture commonly found in many off-the-shelf small UAV FCSs. In this arSEPTEMBER 2017

Figure 2.

Decentralized (parallel) filter architecture.

IEEE A&E SYSTEMS MAGAZINE

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