23,726 research outputs found
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Distributed Kalman Filter
The continuing trend toward connected sensors (“internet of things” and” ubiquitous computing”) drives a demand for powerful distributed estimation methodologies. In tracking applications, the distributed Kalman filter (DKF) provides an optimal solution under Kalman filter conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm, which receives all associated measurements. However, the centralized approach requires full communication of all measurements at each time step, whereas the DKF works at arbitrary communication rates since the calculation is fully distributed. A more recent methodology is based on ”accumulated state density” (ASD), which augments the states from multiple time instants to overcome spatial cross-correlations. This chapter explains the challenges in distributed tracking. Then, possible solutions are derived, which include the DKF and ASD approach
Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging
The implementation challenges of cooperative localization by dual
foot-mounted inertial sensors and inter-agent ranging are discussed and work on
the subject is reviewed. System architecture and sensor fusion are identified
as key challenges. A partially decentralized system architecture based on
step-wise inertial navigation and step-wise dead reckoning is presented. This
architecture is argued to reduce the computational cost and required
communication bandwidth by around two orders of magnitude while only giving
negligible information loss in comparison with a naive centralized
implementation. This makes a joint global state estimation feasible for up to a
platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion
for the considered setup, based on state space transformation and
marginalization, is presented. The transformation and marginalization are used
to give the necessary flexibility for presented sampling based updates for the
inter-agent ranging and ranging free fusion of the two feet of an individual
agent. Finally, characteristics of the suggested implementation are
demonstrated with simulations and a real-time system implementation.Comment: 14 page
On Communication-Efficient Multisensor Track Association via Measurement Transformation (Extended Version)
Multisensor track-to-track fusion for target tracking involves two primary
operations: track association and estimation fusion. For estimation fusion,
lossless measurement transformation of sensor measurements has been proposed
for single target tracking. In this paper, we investigate track association
which is a fundamental and important problem for multitarget tracking. First,
since the optimal track association problem is a multi-dimensional assignment
(MDA) problem, we demonstrate that MDA-based data association (with and without
prior track information) using linear transformations of track measurements is
lossless, and is equivalent to that using raw track measurements. Second,
recent superior scalability and performance of belief propagation (BP)
algorithms enable new real-time applications of multitarget tracking with
resource-limited devices. Thus, we present a BP-based multisensor track
association method with transformed measurements and show that it is equivalent
to that with raw measurements. Third, considering communication constraints, it
is more beneficial for local sensors to send in compressed data. Two analytical
lossless transformations for track association are provided, and it is shown
that their communication requirements from each sensor to the fusion center are
less than those of fusion with raw track measurements. Numerical examples for
tracking an unknown number of targets verify that track association with
transformed track measurements has the same performance as that with raw
measurements and requires fewer communication bandwidths
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