3,714 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
Sensor Characterization and Signal Fusion for InstantEye
The practicality and effectiveness of using a TerraRanger Duo—a parallel sonar and infrared time-of-flight distance sensor—payload for obstacle detection is investigated for use with Physical Science Inc.’s InstantEye drone. A Python program was developed to interface with the serial data output before comparing the sensor’s empirical performance against its data sheet. The two signals from the distinct sensor modules, each with their characterized strengths and weaknesses, were then fused with a Kalman filter. This was further refined by imposing conditional weighting based on the known sensor characteristics. The filter output, with conditional corrections, was able to accurately track a single object’s position and velocity within a maximum range of 14 meters
Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems
For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is full column rank. They have the expensive computing burden due to the high-dimension extended measurement matrix. To reduce the computing burden, the weighted measurement fusion white noise estimators are presented. It is proved that weighted measurement fusion white noise estimators have the same accuracy as the centralized measurement fusion white noise estimators, so it has global optimality. It can be applied to signal processing in oil seismic exploration. A simulation example for Bernoulli-Gaussian white noise deconvolution filter verifies the effectiveness
Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.Comment: 5 pages, 1 figur
Target tracking based on a multi-sensor covariance intersection fusion Kalman filter
In a multi-sensor target tracking system, the
correlation of the sensors is unknown, and the
cross-covariance between the local sensors can not
be calculated. To solve the problem, the multisensor
covariance intersection fusion steady-state
Kalman filter is proposed. The advantage of the
proposed method is that the identification and
computation of cross-covariance is avoided, thus
the computational burden is significantly reduced.
The new algorithm gives an upper bound of the
covariance intersection fused variance matrix
based on the convex combination of local
estimations, therefore, ensures the convergence of
the fusion filter. The accuracy of the covariance
intersection (CI) fusion filter is lower than and
close to that of the optimal distributed fusion
steady-state Kalman filter, and is far higher than
that of each local estimator. A numerical example
shows that the covariance intersection fusion
Kalman filter has enough fused accuracy without
computing the cross-covariance
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