Decentralized Information Filter with Noncommon States

Saini, VP; Paranjape, Aditya A.; Maity, Arnab

doi:10.2514/1.g003862

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Reference [19] uses CI with Gaussian distributions to relax the assumption of known correlations of the fused state. Reference [20] suggests a similar solution to [19] but restrict it to systems where all agents have the same set of common states. Although scalable and simple, such approaches do not account for dependencies between locally exclusive states.…”

Section: B Related Workmentioning

confidence: 99%

Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

Ofer¹,

Ahmed²

2021

Preprint

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In Bayesian peer-to-peer decentralized data fusion for static and dynamic systems, the underlying estimated or communicated distributions are frequently assumed to be homogeneous between agents. This requires each agent to process and communicate the full global joint distribution, and thus leads to high computation and communication costs irrespective of relevancy to specific local objectives. This work considers a family of heterogeneous decentralized fusion problems, where we consider the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest that are subsets of a larger full global joint state. We exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate heterogeneous conditionally factorized channel filter methods. We further extend existing methods for approximate conservative filtering and decentralized fusion in heterogeneous dynamic problems. Numerical examples show more than 99.5% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.

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Section: B Related Workmentioning

confidence: 99%

Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

Ofer¹,

Ahmed²

2021

Preprint

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“…Substituting from (40) into (39) while using (12), (13) and (32), then after simple mathematical manipulation, it is easy to get: j (k + 1|k + 1 ) is given by:…”

Section: The Recursive Formula Of the Filtered Estimatormentioning

confidence: 99%

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

Hassan

2020

IEEE Access

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In this paper, a decomposed state estimator is developed for stochastic constrained nonlinear discrete-time dynamical systems with uncertain parameters. The proposed estimator can deal with general nonlinear uncertain stochastic systems without any pre-defined specifications on the system structure and/or the measurement model. Moreover, it can handle estimation problems for nonlinear stochastic systems subject to a set of imposed linear and/or nonlinear equality and/or inequality constraints. The mathematical structure of the proposed estimator is developed using multiple projection approach and its performance is analyzed and compared with the global (un-decomposed) structure. The main features of the proposed estimator are: it reduces the processing time, it can handle estimation problems with bad initial conditions of the estimator, it can be implemented on modern parallel processing facilities to further reduce the processing time, and last but not least, it minimizes the rounding off errors and hence improves the numerical stability of the algorithm. Simulations results on different real applications are presented to show the effectiveness of the developed approach, and its improved performance when compared with other techniques reported in the literature. INDEX TERMS Constrained stochastic uncertain nonlinear systems, decomposed state estimation, extended Kalman filter, large scale systems, performance analysis.

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“…Previous work on decentralized navigation systems focused on developing decentralized estimation algorithms [10][11][12][13][14][15][16][17][18][19] . The Full Order Extended Kalman Filter (FOEKF) was developed to enable each spacecraft to operate a navigation subsystem and process its own measurements [10][11] .…”

Section: Introductionmentioning

confidence: 99%

“…However, the optimal states of other spacecraft cannot be obtained in the decoupled observation equation, which limits the estimation accuracy. The Iterative Reduced Order Extended Kalman Filter (IREKF) was developed to compensate the accuracy of ROEKF by involving a procedure of iteration [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Fully Decentralized Cooperative Navigation for Spacecraft Constellations

Qin

Macdonald

Dong

2021

IEEE Trans. Aerosp. Electron. Syst.

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This paper proposes a method to decentralize the navigation burden, and improve the fault tolerance for a spacecraft constellation. The constellation body reference system is introduced, which is the perifocal frame of one satellite in the constellation. The structure of the proposed navigation method is constructed to enable each spacecraft to estimate its own orbit in this body reference system. This step is essentially the relative orbit determination based on inter-satellite range measurements. Thereafter, the approach to transfer an orbit from the constellation body reference system to inertial reference system is developed. The essential requirements on absolute

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Decentralized Information Filter with Noncommon States

Cited by 9 publications

References 22 publications

Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

Fully Decentralized Cooperative Navigation for Spacecraft Constellations

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