Centralized and distributed adaptive cubature information filters for multi-sensor systems with unknown probability of measurement loss

Lv, Yuanwei; Yang, Guang‐Hong

doi:10.1016/j.ins.2023.02.035

Cited by 11 publications

(8 citation statements)

References 45 publications

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“…□ Theorem 4. For nonsingular F k , the EKF parameters given through Equations ( 17) and (18), and the inequalities presented in Equation (22), the stochastic dynamic model e k|k (Equation 20) remains exponentially bounded in mean square, that is,…”

Section: Statementmentioning

confidence: 99%

“…Let us now consider the bound of P k|k given in Equation (22). Taking inverse and multiplying e T k|k and…”

Section: Statementmentioning

confidence: 99%

“…Remark The missing and delay probabilities can be obtained from intelligent analysis of historic data patterns and training information. Nevertheless, such probabilities can also be obtained online [14, 20, 22]. …”

Section: Gaussian Filtering With Irregularities In Measurementsmentioning

confidence: 99%

“…The literature further witnesses some preliminary contributions [12,[21][22][23][24] to handle the missing measurements independently (ignoring the presence of delay). More specifically, [12] redesigns the conventional EKF for partially missing (fractionally received) measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The same approach is extended in [24] for a new nonlinear filter named high‐degree CKF. This approach was extended in [22] for unknown missing measurement probability, considering a multisensor environment with centralized and distributed data processing techniques. A very recent contribution [25] discusses about the joint occurrences of delayed and missing measurements.…”

Section: Introductionmentioning

confidence: 99%

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Improved Gaussian filtering for handling concurrent delayed and missing measurements

Kumar

Naik

Pachori

et al. 2023

Asian Journal of Control

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This article addresses the Gaussian filtering problem under the environment of jointly occurring delayed and missing measurements. In this work, the former irregularity is incorporated (in the measurement model) using a Bernoulli random variable (BRV) and a geometric random variable, while the latter is subsumed with the help of the BRV; thereby, it enables to take account of large delay extents efficiently. Specifically, a modified measurement model, which incorporates the concerned irregularities, is introduced. Accordingly, the measurement‐related statistical parameters, that is, measurement estimate, covariance, and cross‐covariance, are rederived with respect to the modified measurement model. The rederived parameters replace the corresponding ones in the traditional Gaussian filtering algorithm, resulting in the proposed Gaussian filter. The simulation results conclude the superior performance of the proposed filter over the existing filters in handling the coexisting delay and missing measurement irregularities.

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Section: Statementmentioning

confidence: 99%

“…Let us now consider the bound of P k|k given in Equation (22). Taking inverse and multiplying e T k|k and…”

Section: Statementmentioning

confidence: 99%