Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Vahidpour, Vahid; Rastegarnia, Amir; Latifi, Milad; Khalili, Azam; Sanei, Saeid

doi:10.1109/taes.2019.2933961

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…In [9], in order to avoid the unlimited bandwidth requirement, the parameter estimate is quantized before the diffusion of information. In the partial diffusion strategies [10]- [13], only a subset of the local estimates is allowed to share among the neighbors. Vahidpour et al in [13] study the effect of channel noise during the exchange of weight estimates for partial diffusion algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In the partial diffusion strategies [10]- [13], only a subset of the local estimates is allowed to share among the neighbors. Vahidpour et al in [13] study the effect of channel noise during the exchange of weight estimates for partial diffusion algorithm. In [14], the nodes transmit the sign of innovation (SOI) sequence in the distributed state estimation framework.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Partial Diffusion Kalman Filter With Adaptive Combiners

Khalili

Vahidpour

Rastegarnia

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partial Diffusion Kalman Filter With Adaptive Combiners

Khalili

Vahidpour

Rastegarnia

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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“…In [11], sensor data transmitted are compressed via dimensionality reduction. The partial diffusion algorithm proposed in [12], [13] reduces the amount of information exchanged between the sensor nodes by selecting the entries to be transmitted simply using an entry selection matrix without complex algorithms.…”

Section: Introductionmentioning

confidence: 99%

Information-Weighted Consensus Filter with Partial Information Exchange

Jeon¹,

He²

2021

Preprint

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In this note, the information-weighted consensus filter (ICF) with partial information exchange is proposed to reduce the bandwidth of the signals transmitted between the sensor nodes and guarantee its convergence to the centralized Kalman filter (CKF). In the proposed algorithm, a part of information chosen with the entry selection matrix is transmitted to the sensor nodes in the neighborhood at each consensus step, and consensus averaging is conducted at each sensor node with the partial and the local information. This ensures that the proposed distributed estimation algorithm converges to the centralized algorithm, while allowing the proposed algorithm to achieve bandwidth reduction of the signals transmitted between the sensors. With the proposed algorithm, the stability of the estimation error dynamics is proven and the convergence to the centralized algorithm is mathematically shown using the property of the average consensus. Simulations are conducted to validate the proposed ICF with partial information exchange and the related theoretical findings.

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“…The performance analysis of time-domain adaptive filters in the under-modeling situation is established in [8], where a deficient length ACLMS in C has been considered for second order improper signals. Some distributed versions of partial-update adaptive filters such as [32]- [37] have been developed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis

Vahidpour

Rastegarnia

Khalili

et al. 2020

IEEE Trans. Signal Process.

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Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationallyand memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partialupdate schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partialupdate implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for noncircular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples.

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Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Cited by 13 publications

References 42 publications

Partial Diffusion Kalman Filter With Adaptive Combiners

Partial Diffusion Kalman Filter With Adaptive Combiners

Information-Weighted Consensus Filter with Partial Information Exchange

Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis

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