Partial Diffusion Kalman Filter With Adaptive Combiners

Khalili, Azam; Vahidpour, Vahid; Rastegarnia, Amir; Bazzi, Wael M.; Sanei, Saeid

doi:10.1109/taes.2020.3046085

Cited by 8 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note also that the communication saving is achieved at the expense of performance loss (57.85% and 40% for L = 1 and L = 3, respectively. Finally, Table I compares the steady-state performance of the proposed algorithm with that of a non-cooperative system, DiffKF, and the algorithms in [24], [27]. As we expected diffKF exhibits better performance since in diffKF nodes share all available local information.…”

Section: Empirical Evaluationsmentioning

confidence: 86%

“…Efforts have been made to reduce the communication burden in diffusion networks. In [22]- [27], some partial diffusion strategies have been proposed where a subset of the entries of intermediate estimate vectors are allowed to share among the neighbors. In [28], the Krylov subspace projection method has been used to perform dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Khalili¹,

Vahidpour²,

Rastegarnia³

et al. 2022

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multi-agent networks is proposed in this paper. In the proposed algorithm (referred to as reduced-link diffusion Kalman filter (RL-diffKF)) the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL-diffKF algorithm, an indepth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations.

show abstract

Section: Empirical Evaluationsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Khalili¹,

Vahidpour²,

Rastegarnia³

et al. 2022

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

show abstract

“…h i is auxiliary matrix. i 1 ; …; i n i is the index of neighbourhood node of node i, and e i 1 represents the i 1 th column of the N � N -dimensional identity matrix, then the optimisation problem (38) can be written as follows:…”

Section: Weight Adaptationmentioning

confidence: 99%

“…This was further refined in refs. [37,38] leading to the development of the partial diffusion Kalman filter (PDKF) algorithm. In the incremental update stage of PDKF, each node do not exchange the local data with their neighbours only using its own measurement for local estimation.…”

Section: Introductionmentioning

confidence: 99%

A novel distributed bearing‐only target tracking algorithm for underwater sensor networks with resource constraints

Zhao,

Li,

Pang

et al. 2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Underwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

show abstract

“…The method was originally reported for LMS adaptive filter [7], but its extended to other adaptive filters [8]- [12]. Some distributed versions of PU adaptive filters such as [13]- [19] have been developed in the literature.…”

Section: Introductionmentioning

confidence: 99%

State Estimation in Linear Dynamical Systems By Partial Update Kalman Filtering

Pourasad

Vahidpour

Rastegarnia

et al. 2021

Circuits Syst Signal Process

Self Cite

View full text Add to dashboard Cite

In this letter, we develop a partial update Kalman filtering (PUKF) algorithm to solve the state of a discrete-time linear stochastic dynamical system. In the proposed algorithm, only a subset of the state vector is updated at every iteration, which reduces its computational complexity, compared to the original KF algorithm. The required conditions for the stability of the algorithm are discussed. A closed-form expression for steady-state mean-square deviation (MSD) is also derived. Numerical examples are used to validate the correctness of the provided analysis. They also reveal the PUKF algorithm exhibits a trade-off between the estimation accuracy and the computational load which is extremely profitable in practical applications.

show abstract

Partial Diffusion Kalman Filter With Adaptive Combiners

Cited by 8 publications

References 33 publications

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

A novel distributed bearing‐only target tracking algorithm for underwater sensor networks with resource constraints

State Estimation in Linear Dynamical Systems By Partial Update Kalman Filtering

Contact Info

Product

Resources

About