Nonlinear event-based state estimation using sequential Monte Carlo approach

Chitraganti, Shaikshavali; Darwish, M.A.

doi:10.1109/cdc.2017.8263966

Cited by 2 publications

(6 citation statements)

References 26 publications

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“…By reordering the condition to n S i=1 p T (i) > 1 − c, this can be recognize from (28) as the generalized inverse distribution function.…”

Section: ) Estimating the Trigger Probabilitymentioning

confidence: 99%

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Improved event-based particle filtering in resource-constrained remote state estimation

Ruuskanen¹,

Cervin²

2022

Preprint

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Event-based sampling has been proposed as a general technique for lowering the average communication rate in remote state estimation, which can be important in scenarios with constraints on resources such as network bandwidth or sensor energy. Recently, the interest of applying particle filters to event-based state estimation has seen an upswing, partly to tackle nonlinear and non-Gaussian problems, but also since event-based sampling does not allow an analytic solution for linear-Gaussian systems. Thus far, very little has been mentioned regarding the practical issues that arise when applying particle filtering to event-based state estimation. In this paper, we provide such a discussion by (i) demonstrating that there exists a high risk of sample degeneracy at new events, for which the auxiliary particle filter provides an intuitive solution, (ii) introducing a new alternative to the local predictor approach based on precomputing state estimates which is better suited to solve the issue of observer-to-sensor communication for closed-loop triggering in difficult systems, and (iii) exploring the difficulties surrounding the increased computational load when implementing the particle filter under event-based sampling.

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“…By reordering the condition to n S i=1 p T (i) > 1 − c, this can be recognize from (28) as the generalized inverse distribution function.…”

Section: ) Estimating the Trigger Probabilitymentioning

confidence: 99%

“…In the event-based literature, an approach for tackling the likelihood evaluation has been to use simple Monte Carlo integration [13], [28],…”

Section: A Evaluating the No-event Likelihood Densitymentioning

confidence: 99%

Improved event-based particle filtering in resource-constrained remote state estimation

Ruuskanen¹,

Cervin²

2022

Preprint

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“…System 2: The classical, multi-modal nonlinear system often used for benchmarking particle filters [11], [14], [18], [19]: particles. The mean squared error (MSE) of the state estimation and the fraction of time instances where γ k = 1 are logged in the simulation.…”

Section: Simulation Studymentioning

confidence: 99%

“…In [12] and [13], the authors derive bootstrap particle filters under the SOD scheme. In [14], IBT is instead used, but the choice of trigger is not analyzed further. These filters suffer from particle degeneracy at event instances; in our previous work [15] we tackled this issue by using an auxiliary particle filter.…”

Section: Introductionmentioning

confidence: 99%

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On Innovation-Based Triggering for Event-Based Nonlinear State Estimation Using the Particle Filter

Ruuskanen¹,

Cervin²

2020

2020 European Control Conference (ECC)

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Event-based sampling has been proposed as a general technique for lowering the average communication rate, energy consumption and computational burden in remote state estimation. However, the design of the event trigger is critical for good performance. In this paper, we study the combination of innovation-based triggering and state estimation of nonlinear dynamical systems using the particle filter. It is found that innovation-based triggering is easily incorporated into the particle filter framework, and that it vastly outperforms the classical send-on-delta scheme for certain types of nonlinear systems. We further show how the particle filter can be used to jointly precompute the future state estimates and trigger probabilities, thus eliminating the need for periodic observerto-sensor communication, at the cost of increased computational burden at the observer. For wireless, battery-powered sensors, this enables the radio to be turned off between sampling events, which is key to saving energy.

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Nonlinear event-based state estimation using sequential Monte Carlo approach

Cited by 2 publications

References 26 publications

Improved event-based particle filtering in resource-constrained remote state estimation

Improved event-based particle filtering in resource-constrained remote state estimation

On Innovation-Based Triggering for Event-Based Nonlinear State Estimation Using the Particle Filter

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