Nonlinear event-based state estimation using particle filtering approach

Sid, Mohamed Amine; Chitraganti, Shaikshavali

doi:10.1109/icmic.2016.7804238

Cited by 6 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the particle filters, adaptive resampling with systematic resampling was used in the resampling step where the limit for the effective sample size was set to N lim = N/2. In the special case of a LG system, the joint density (14) can for γ k = 1 be found exactly as…”

Section: Approximating the Fully-adapted Filter For Event-based Smentioning

confidence: 99%

“…In [13] the use of eventbased sampling in nonlinear filtering is described, and as an example a particle filter is demonstrated. In [14], Sid and Chitraganti derive an event-based extension to the particle filter where the likelihood is computed using numerical integration. In [15], Davar and Mohammadi derive an eventbased particle filter tailored for an LG system with SOD triggering.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event-Based State Estimation Using the Auxiliary Particle Filter

Ruuskanen

Cervin

2019

2019 18th European Control Conference (ECC)

View full text Add to dashboard Cite

Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is not well suited for state estimation in event-based systems. We propose an extension to the auxiliary particle filter for systems with event-based measurements, in which certain existing techniques for finding an approximation of the fully adapted filter can easily be utilized. In a simulation study, we demonstrate that at new measurement events, the benefits of using the auxiliary particle filter increases when fewer measurements are being sent.

show abstract

Section: Approximating the Fully-adapted Filter For Event-based Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-Based State Estimation Using the Auxiliary Particle Filter

Ruuskanen

Cervin

2019

2019 18th European Control Conference (ECC)

View full text Add to dashboard Cite

show abstract

“…The combination of event-based sampling and particle filtering has recently been explored in several papers. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Ruuskanen¹,

Cervin²

2020

2020 European Control Conference (ECC)

View full text Add to dashboard Cite

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.

show abstract

“…These issues have resulted in a recent surge of interest in developing intelligent transmission, scheduling, and estimation schemes [4]- [12] to reduce the communication overhead of sensors in order to increase their practical applicability by improving their energy efficiency.…”

mentioning

confidence: 99%

“…However, while Gaussian-based approximation of the eventbased posterior has been investigated extensively, application of non-Gaussian filtering using particle filters [25]- [28] is still in its infancy. To the best of our knowledge, only very recently, EBE using non-Gaussian particle filter approximation is considered in [11] and [12], where in the latter simply the number of particles belonging to the triggering set is used to update particle weights, while the former uses stochastic triggering [19] which results in having a Gaussian posterior. The paper addresses this gap.…”

mentioning

confidence: 99%

Event-based particle filtering with point and set-valued measurements

Davar

Mohammadi

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-The paper is motivated by recent and rapid growth of Cyber-Physical Systems (CPS) and the critical necessity for preserving restricted communication resources in their application domains. In this context, a distributed state estimation architecture is considered where a remote sensor communicates its measurements to the fusion centre (FC) in an event-based fashion. We propose a systematic and intuitively pleasing distributed state estimation algorithm which jointly incorporates point and set-valued measurements within the particle filtering framework. Referred to as the event-based particle filter (EBPF), point-valued measurements are incorporated in the estimation recursion via a conventional particle filter formulation, while set-valued measurements are incorporated by developing an observation update step similar in nature to quantized particle filtering approach. More specifically, in the absence of an observation (i.e., having a set-valued measurement), the proposed EBPF evaluates the probability that the unknown observation belongs to the eventtriggering set based on its particles which is then used to update the corresponding particle weights. The simulation results show that the proposed EBPF outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed hybrid estimation algorithm.

show abstract

Nonlinear event-based state estimation using particle filtering approach

Cited by 6 publications

References 19 publications

Event-Based State Estimation Using the Auxiliary Particle Filter

Event-Based State Estimation Using the Auxiliary Particle Filter

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

Event-based particle filtering with point and set-valued measurements

Contact Info

Product

Resources

About