Kalman filtering for multiple time-delay systems

Lu, Xiao; Zhang, Huanshui; Wang, Wei; Teo, K.L.

doi:10.1016/j.automatica.2005.03.018

Cited by 150 publications

(109 citation statements)

References 4 publications

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“…Recent results on Kalman filtering for time-delay systems can be found in [69][70][71][72]. The novel idea in [69] and [70] is to reorganize the measurements from different channels as a delay free system.…”

Section: Treatments Of Network-induced Phenomenamentioning

confidence: 99%

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Particle filtering with applications in networked systems: a survey

Wang

Yuan

2016

Complex Intell. Syst.

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The particle filtering algorithm was introduced in the 1990s as a numerical solution to the Bayesian estimation problem for nonlinear and non-Gaussian systems and has been successfully applied in various fields including physics, economics, engineering, etc. As is widely recognized, the particle filter has broad application prospects in networked systems, but network-induced phenomena and limited computing resources have led to new challenges to the design and implementation of particle filtering algorithms. In this survey paper, we aim to review the particle filtering method and its applications in networked systems. We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress inThe work of W. Li, Y. Yuan and L.

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Section: Treatments Of Network-induced Phenomenamentioning

confidence: 99%

“…The novel idea in [69] and [70] is to reorganize the measurements from different channels as a delay free system. This reorganized innovation is combined with the orthogonal projection formula to derive the optimal filter.…”

Section: Treatments Of Network-induced Phenomenamentioning

confidence: 99%

Particle filtering with applications in networked systems: a survey

Wang

Yuan

2016

Complex Intell. Syst.

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“…The ways of using a Kalman filter to design observer for time-delay systems were shown in [18]. The methodologies for designing robust observer for timedelay systems were described in [19], [20].…”

Section: Observers For Time-delay Systemsmentioning

confidence: 99%

Evaluation of Load Balancing Performance of Parallel Processing Linear Time-Delay Systems

Kabir

Alam²,

Azad³

2015

IJIEEB

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Abstract-Time delays in system states or control may result into unacceptable system operation or uncertainty in specialised technical systems like aircraft control, plant control, robotics etc. The issue of robustness, controllability, traceability, flexible management, reliability, and safety of such systems with time-delays, has been one of the primary research focuses of the last few decades. In parallel computing, different computing subunits share their tasks to balance loads to increase performance and throughput. In order to do so, subsystems have to communicate among themselves, adding further delay on top of existing system delay. It is possible to maintain performance and stability of the whole system, by designing observer for every subsystem in the system, overseeing the system state and compensating for existing time-delay. This paper reviews present literature to identify a linear time-delay system for load balancing and evaluates the stability and load balancing performance of the system with and without an observer. Stability is analysed in terms of oscillation in the system responses and performance is evaluated as the speed of load-balancing operation.

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“…If this is the case, existing IMS filtering-based mechanisms will not be applicable. Of course, to deal with a time delay of measurements in the estimation filtering, several estimation filters have been researched in Basin and Zuniga, 15 Zhang et al, 16 and Lu et al 17,18 However, these estimation filters also have the IMS. Although existing indoor positioning mechanisms adopt these estimation filters in Basin and Zuniga, 15 Zhang et al, 16 and Lu et al 17,18 to deal with a measurement delay, they can still show poor performance and even divergence phenomenon for temporary modeling uncertainties and numerical errors.…”

Section: Introductionmentioning

confidence: 99%

A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

Kim

Lee

Jang

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, an alternative indoor positioning mechanism is proposed considering finite memory structure filter as well as measurement delay. First, a finite memory structure filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least-squares criterion, which utilizes only finite measurements on the most recent window. The proposed finite memory structure filtering-based mechanism gives the filtered estimates for position, velocity, and acceleration of moving target in real time, while removing undesired noisy effects and preserving desired moving positions. Second, the proposed mechanism is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Third, through discussions about the choice of window length, it is shown that this can be considered as a useful design parameter to make the performance of the proposed mechanism as good as possible. Finally, computer simulations show that the performance of the proposed finite memory structure filtering-based mechanism can outperform the existing infinite memory structure filtering-based mechanism for the abruptly varying acceleration of moving target. KeywordsIndoor positioning system, wireless sensor networks, measurement delay, finite memory structure filter, infinite memory structure filter.

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Kalman filtering for multiple time-delay systems

Cited by 150 publications

References 4 publications

Particle filtering with applications in networked systems: a survey

Particle filtering with applications in networked systems: a survey

Evaluation of Load Balancing Performance of Parallel Processing Linear Time-Delay Systems

A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

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