Parallelization of the Kalman filter on multicore computational platforms

Rosen, Olov; Medvedev, Alexander; Wigren, Torbjörn

doi:10.1016/j.conengprac.2013.03.008

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…It is pointed out here that the parallel method in this paper has a fundamental difference with some other parallel methods [ 23 , 24 ]. The others owe their decoupling to the so-called “mandatory delay”, which may cause certain accuracy damage.…”

Section: Computational Optimizationmentioning

confidence: 99%

Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

Wang

et al. 2015

Sensors

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Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

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Section: Computational Optimizationmentioning

confidence: 99%

Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

Wang

et al. 2015

Sensors

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“…According to (26) and (27), a Kalman filter gain of linear continuous-time systems should be solved by a model state error covariance matrix , covariance matrices of random process noise, and measurement noises and . Based on the above statistics, is used to calculate the Kalman gain Shock and Vibration of discrete systems; this process has been shown by (34).…”

Section: The Simulation Of Wind-induced Motions Of a High-risementioning

confidence: 99%

“…A robust cubature Kalman filter (CKF) was designed for multisensors discretetime systems with uncertain noise variances in [23]. Generally, Kalman filter, considering the disturbance as the observation input, can be used to estimate the system state by output data and is often applied in linear, discrete-time and finite dimensional systems [24][25][26][27]. Normal Kalman filter cannot consider input excitation during state estimation.…”

Section: Introductionmentioning

confidence: 99%

An Observer-Based Controller with a LMI-Based Filter against Wind-Induced Motion for High-Rise Buildings

Chen

Teng

et al. 2017

Shock and Vibration

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Active mass damper (AMD) control system is proposed for high-rise buildings to resist a strong wind. However, negative influence of noise in sensors impedes the application of AMD systems in practice. To reduce the adverse influence of noise on AMD systems, a Kalman filter and a linear matrix inequality-(LMI-) based filter are designed. Firstly, a ten-year return period fluctuating wind load is simulated by mixed autoregressive-moving average (MARMA) method, and its reliability is tested by wind speed power spectrum and correlation analysis. Secondly, a designed state observer with different filters uses wind-induced acceleration responses of a high-rise building as the feedback signal that includes noise to calculate control force in this paper. Finally, these methods are applied to a numerical example of a high-rise building and an experiment of a single span four-storey steel frame. Both numerical and experimental results are presented to verify that both Kalman filter and LMI-based filter can effectively suppress noise, but only the latter can guarantee the stability of AMD parameters.

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Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising

Yan

Xia

et al. 2019

Kalman Filtering and Information Fusion

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Parallelization of the Kalman filter on multicore computational platforms

Cited by 5 publications

References 21 publications

Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

An Observer-Based Controller with a LMI-Based Filter against Wind-Induced Motion for High-Rise Buildings

Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising

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