An overview of multirate multisensor systems: Modelling and estimation

Sun, Shuli

doi:10.1016/j.inffus.2019.05.002

Cited by 32 publications

(6 citation statements)

References 97 publications

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“…In this paper, a generic multi-sensor fusion scheme based on nonlinear MHE is proposed. The proposed fusion scheme is generic in the sense that it can take care of multiple sample rates (Lin and Sun, 2019), missing measurements, outlier rejection, and real-time requirements.…”

Section: Related Workmentioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

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Section: Related Workmentioning

confidence: 99%

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

Osman

Mehrez

Daoud

et al. 2021

Transactions of the Institute of Measurement and Control

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“…As such, the studies on SNs with multi-rate sampling schemes have stirred rapidly increasing research interests. [23][24][25] The earliest research on MRSs is attributed to the work of Kranc, 26 where the Z-transform method that solves the simultaneous algebraic equations to transform the data sampling system into a same-rate sampling system has been proposed, and the stability and performance analysis theory of single-rate systems has been utilized. In most recent studies, three classical methods are often adopted to transform MRSs into single-rate systems.…”

Section: Introductionmentioning

confidence: 99%

“…The multi‐rate systems (MRSs) are often encountered in civilian, military and industrial areas because different electronic components have different physical properties, and that is particularly suitable for SNs which consist of a large number of sensor nodes. As such, the studies on SNs with multi‐rate sampling schemes have stirred rapidly increasing research interests 23–25 . The earliest research on MRSs is attributed to the work of Kranc, 26 where the

Z

‐transform method that solves the simultaneous algebraic equations to transform the data sampling system into a same‐rate sampling system has been proposed, and the stability and performance analysis theory of single‐rate systems has been utilized.…”

Section: Introductionmentioning

confidence: 99%

An event‐triggered method to distributed filtering for nonlinear multi‐rate systems with random transmission delays

Li,

Hu,

Chen

et al. 2024

Adaptive Control & Signal

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SummaryIn this article, an event‐triggered recursive filtering problem is studied for a class of nonlinear multi‐rate systems (MRSs) with random transmission delays (RTDs). The RTDs are described by utilizing random variables with a known probability distribution and the Kronecker function. To facilitate further study, the MRS is converted into a single‐rate one by adopting an iteration equation approach. To address the challenge of filter design caused by different measurement sampling periods, a modified prediction method of measurements is given. Moreover, an event‐triggered mechanism (ETM) is introduced to regulate the innovation transmission frequency. The objective of the addressed filtering problem is to design a recursive distributed filtering method for MRSs subject to ETM and RTDs, where a minimum upper bound on the filter error covariance is obtained. Moreover, the filter gain matrix is formulated by resorting to the solutions to matrix difference equations. Besides, the boundedness in the mean‐square sense of the filtering error is analyzed and a sufficient condition is provided. Finally, simulations with comparison experiments are presented to demonstrate the effectiveness of the newly proposed theoretical results.

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“…MRSs for dynamic system monitoring have been well investigated in many science and engineering fields, such as seismology, structural engineering, geodetic, and navigation [6,8,[10][11][12]. For MRSs where the acceleration has a faster sampling rate while the displacement has a slower sampling rate, the displacement estimation problem with the purpose of improving the quality and frequency of measured displacement data can be solved well using Kalman filtering approaches [13]. In the literature, most researchers construct first-principle state-space models of the dynamic systems and then discretize the continuous-time systems at the sampling points of the acceleration measurement so that Kalman filter can be applied [6,10,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…For MRSs where the acceleration has a faster sampling rate while the displacement has a slower sampling rate, the displacement estimation problem with the purpose of improving the quality and frequency of measured displacement data can be solved well using Kalman filtering approaches [13]. In the literature, most researchers construct first-principle state-space models of the dynamic systems and then discretize the continuous-time systems at the sampling points of the acceleration measurement so that Kalman filter can be applied [6,10,12,13]. Smyth and Wu propose a method based on the multi-rate Kalman filter and smoother to accurately estimate the displacement and velocity from noisy acceleration measurements [6].…”

Section: Introductionmentioning

confidence: 99%

Displacement reconstruction based on Kalman smoothing in multi-rate suspension health monitoring systems

Wang,

Zheng,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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Suspension displacements and wheel center accelerations are important signals for suspension health monitoring systems to improve vehicle reliability and safety. The current way to obtain these signals is to install sensors on vehicles to conduct direct measurements. Usually, displacements are sampled at a slower rate than accelerations due to technical or economic limitations in real scenarios. This paper introduces a method for displacement reconstruction with low-sampling-rate displacement and high-sampling-rate acceleration measurements by formulating the reconstruction problem as a state estimation problem. A state-space model is established by identifying two data-driven models: a time-series Auto-Regressive model and a Finite Impulse Response model. Then, Kalman smoothing is used to estimate the displacement. A series of experiments have been done to show that the estimates from Kalman smoother coincide with the measurements.

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An overview of multirate multisensor systems: Modelling and estimation

Cited by 32 publications

References 97 publications

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

An event‐triggered method to distributed filtering for nonlinear multi‐rate systems with random transmission delays

Displacement reconstruction based on Kalman smoothing in multi-rate suspension health monitoring systems

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