The modeling and estimation of asynchronous multirate multisensor dynamic systems

Lin, Yan; Liu, B.S.; Zhou, Donghua

doi:10.1016/j.ast.2005.09.001

Cited by 80 publications

(58 citation statements)

References 13 publications

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“…Its advantage is that it does not require any prior knowledge of measurement data, and only on the basis of measurement precision of the measurement data to determine the corresponding weighting of different data and calculate the minimum mean square error value. It can inhibit the drift and noise of sensors to some extent, improve the accuracy of measurement [2]. The model is shown in figure 3.…”

Section: The Data Fusionmentioning

confidence: 99%

The Research of Gas Detection Based on data Fusion

Wang¹,

Liu²

2015

Advances in Intelligent Systems Research

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Abstract:Aiming at the problem of gas pollution in the environment, the traditional testing equipments often appear the phenomenon of misstatement and false positives. A real-time online monitoring system is designed based on the detecting and control of harmful gases. The lower computer system make the STM32F103 microcontroller as the core, PC as the monitoring center, combining the technology of multi-sensor data fusion, using self-adapting weighting data fusion algorithm and decision level data fusion algorithm based on fuzzy set theory. Through the upper machine people can not only understand the quality of the indoor environment in time, and the system can inhibit the drift and noise of sensors to some extent, improve the measuring accuracy of the monitoring system for harmful gas in the environment, improve system reliability, reduce system fuzziness, enhance the reliability of system and assure the system having good robustness.

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Section: The Data Fusionmentioning

confidence: 99%

The Research of Gas Detection Based on data Fusion

Wang¹,

Liu²

2015

Advances in Intelligent Systems Research

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“…III-A), and containing the least external influence, e.g., dynamic excitation. Alternatively, time synchronization can be included implicitly in the sensor fusion method by means of multi-rate fusion filters that account for different sampling rates and are updated whenever one of the data acquisition sources provides a new measurement [31], [32].…”

Section: ) Time Synchronizationmentioning

confidence: 99%

In-flight data acquisition and flight testing for system identification of flapping-wing MAVs

Caetano

Armanini

Karásek

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Although flapping-wing micro aerial vehicles have become a hot topic in academia, the knowledge we have of these systems, their force generation mechanisms and dynamics is still limited. Recent technological advances have allowed for the development of free flight test setups using on-board sensors and external tracking systems, for system identification purposes. Nevertheless, there is still little knowledge about the system requirements, as well as on how to perform free flight test experiments, and process the collected data. The present article presents the guidelines for flapping-wing micro aerial vehicle free flight testing. In particular, it gathers information produced by different studies and provides the best practices for the proper system dimensioning, system setup, on-board sensors, maneuver input design, error analyses and data postprocessing, for the reconstruction of the forces and moments that act during free flight of a flapping-wing robot, for system identification and modeling purposes. Furthermore, this article compares the results obtained using external optical position tracking systems with on-board and external sensor fusion, and provides suitable solutions and methods for data fusion and force reconstruction.

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“…According to the linear dynamic system with multirate multisensor asynchronous sampling, literature [8] presented the following data block and the dimension expand model.…”

Section: B Data Block and Dimension Expand Modelmentioning

confidence: 99%

“…In this paper, aimed at a multirate multisensor dynamic system, using the model of data block and expand dimension [8], the asynchronous multirate multisensor information fusion in the form into synchronous single rate information fusion, and the use of Kalman filtering and Carlson optimal data fusion criterion [9], achieve multisensor state fusion estimation. The experiment of state fusion estimation on radar tracking shows that this algorithm is better than the result of directed Kalman filtering on smallest scale, the estimation error is less than single sensor Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

State Fusion Estimation Based on Asynchronous Multirate Multisensor

Li¹,

Zhang²

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-On theory of multisensor state fusion estimation, more research is a single rate synchronization problem, however, it is the multirate asynchronous problem often encountered in practice. Therefore, research on the state fusion estimation of asynchronous multirate multisensor have more practice application value. In this paple, by expand the dimension of the system state and measurements and by dividing them into proper data blocks, the multirate asynchronous sampling system is formalized into a synchronous sampling system with single sampling rate, therefore, by use of Kalman filter and Carlson optimal data fusion criterion, the optimal state fusion estimation in the sense of linear minimum variance is achieved. The experiment of state fusion estimation on radar tracking shows that this algorithm is better than the result of directed Kalman filtering on smallest scale, the estimation error is less than single sensor Kalman filtering. The method can also be used for integrated navigation, signal processing, image processing and many fields.

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The modeling and estimation of asynchronous multirate multisensor dynamic systems

Cited by 80 publications

References 13 publications

The Research of Gas Detection Based on data Fusion

The Research of Gas Detection Based on data Fusion

In-flight data acquisition and flight testing for system identification of flapping-wing MAVs

State Fusion Estimation Based on Asynchronous Multirate Multisensor

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