Hybrid state space model-based sensor data blind correction method

A space model and mixed state technology, applied in the field of sensor networks, can solve problems such as high cost of sensors, inability to accurately know the true value of sensor signals, and too many sensors

Active Publication Date: 2019-06-18
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] In the large-scale deployment of wireless sensor networks, the application of sensor data correction often has the following two problems: the number of sensors in the wireless sensor network is too large...

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  • Hybrid state space model-based sensor data blind correction method
  • Hybrid state space model-based sensor data blind correction method
  • Hybrid state space model-based sensor data blind correction method

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Embodiment Construction

[0095] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0096] see Figure 1 ~ Figure 3 , figure 1 It is a flow chart of the sensor data blind calibration method based on the hybrid state space model described in...

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Abstract

The invention relates to a hybrid state space model-based sensor data blind correction method and belongs to the field of sensor networks. The method includes the following steps that: S1, a sensor data blind correction problem is modeled as an HSSM (hybrid state space model); S2, the a posteriori distribution of nonlinear observation signal parameters in the HSSM is obtained through using a UT-FBalgorithm according to relations between various parameters in the HSSM, and the a posteriori distribution of the other parameters in the HSSM is obtained through using Bayes' theorem and Dirichlet process; S3, the IMCMC (Iterative Markov Chain Monte Carlo) sampling method is used to iteratively collect the samples of the a posterior distribution of the parameters; and S4, some initial samples ofa sample set obtained by means of sampling in the S3 are discarded according the nature of the Markov chain, and the average value of an obtained sample set is solved, so that the estimated values ofcorrected parameter gain and offset are obtained. With the method of the invention adopted, the accuracy of sensor correction is improved under the premise of ensuring that the established model is close to the real application scenario of a sensor network.

Description

technical field [0001] The invention belongs to the field of sensor networks, and relates to a sensor data blind correction method based on a mixed state space model. Background technique [0002] With the widespread application of large-scale, long-distance wireless sensor networks, sensor calibration has gradually become a problem that needs attention. Due to cheap sensor materials for large-scale deployment, insufficient electronic components, and susceptibility to environmental influences, each sensor in a large-scale deployment needs to undergo a calibration step before its observations can be used correctly. [0003] The sensor parameters that need to be corrected usually include gain, offset and drift. Among them, the gain refers to the parameter that determines how much the sensor responds when the input signal changes, and the offset refers to the sensor data that determines the zero reference value. The parameters of the offset value on the y-axis, the drift refer...

Claims

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Application Information

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IPC IPC(8): G01D18/00
Inventor 谢昊飞高兴王平苏文君
Owner CHONGQING UNIV OF POSTS & TELECOMM
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