Hybrid state space model-based sensor data blind correction method

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...

AI Technical Summary

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, and the cost of correcting each sensor one by one using traditional correction methods is too high; In the scene, it is impossible to accurately know the true value of the environmental signal observed by the sensor

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