基于自支持度和假设检验的多传感器数据融合一致性检验

Kai Zheng, Gangquan Si, Zhou Zhou, Jiaxi Chen, Wenmeng Yue
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引用次数: 2

摘要

针对观测结果中存在传感器虚假或错误信息影响数据融合估计的可能性,提出了一种基于自支持度和假设检验的融合估计算法。采用一致性测试方法,通过对观测结果的检验来识别和去除失效传感器的观测值,从而有效地利用数据融合算法进行估计,具有重要意义。基于一致性检验中使用的后验概率和假设检验的知识,我们把一致性检验问题看作是对两个总体均值之差的假设检验。同时,研究了多传感器的多值问题。根据不同时间的观测值,可以得到各传感器在不同时间关于假设检验的一致性值。仿真结果表明,基于自支持度和假设检验的新方法对各传感器观测结果的质量进行评估,识别虚假和错误的观测结果,并提供具有可靠一致性传感器组的数据融合估计,简单有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Consistency test based on self-support degree and hypothesis testing for multi-sensor data fusion
Focusing on the possibility for observed results with false or erroneous information of sensors which may affect data fusion estimation, a new algorithm is proposed based on self-support degree and hypothesis testing. It is significant to take the consistent test method to identify and remove the observations of failed sensors by checking on the observed results and then the data fusion algorithm could be used for estimation effectively. Based on posteriori probability used in consistent test and the knowledge of hypothesis testing, we regard the problem of consistency test as the hypothesis testing of the difference between two population means. Meantime, the multi-valued problem of multiple sensors is researched. Based on the observed values of different time, each sensor's consistency value of different time about hypothesis testing can be obtained. The results of simulation show the simplicity and effectiveness of the new method based on self-support degree and hypothesis Testing for evaluating the quality of each sensor observations, identifying false and erroneous observed results and providing data fusion estimation with the reliable consistent sensor group.
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