|Abstract:||Global navigation satellite system (GNSS) simulator is widely used to test the positioning performance of receiver. In order to make the simulated signal more realistic, it is important to study the statistical characteristics of GNSS signal power in different types of environment. This paper researches the characteristics of signal attenuation for both GPS and BDS GEO satellite in urban, suburb, viaduct-down and viaduct-up environment. Here, a hidden Markov Gaussian mixture model (HMGMM) is proposed to describe the characteristics of signal power attenuation. The statistical model is composed of four states which contains one invisibility state and three visibility states. For each visibility state, we use Gaussian distribution to describe the probability density of signal power attenuation. And the invisibility state means that no signal can be detected by receiver. The expectation maximization (EM) algorithm is used to calculate all the parameters of HM-GMM including state transition matrix, probability matrix and parameters of each Gaussian distribution. In the experiment, the raw data of signal power attenuation is measured by a business receiver, and the carrier of receiver is a van. The raw data is analyzed to build a set of statistical models in different elevation interval, and all detailed parameters is shown in this paper. In conclusion, we propose a novel distribution model to characterize GNSS signal attenuation in different types of environment. The result will give useful guidance to improve the performance of GNSS simulator.|
Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||2011 - 2018|
|Cite this article:||
Wang, Yuze, Liu, Peilin, Adeel, M., Chen, Xin, "Statistical Model Based on Markov Chain for GPS and BDS Signal in Different Environments," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2011-2018.
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