A new evil waveforms evaluating method for new BDS navigation signals View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04

AUTHORS

Chengyan He, Ji Guo, Xiaochun Lu, Xue Wang, Yongnan Rao, Li Kang, Zhigang Hu

ABSTRACT

With the advent of new global navigation satellite systems (GNSSs) and new signals, GNSS users will rely more on them to obtain higher-accuracy positioning. Evil waveform monitoring and assessment are of great importance for GNSS to achieve its positioning, velocity, and timing service with high accuracy. However, the advent of new navigation signals introduces the necessity to extend the traditional analyzing techniques already accepted for binary phase-shift keying modulation to new techniques. First, the well-known second-order step thread model adopted by the International Civil Aviation Organization is introduced. Then the extended new general thread models are developed for the new binary offset carrier modulated signals. However, no research has been done on navigation signal waveform symmetry yet. Simulation results showed that, waveform asymmetry may also cause tracking errors, range biases, and position errors in GNSS receivers. It is thus imperative that the asymmetry be quantified to enable the design of appropriate error budgets and mitigation strategies for various application fields. A novel evil waveform analysis method, called waveform rising and falling edge symmetry (WRaFES) method, is proposed. Based on this WRaFES method, the correlation metrics are provided to detect asymmetric correlation peaks distorted by received signal asymmetry. Then the statistical properties of the proposed methods are analyzed, and a proper deformation detection threshold is calculated. Finally, both simulation results and experimentally measured results of Beidou navigation satellite system (BDS) M1-S B1Cd signal are given, which show the effectiveness and robustness of the proposed thread models. More... »

PAGES

37

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10291-018-0698-x

DOI

http://dx.doi.org/10.1007/s10291-018-0698-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100590460


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