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. 相似文献
Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.