In order to improve the positioning and navigation performance of Global Navigation Satellite System (GNSS) receivers, a novel method to extract auxiliary information for GNSS receiver is proposed in this paper, which obtains the area GNSS auxiliary information (AGAI) with enough credibility and area attribute. Firstly, a mass of historical GNSS intermediate frequency data is divided into blocks to be acquired and tracked parallel getting massive pseudorange and navigation message (MPD). Then, the massive MPD are weighted and fused parallel by an appropriate weight matrix, which is determined by a priori weighting based on altitude angle and posterior weighting based on M-residuals variable components estimation. Lastly, the fused information is corresponded to the corresponding location coordinate, completing the parallel extraction of AGAI. The method implemented by parallel programming models MapReduce of Hadoop to guarantee a high efficiency. Experimental results show that the positioning and velocity error of GNSS receivers are reduced by 18.24% and 20.48% using AGAI instead of traditional auxiliary information, and the execution time of the method using MapReduce is reduced by 46.72%, so the proposed method is reliable and effective.
Effective identification of wireless channel in different scenarios or regions can solve the problems of multipath interference in process of wireless communication. In this paper, different characteristics of wireless channel are extracted based on the arrival time and received signal strength, such as the number of multipath, time delay and delay spread, to establish the feature vector set of wireless channel which is used to train backpropagation (BP) neural network to identify different wireless channels. Experimental results show that the proposed algorithm can accurately identify different wireless channels, and the accuracy can reach 97.59%.
The Ordering decisions made by decision-makers are affected by their personality characteristics and uncertain market environment, and have always been focused on by scholars. This study used MBTI to classify the personality characteristics of decision-makers, and the E-prime software combined with the newsvendor model is used to simulate the ordering process of decision-makers. The experimental data are subjected to GMM regression analysis to explore the influence of the decision-maker's personality characteristics on the ordering decision. The results show that the previous market demand has a significant impact on decision-makers in the uncertain market environment, indicating that participants have a significant anchoring effect. The order quantity of extroverted participants higher than that of introverted participants. With the increase of trials, the order quantity of the decision-maker has the obvious pull-tocenter effect and stabilizes at a subjective level.
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