Mosavi M R
Department of Electrical Engineering, Behshahr University of Science and Technology, Behshahr, 48518-78413, Iran.
Int J Neural Syst. 2007 Oct;17(5):383-93. doi: 10.1142/S0129065707001226.
The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.
全球定位系统（GPS）是一个卫星网络，其最初目的是为军事用户提供精确的导航、制导和时间传递。在过去十年中，民用GPS应用也迅速同步增长，包括农业、采矿、测量、航海和户外休闲等领域。其中最重要的民用应用之一是商业航空。在选择性可用性（SA）关闭前后，独立民用用户的定位精度分别为100米和300纳秒、25米和200纳秒。在某些应用中，需要高精度。本文提出了五种神经网络（NN），用于对GPS接收机定时数据进行可接受的降噪处理。本文使用实际数据收集来评估这些方法的性能。为此设计并实现了一个实验测试装置。从粗捕获（C/A）码单频GPS接收机获得的实验结果有力地支持了这些方法实现高精度定时的潜力。所获得结果的质量非常好，在有SA和无SA的情况下，GPS定时均方根误差分别降至小于120纳秒和40纳秒。
