Algorithm Research Of Track Fusion Based On QAR Data

With the rapid growth of civil aviation of China, the lack of airspace resource is becoming the most critical restrictive factors for air transportation. The airspace for aviation is becoming more and more crowded, leading increasingly more flight delays, waste passengers' much more time and causes large losses to both airlines and passengers. What's worse, the increased fuel consumption due to traffic delays and diversions is making the acute aeronautical environment issues more serious. Therefore, improving air traffic flow management and improving utilization of airspace resources have become hot topics concerned and have been studied by both the industry and airspace experts.Inspired by the ideas of reducing vertical separation minimum(RVSM) standard and increasing airspace capacity, this paper analyzes the civil aviation aircraft distribution in cruising level space, presenting a novel estimating method under character of panel data for cruising altitude density.Firstly, according to our study intent and content as well as the idea of information fusion, improve the common framework model of track fusion, and thus establish our framework model of QAR track fusion.Second, according to the framework model of QAR track fusion, do successive temporal-spatial alignment and condense-forged temporal-spatial alignment respectively, and thus calculate each track's altitude values of its main cruise phase relative to the lower boundary of respective cruising flight level.Then, both under the successive temporal-spatial alignment scheme and condense-forged temporal-spatial alignment scheme respectively, estimate the probability density of the relative altitude values using kernel density estimate method, with the least square cross-validation method choosing asymptotically optimal smoothing parameters.Finally, both under the successive temporal-spatial alignment scheme and condense-forged temporal-spatial alignment scheme respectively, according to the probability density data of the relative altitudes, estimate the confident track aisles of our civil tracks' main cruising relative altitudes under different confidence degree.