This paper introduces the background, aim, experimental design, configuration and data processing for an airborne test flight of the HY-2 Microwave scatterometer(HSCAT). The aim was to evaluate HSCAT performance and a...This paper introduces the background, aim, experimental design, configuration and data processing for an airborne test flight of the HY-2 Microwave scatterometer(HSCAT). The aim was to evaluate HSCAT performance and a developed data processing algorithm for the HSCAT before launch. There were three test flights of the scatterometer, on January 15, 18 and 22, 2010, over the South China Sea near Lingshui, Hainan. The test flights successfully generated simultaneous airborne scatterometer normalized radar cross section(NRCS), ASCAT wind, and ship-borne-measured wind datasets, which were used to analyze HSCAT performance. Azimuthal dependence of the NRCS relative to the wind direction was nearly cos(2w), with NRCS minima at crosswind directions, and maxima near upwind and downwind. The NRCS also showed a small difference between upwind and downwind directions, with upwind crosssections generally larger than those downwind. The dependence of airborne scatterometer NRCS on wind direction and speed showed favorable consistency with the NASA scatterometer geophysical model function(NSCAT GMF), indicating satisfactory HSCAT performance.展开更多
为了提高无人机俯仰角故障数据处理和预测的精确性和可靠性,避免增加无人机试飞成本,利用长短期记忆网络(long short term memory,LSTM)、注意力机制+LSTM模型和差分自回归移动平均模型(autoregressive integrated moving average model...为了提高无人机俯仰角故障数据处理和预测的精确性和可靠性,避免增加无人机试飞成本,利用长短期记忆网络(long short term memory,LSTM)、注意力机制+LSTM模型和差分自回归移动平均模型(autoregressive integrated moving average model,ARIMA)模型预测无人机试飞俯仰角故障数据。结果表明,ARIMA预测结果:平均绝对误差(mean absolute error,MAE)为0.35,均方根误差(root mean square error,RMSE)为0.73,平均绝对百分比误差(mean absolute percentage error,MAPE)为23.80%;LSTM模型预测结果:MAE=0.49,RMSE=0.74,MAPE=45.20%;注意力机制+LSTM模型预测结果:MAE=0.17,RMSE=0.53,MAPE=18.93%。可见注意力机制+LSTM模型比其余两种模型更适合于试飞俯仰角的数据预测,以上3种方法对无人机故障数据预测都具有实际意义,有效的预测可以推进自动飞行器和移动机器人的异常检测或外国直接投资研究的最新进展,以进一步提高自动和远程飞行操作的安全性。展开更多
基金Supported by the National Natural Science Foundation of China(No.41106152)the National Science and Technology Support Program of China(No.2013BAD13B01)+3 种基金the National High Technology Research and Development Program of China(863 Program)(No.2013AA09A505)the International Science&Technology Cooperation Program of China(No.2011DFA22260)the National High Technology Industrialization Project(No.[2012]2083)the Marine Public Projects of China(Nos.201105032,201305032,201105002-07)
文摘This paper introduces the background, aim, experimental design, configuration and data processing for an airborne test flight of the HY-2 Microwave scatterometer(HSCAT). The aim was to evaluate HSCAT performance and a developed data processing algorithm for the HSCAT before launch. There were three test flights of the scatterometer, on January 15, 18 and 22, 2010, over the South China Sea near Lingshui, Hainan. The test flights successfully generated simultaneous airborne scatterometer normalized radar cross section(NRCS), ASCAT wind, and ship-borne-measured wind datasets, which were used to analyze HSCAT performance. Azimuthal dependence of the NRCS relative to the wind direction was nearly cos(2w), with NRCS minima at crosswind directions, and maxima near upwind and downwind. The NRCS also showed a small difference between upwind and downwind directions, with upwind crosssections generally larger than those downwind. The dependence of airborne scatterometer NRCS on wind direction and speed showed favorable consistency with the NASA scatterometer geophysical model function(NSCAT GMF), indicating satisfactory HSCAT performance.
文摘为了提高无人机俯仰角故障数据处理和预测的精确性和可靠性,避免增加无人机试飞成本,利用长短期记忆网络(long short term memory,LSTM)、注意力机制+LSTM模型和差分自回归移动平均模型(autoregressive integrated moving average model,ARIMA)模型预测无人机试飞俯仰角故障数据。结果表明,ARIMA预测结果:平均绝对误差(mean absolute error,MAE)为0.35,均方根误差(root mean square error,RMSE)为0.73,平均绝对百分比误差(mean absolute percentage error,MAPE)为23.80%;LSTM模型预测结果:MAE=0.49,RMSE=0.74,MAPE=45.20%;注意力机制+LSTM模型预测结果:MAE=0.17,RMSE=0.53,MAPE=18.93%。可见注意力机制+LSTM模型比其余两种模型更适合于试飞俯仰角的数据预测,以上3种方法对无人机故障数据预测都具有实际意义,有效的预测可以推进自动飞行器和移动机器人的异常检测或外国直接投资研究的最新进展,以进一步提高自动和远程飞行操作的安全性。
