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Paper 15 -- Improving Trajectory Forecasting Through Adaptive Filtering Techniques

An adaptive Kalman filter is presented to provide improvements in short-term (5 to 20 minutes) trajectory forecasting. The approach treats the wind uncertainty as a random process with an autocorrelation function of known form. The filter estimates the current contribution of the wind uncertainty and estimates the future position given that future wind uncertainty will be correlated with the current wind uncertainty. The adaptation algorithm obtains the model parameters by looking at recorded data and can be applied in a real-time setting. Since the model adapts to measured data, the model does not require data exchange and should handle non-stationary processes that vary slowly.A simulation was developed of the aircraft longitudinal equations of motion incorporating the effect of wind uncertainty. Simulation results show a potential reduction of trajectory forecasting errors between 70% at 5-minute look-ahead to 40% at 20-minute look-ahead. A sensitivity analysis revealed that the full longitudinal aircraft model is required for improvements below 5 minutes look-ahead, but a simplified model can be used for larger look-ahead times.The filter was applied to a sample of radar data and reduced the trajectory forecasting uncertainty from 1.4 to 1.0 nautical mile at a 10-minute look-ahead. As an example, the impact of such a reduction on conflict probe performance was analyzed. Both missed and false alerts could be reduced and the buffer size could also be reduced to maintain a constant missed alert level.
Theme: Decision Support
Posted by: Stephane Mondoloni / Other authors: Diana Liang
Note: Unset Received On Dec 20, 2005

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