电讯技术
電訊技術
전신기술
TELECOMMUNICATIONS ENGINEERING
2014年
2期
156-162
,共7页
广播式自动相关监视( ADS-B)%意图信息%航迹预测%残差均值交互式多模型( RMIMM)
廣播式自動相關鑑視( ADS-B)%意圖信息%航跡預測%殘差均值交互式多模型( RMIMM)
엄파식자동상관감시( ADS-B)%의도신식%항적예측%잔차균치교호식다모형( RMIMM)
automatic dependent surveillance-broadcast ( ADS-B)%intent information%trajectory predic-tion%residual-mean interacting multiple model ( RMIMM)
在飞行冲突探测和恶劣天气情况下,单纯利用跟踪滤波算法无法准确判断目标飞机未来的飞行动态。为了有效提高航迹预测精度,针对广播式自动相关监视( ADS-B )信息中意图信息对航迹的影响,提出了一种基于ADS-B意图信息的航迹预测改进算法。该改进算法先通过残差均值交互式多模型( RMIMM)算法,推算出目标飞机的飞行状态参数和飞机运动模式,再结合天气状况、飞行计划和空中交通管制规章进行意图估计,最后通过飞行状态参数、飞机运动模式以及意图信息共同预测目标飞机的航迹。运用蒙特卡洛方法进行仿真,结果表明,与现有算法相比,该改进算法不仅能提供更加精确的航迹预测,而且降低了意图推断的时间延迟。
在飛行遲突探測和噁劣天氣情況下,單純利用跟蹤濾波算法無法準確判斷目標飛機未來的飛行動態。為瞭有效提高航跡預測精度,針對廣播式自動相關鑑視( ADS-B )信息中意圖信息對航跡的影響,提齣瞭一種基于ADS-B意圖信息的航跡預測改進算法。該改進算法先通過殘差均值交互式多模型( RMIMM)算法,推算齣目標飛機的飛行狀態參數和飛機運動模式,再結閤天氣狀況、飛行計劃和空中交通管製規章進行意圖估計,最後通過飛行狀態參數、飛機運動模式以及意圖信息共同預測目標飛機的航跡。運用矇特卡洛方法進行倣真,結果錶明,與現有算法相比,該改進算法不僅能提供更加精確的航跡預測,而且降低瞭意圖推斷的時間延遲。
재비행충돌탐측화악렬천기정황하,단순이용근종려파산법무법준학판단목표비궤미래적비행동태。위료유효제고항적예측정도,침대엄파식자동상관감시( ADS-B )신식중의도신식대항적적영향,제출료일충기우ADS-B의도신식적항적예측개진산법。해개진산법선통과잔차균치교호식다모형( RMIMM)산법,추산출목표비궤적비행상태삼수화비궤운동모식,재결합천기상황、비행계화화공중교통관제규장진행의도고계,최후통과비행상태삼수、비궤운동모식이급의도신식공동예측목표비궤적항적。운용몽특잡락방법진행방진,결과표명,여현유산법상비,해개진산법불부능제공경가정학적항적예측,이차강저료의도추단적시간연지。
For the flight conflict detection and vile weather, the future flight dynamic of the target aircraft can not be accurately judged if tracking filtering algorithm is only used. In order to enhance effectively the trajectory prediction accuracy, an improved trajectory prediction algorithm is proposed based on Automatic Dependent Surveillance-Broadcast ( ADS-B) intent information which has the effect on aircraft trajectory. For this improved algorithm, the flight state parameters and aircraft movement modes for the target aircraft are first estimated by Residual-Mean Interacting Multiple Model ( RMIMM) algorithm, and then the target aircraft intent is estimated by the combination of the weather, flight plan, air traffic control regulations, as well as the flight state parameters and aircraft movement modes derived from RMIMM algorithm, and finally the target aircraft trajectory is predicted by the joint use of flight state parameters, aircraft movement modes and intent information. The Monte Carlo simulation is carried out, and the results show that not only can the more accurate trajectory prediction be achieved, but also intent inference time delay is reduced for this improved algorithm, compared with the existing algorithms.