Open Access

ARTICLE

Intent Inference Based Trajectory Prediction and Smooth for UAS in Low-Altitude Airspace with Geofence

by Qixi Fu, Xiaolong Liang, Jiaqiang Zhang, Xiangyu Fan

1 National Key Laboratory of Air Traffic Collision Prevention, Air Force Engineering University, Xi’an, 710051, China.
2 Shaanxi Province Laboratory of Meta-Synthesis for Electronic and Information System, Xi’an, 710051, China.

* Corresponding Author: Jiaqiang Zhang. Email: .

Computers, Materials & Continua 2020, 63(1), 417-444. https://doi.org/10.32604/cmc.2020.07044

Received 27 April 2019; Accepted 11 July 2019; Issue published 30 March 2020

Abstract

In order to meet the higher accuracy requirement of trajectory prediction for Unmanned Aircraft System (UAS) in Unmanned Aircraft System Traffic Management (UTM), an Intent Based Trajectory Prediction and Smooth Based on Constrained State-dependent-transition Hybrid Estimation (CSDTHE-IBTPS) algorithm is proposed. Firstly, an intent inference method of UAS is constructed based on the information of ADS-B and geofence system. Moreover, a geofence layering algorithm is proposed. Secondly, the Flight Mode Change Points (FMCP) are used to define the relevant mode transition parameters and design the guard conditions, so as to generate the mode transition probability matrix and establish the continuous state-dependent-transition model. After that, the constrained Kalman filter (CKF) is applied to improve State-dependent-transition Hybrid Estimation (SDTHE) algorithm by applying equality constraint to the velocity of UAS in the straight phase and turning phase, respectively, and thus the constrained state-dependent-transition hybrid estimation (CSDTHE) algorithm is constructed. Finally, the results of intent inference and hybrid estimation are used to make trajectory prediction. Furthermore, each flight segment of trajectory is smoothed respectively by Rauch-Tung-Striebel (RTS) backward smooth method using the proposed CSDTHE-RTS algorithm, so as to obtain more accurate trajectory prediction results. The simulation shows that the proposed algorithm can reduce the errors of trajectory prediction and the time delay of intent inference.

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Keywords

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