Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

Abstract

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-theart approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-theart methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

“…It is also straightforward to see that the requirements listed in Table 4 can be perceived as boundaries for the optimization problems that have the overlapping metrics from Table 2. High range for mobility support C 0-1000 km/h UAV airspeed estimation [67]; survey of sensor fusion techniques for all-speed autonomous vehicles [68] High positioning accuracy P, S 0.1-10 m 5G-based positioning [69]; positioning metrics in Cellular vehicle-to-everything (C-V2X) communications [70]; precision needed for fully autonomous driving [12] High throughputs C 0.1-50,000 Gbps Air-to-ground (A2G) communications for flying vehicles [71]; high throughputs through cognitive internet of vehicles [72] Low latencies C, P, S 1-30 ms LEO latencies compared with terrestrial network latencies [73] High Coverage C, P, S >90% Global coverage design [74,75]; CubeSat constellation design for IoT [76];…”