Understanding drone sensor data for autonomous flight

Flare Bright是世界领先的航空航天无人机和高级空气机动(AAM)软件公司，利用先进的数字双胞胎(DT)开发机器学习(ML)增强的软件生态系统，将实现安全、可靠、自主的超视线(BVLOS)飞行。Flare Bright的核心ML-DT软件生态系统正在产生两种具有重要商业意义的产品，这两种产品都具有巨大的潜在市场，被认为是无人机技术实现预期经济增长的关键推动因素。首先，Flare Bright开发了一种高分辨率的无传感器风速测量能力，使用无人机本身作为传感器。其他解决方案，如皮托探头，精确度较低，响应速度较慢，从尺寸和重量的角度来看也不实用。这种独特的能力已经在加的夫机场的UKRI的未来飞行项目SafeZone中得到了展示，所实现的准确性已经在ICAS 2022的技术论文中进行了仔细的审查。这种测量能力的准确性和分辨率使其成为安全飞行的关键推动因素，允许更灵敏的飞行控制调谐并生成风数据，从而能够在城市或复杂的流动环境中定义安全运行极限，在这些环境中，飞机经历的风条件与目前可用的平均预报数据有很大不同。其次，Flare Bright开发了一种ML增强的惯性导航系统(INS)，在GPS被拒绝或损坏时，该系统使用现有的机载小型且廉价的传感器提供准确的短期导航。现有的高精度惯导系统体积大、重量重、耗电量大，不适合未来重量和功率至关重要的无人机和电动飞机。因此，我们的能力是BVLOS的关键，因为GPS的丢失已经经常阻碍操作，并且在未来，由于更多的技术使用和故意干扰信号而导致的电磁干扰可能会更频繁地发生。Flare Bright已经开发出一项正在申请专利的技术，该技术可以使用嵌入的DT来估计飞机上的气流，如果知道地面速度，可以从DT估计风速，反之亦然。然而，当应用于广泛的飞行包线时，这种技术会受到具有多个潜在解决方案的复杂的一对多问题的阻碍。使用尖端的数学技术，该项目旨在确定是否可以确定或计算出单一解决方案比其他解决方案更有可能，从而显著提高FLARE Bright的两个核心产品的影响和生存能力。

Flare Bright is a world-leading aerospace drone and Advanced Air Mobility (AAM) software company developing a Machine Learning (ML) boosted software ecosystem using advanced Digital Twins (DT) that will enable safe, reliable, autonomous Beyond Visual Line Of Sight (BVLOS) flight.Flare Bright's core ML-DT software ecosystem is leading to two commercially important products, both with huge potential markets and identified as critical enablers to drone technology achieving the economic growth predicted.Firstly, Flare Bright has developed a high resolution 'sensorless' wind measurement capability that uses the drone itself as the sensor. Other solutions, such as pitot probes, are less accurate, slower in response and not practical from a size and weight perspective. This unique capability has been demonstrated at Cardiff Airport, within UKRI's Future Flight project SafeZone, and the accuracy achieved has been scrutinised in an ICAS 2022 technical paper. The accuracy and resolution of this measurement capability makes it a key enabler in safer flight, allowing more responsive flight control tunes and generating wind data that would enable safe operating limits to be defined in urban or complex flow environments where wind conditions experienced by aircraft are significantly different from averaged forecast data that is currently available.Secondly, Flare Bright has developed an ML-boosted Inertial Navigation System (INS) that provides accurate short-term navigation when GPS is denied or corrupted using existing, small and inexpensive, on-aircraft sensors. Existing high accuracy INS are large, heavy and consume lots of power, making them unsuitable for drones and electric aircraft of the future, where weight and power are critical. Our capability is therefore key to BVLOS as GPS drop-outs already regularly hinder operation, and, in future, are likely to occur more frequently due to electromagnetic interference from increased technology use and deliberate jamming disrupting signals.This project aims to address a challenge underpinning both these products. Flare Bright has developed a patent pending technique that enables airflow over the aircraft to be estimated using an embedded DT, from which either wind speed can be estimated if ground velocity is known or vice versa. However, when applied across a wide flight envelope, this technique is hindered by a complex "one-to-many" problem with multiple potential solutions. Using cutting edge mathematical techniques, this project aims to identify if a single solution may be ascertained or calculated to be more likely than the others, thereby significantly increasing the impact and viability of both Flare Bright's core products.