Quantum inertial sensors on a moving platform

全球导航卫星系统（GNSS），如伽利略（Galileo）、格洛纳斯（GLONASS）、北斗（BeiDou）或全球定位系统（GPS），是世界上广泛使用的既定导航技术。然而，GNSS不能在地下或水下工作，很容易受到天气、恶意干扰、欺骗甚至完全拒绝服务的干扰。显然，当作为主要的导航方法使用时，系统故障可能会导致灾难性的后果。惯性导航系统（INS）为GNSS提供了一个有吸引力的替代方案。它们不需要发送或接收卫星信号或任何外部参考，因此可以部署在任何环境中。INS的工作原理是测量车辆的加速度和旋转，然后使用一组复杂的方程计算其相对位置变化，这些方程依赖于对多个惯性信号对时间的两次积分。最先进的惯性传感器在测量偏差中存在不稳定漂移，这些漂移随机变化，使系统的精度误差越来越大。这些不断增长的位置误差限制了这些设备可以使用的范围和时间。最好的军用级INS（例如iXblue-Marins M8）的定位精度高达每96小时1海里。这意味着在大约2小时后，定位精度将不再与标准GNSS系统（~1英尺）相媲美，这限制了比这更长的时间尺度上的导航。量子惯性传感器（QIS）为克服由偏置漂移引起的性能限制提供了一条令人兴奋的途径。QIS利用物质波干涉测量技术，利用超冷的自由原子云来测量车辆的加速度和旋转。这些自由原子构成了一个几乎完美的惯性观察者，它不需要校准，不受老化的影响，也可能没有漂移。QIS已经在实验室环境中显示出巨大的希望，显示出超过传统方法的灵敏度和稳定性。基于传统惯性传感器（因其高动态范围和带宽而受到青睐）和低偏置漂移量子点的混合导航系统已经被提出。帝国理工学院的CCM导航团队已经实现了高性能实验室规模的量子加速度计，这引起了一系列热衷于开发量子导航技术的工业部门的极大兴趣。我博士的重点将是把量子加速度计从实验室规模的研究转化为一种可以在移动平台上部署和测试的技术。我要做一个新的可移动量子加速度计，并在移动平台上进行测试。我将首先建立一个新的设备，它将M平方激光系统、特高压科学室和控制系统结合在一个19英寸的机架上。该系统将能够使用电池供电，并将设计为便于运输。我将首先在实验室环境中演示这个新系统的性能，并将其与现有的实验室规模系统进行比较。比较两个相似的系统将使我能够第一次评估我们设备的偏置漂移，这将使我能够评估该技术可以实现的长期性能。然后，我将在移动平台上试用我的新系统，并将其在现场的性能与实验室系统进行比较。

Global Navigation Satellite Systems (GNSS), such as Galileo, GLONASS, BeiDou, or the Global Positioning System (GPS), are established navigation technologies used extensively the world over. However, GNSS does not work underground or underwater and it is vulnerable to disruption by the weather, or through malicious jamming, spoofing and even total denial of service. Obviously, when used as a primary method of navigation, having the system fail could lead to disastrous consequences. Inertial Navigation Systems (INS) offer an attractive alternative to GNSS. They do not need to send or receive satellite signals or any external references, and as a result, can be deployed in any environment. INS's work by measuring the acceleration and rotation of a vehicle and then calculating its relative change in position using a complex set of equations, which rely on integrating multiple inertial signals twice with respect to time. State-of-the-art inertial sensors suffer from unstable drifts in their measurement bias, which randomly vary, giving a growing error in the accuracy of the system. These growing position errors limit the range and amount of time that these devices can be used for. The best, military-grade INS's (e.g. iXblue-Marins M8) have position accuracy of up to 1 nautical mile per 96 hours. This means that after around 2 hours, the position accuracy will no longer be comparable to that of standard GNSS systems (~1ft), which inhibits navigation on timescales much longer than this.Quantum Inertial Sensors (QIS) offer an exciting route to overcome the performance limitations that arise from bias drift. QIS's exploit matter wave interferometry using ultra-cold clouds of free atoms to measure the acceleration and rotation of a vehicle. These free atoms constitute an almost perfect inertial observer, which does not require calibration, does not suffer from aging, and has potentially no drift. QIS's have already shown great promise in a laboratory setting, demonstrating sensitivities and stabilities exceeding those of conventional methods. Navigation systems based on a hybridisation of classical inertial sensors (favoured for their high dynamic range and bandwidth) with low-bias-drift QISs have already been proposed. The CCM navigation team at Imperial has already realised high performance laboratory scale quantum accelerometers, which has stimulated significant interest from a range of industrial sectors, who are keen to develop quantum navigation technologies. The focus of my PhD will be to translate quantum accelerometers from laboratory scale research into a technology that can be deployed, and tested, on a moving platform. I am going build a new transportable quantum accelerometer and test it on a moving platform. I will begin by building a new apparatus that combines an M Squared laser system, UHV science chamber and control system in a single 19" rack. The system will be capable of running on battery power, and will be designed for ease of transportation. I will first demonstrate the performance of this new system in a laboratory environment and compare it with existing lab scale systems. Comparing two similar systems will enable me to assess the bias drift of our devices for the first time, which will enable me to assess the long-term performance that this technology can achieve. I will then trial my new system on a moving platform and compare its performance in the field with a laboratory system.