Object Detection, Location and Identification at Radio Frequencies in the Near Field

英国最近发生的事件（例如2019年伦敦桥袭击事件，造成2人死亡，以及与恐怖有关的斯特里汉姆事件，有2人被刺伤）突出表明，需要改进对包括刀、枪和简易爆炸装置在内的威胁的早期对峙检测。为了能够在距离传感器10米左右的距离内描述和识别这些小物体，使用电磁场测量需要在300MHz到12GHz范围内的频率，在这个范围内波传播效应很重要。这个范围内的频率传统上也被用于雷达（无线电探测和测距），用于距离传感器很远的大型物体（如船只、飞机和空中威胁），使用远场散射模式。然而，虽然雷达传统上与远场物体的定位和探测有关，但雷达也可用于近场物体的分类（例如自动驾驶汽车，停车传感器和用于寻找地雷和未爆炸弹药的探地雷达（GPR），考古搜索和建筑行业公用事业的位置）。此外，鉴于b谷歌、特斯拉、优步等许多公司开发的自动驾驶汽车，以及自动驾驶制造中的相关应用，人们对改进物体定位也有相当大的兴趣。在所有这些应用中，也有相当大的需求来改进小物体的特征和识别，这些小物体不受可以被电磁场穿透的边界（例如墙壁、地面、衣服、烟雾、雾或云）的阻碍。该提案旨在利用300MHz至12GHz范围内的电磁频率改进近场小物体的表征、分类和识别，从而产生新的数学结果、用于物体识别的统计计算工具和电磁传感器的设计建议。我们的假设是，物体的高张量描述与概率分类方法相结合，提供了一种有效的方法来识别小物体，使用电磁场测量定位远离目标，但在近场，在波传播频率。为了验证我们的假设，我们将推导出新的渐近展开，从而根据新的张量描述得出新的对象特征。我们将使用这些张量研究对象的新的最小收缩表示，并理解可以从这些最小表示中获得的关于对象的信息。我们将开发新的计算工具来计算这些特征和分类器，这些特征和分类器建立在张量系数库上，以便从实际测量中做出对象预测。

Recent events in the UK (eg the 2019 London Bridge Attack, in which 2 people were killed, and the Terror Related Streatham Incident, where 2 peoplewere stabbed) have highlighted the need for improved early stand-off detection of threats, which include knives, guns and improvised explosive devices. To be able to characterise and identify these small objects at stand-off distances in the order of 10s metres from the sensor using electromagnetic field measurements requires frequencies in the 300MHz to 12GHz range, where wave propagation effects are important. Frequencies in this range have also been traditionally been used in radar (radio detection and ranging) for large objects (eg ships, aircraft and air borne threats) over much larger distances from the sensor using far field scattering pattens. However, while radar is traditionally associated with the positioning and detection of objects in the far field, radar can also used be for the classification of objects in the near field (such as in autonomous vehicles, parking sensors, and ground penetrating radar (GPR) for finding landmines and unexploded ordnance, archaeological searches and the location of utilities for the construction industry). Furthermore, there is also considerable interest in improved object positioning given the development of autonomous vehicles by Google, Tesla, Uber and many others as well as related applications in autonomous manufacturing. In all these applications there is also considerable demand to improve the characterisation and identification of small objects that are not impeded by boundaries that can be penetrated by electromagnetic fields (eg walls, ground, clothing, smoke, fog or clouds). This proposal is aimed at improving the characterisation, classification and identification of small objects in the near field using electromagnetic frequencies in the range 300MHz to 12GHz leading to new mathematical results, statistical computing tools for object identification and design recommendations for electromagnetic sensors. Our hypothesis is that a higher tensor description of an object combined with a probabilistic classification approach provides an effective means of identifying small objects using electromagnetic field measurements positioned away from the target, but in the near field, at wave propagation frequencies. To test our hypothesis, we will derive new asymptotic expansions, which lead to new object characterisations in terms of new tensor descriptions. We will investigate new minimal contracted representations of objects using these tensors and understand the information about an object that can be obtained from these minimal representations. We will develop new computational tools for computing these characterisations and classifiers that build on a library of tensor coefficients to make object predictions from practical measurements.