The increasing spreading of small commercial Unmanned Aerial Vehicles (UAVs,
aka drones) presents serious threats for critical areas such as airports, power
plants, governmental and military facilities. In fact, such UAVs can easily
disturb or jam radio communications, collide with other flying objects, perform
espionage activity, and carry offensive payloads, e.g., weapons or explosives.
A central problem when designing surveillance solutions for the localization of
unauthorized UAVs in critical areas is to decide how many triangulating sensors
to use, and where to deploy them to optimise both coverage and cost
effectiveness.
In this article, we compute deployments of triangulating sensors for UAV
localization, optimizing a given blend of metrics, namely: coverage under
multiple sensing quality levels, cost-effectiveness, fault-tolerance. We focus
on large, complex 3D regions, which exhibit obstacles (e.g., buildings),
varying terrain elevation, different coverage priorities, constraints on
possible sensors placement. Our novel approach relies on computational geometry
and statistical model checking, and enables the effective use of off-the-shelf
AI-based black-box optimizers. Moreover, our method allows us to compute a
closed-form, analytical representation of the region uncovered by a sensor
deployment, which provides the means for rigorous, formal certification of the
quality of the latter.
We show the practical feasibility of our approach by computing optimal sensor
deployments for UAV localization in two large, complex 3D critical regions, the
Rome Leonardo Da Vinci International Airport (FCO) and the Vienna International
Center (VIC), using NOMAD as our state-of-the-art underlying optimization
engine. Results show that we can compute optimal sensor deployments within a
few hours on a standard workstation and within minutes on a small parallel
infrastructure.

对于无人机在关键领域的威胁，通过计算三角传感器的部署，优化覆盖范围、成本效益和容错性，可实现对无人机的有效定位和监测。

通过计算几何优化无人机传感器在关键区域的容错性和质量保证定位部署

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV  Localization in Critical Areas via Computational Geometry

Unmanned Aerial Vehicle (UAV) localization capability is critical in a Global
Navigation Satellite System (GNSS) denial environment. The aim of this paper is
to investigate the problem of locating the UAV itself through a purely visual
approach. This task mainly refers to: matching the corresponding geo-tagged
satellite images through the images acquired by the camera when the UAV does
not acquire GNSS signals, where the satellite images are the bridge between the
UAV images and the location information. However, the sampling points of
previous cross-view datasets based on UAVs are discrete in spatial distribution
and the inter-class relationships are not established. In the actual process of
UAV-localization, the inter-class feature similarity of the proximity position
distribution should be small due to the continuity of UAV movement in space. In
view of this, this paper has reformulated an intensive dataset for UAV
positioning tasks, which is named DenseUAV, aiming to solve the problems caused
by spatial distance and scale transformation in practical application
scenarios, so as to achieve high-precision UAV-localization in GNSS denial
environment. In addition, a new continuum-type evaluation metric named SDM is
designed to evaluate the accuracy of model matching by exploiting the continuum
of UAVs in space. Specifically, with the ideas of siamese networks and metric
learning, a transformer-based baseline was constructed to enhance the capture
of spatially subtle features. Ultimately, a neighbor-search post-processing
strategy was proposed to solve the problem of large distance localisation bias.

该研究通过视觉方法探究无人机在全球导航卫星系统拒绝服务环境下如何准确定位自身，为此构建了基于转换器的基线并提出了新的连续型评估指标 SDM，以解决现实应用场景中空间距离和尺度变换所带来的问题。同时，利用孪生网络和度量学习构建，设计邻居搜索后处理策略解决大距离定位偏差问题。