Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility

TL;DR

This work tackles the design of an AAM surveillance network and a cloud-based clearinghouse (LASIC) to support safe, secure, and efficient low-altitude operations. It introduces SAND, a binary integer linear programming framework that optimizes sensor placement with terrain-aware detection to achieve full coverage at minimum cost, considering six sensor types and two network configurations. A cloud-based LASIC framework is proposed to ingest, store, and share surveillance data with stakeholders, and a cost-benefit analysis (including NPV and BEP) is performed for Ohio under parameter sensitivity. The results show that heterogeneous sensor networks yield lower total sensor costs and higher NPVs than homogeneous ones, with RF (for both cooperative and non-cooperative aircraft) and ADS-B/Remote ID (for cooperative-only) being particularly cost-effective options. The study provides policy-relevant insights for AAM surveillance investments and points to future work on obstructions, trajectory-aware sensor placement, weather effects, and broader-state applicability.

Abstract

To ensure safe, secure, and efficient advanced air mobility (AAM) operations, an AAM surveillance network is needed to detect and track AAM traffic. Additionally, a cloud-based surveillance data collection, monitoring, and distribution center is needed, where AAM operators and service suppliers, law enforcement agencies, correctional facilities and municipalities can subscribe to for receiving relevant AAM traffic data to plan and monitor AAM operations. In this work, we develop an optimization model to design a surveillance sensor network for AAM that minimizes total sensor cost while providing full coverage in the desired region of operation, considering terrain types of that region, terrain-based sensor detection probabilities, and meeting the minimum detection probability requirement. Moreover, we present a framework for low altitude surveillance information clearinghouse (LASIC), connected to the optimized AAM surveillance network for receiving live surveillance feed. Additionally, we conduct a cost-benefit analysis of the AAM surveillance network and LASIC to justify investment in it. We examine six potential types of AAM sensors and homogeneous and heterogeneous network types. Our analysis reveals the sensor types that are the most profitable options for detecting cooperative and non-cooperative aircraft. According to the findings, heterogeneous networks are more cost-effective than homogeneous sensor networks. Based on the sensitivity analysis, changes in parameters such as subscription fees, number of subscribers, sensor detection probabilities, and minimum required detection probability significantly impact the surveillance network design and cost benefit analysis.

Figures (14)

• Figure S1: Sensors of different types: (a) ADS-B receiver ads-b, (b) radio frequency sensor RF360, (c) remote ID receiver remote, (d) radar echo, and (e) acoustic sensor aco.
• Figure S2: Overview of LASIC framework and associated cost and benefit factors.
• Figure S3: A flow chart illustrating the steps associated with AAM surveillance network designing and cost-benefit analysis of AAM surveillance network and LASIC.
• Figure S4: A flowchart showing the connections of the cloud components of LASIC.
• Figure S5: A heatmap of probability of detection of a radar based on terrain types of 3240 blocks in Dayton.