On ADS-B Sensor Placement for Secure Wide-Area Multilateration

Abstract

As automatic dependent surveillance–broadcast (ADS-B) becomes more prevalent, the placement of on-ground sensors is vital for Air Traffic Control (ATC) to control the airspace. However, the current sensors are placed in an unstructured way that keeps some areas without coverage, and others are over-densified by sensors. Therefore, areas with coverage anomalies may cause issues that inhibit accurate ADS-B verifications as well as the availability of ADS-B altogether. In this paper, we tackle the ADS-B-specific optimal sensor placement (OSP) problem. Of importance are both the optimal coverage and the secure and accurate verification of received ADS-B messages. Specifically, we take into account the following objectives. First, we determine the minimum required number of sensors in order to cover a certain area like Europe. Second, we produce a better placement of the current sensors with respect to the security and accuracy of geometric dilution of precision (GDOP). Finally, we calculate how far the current sensor setup is from our derived optimal solution as well as the cost to reach the optimality. Our experiments show that the ideal fitness score for solving the OSP is below 0.1, meaning that the mean squared error (MSE) of the required and achieved GDOPs is significantly small, thus accomplishing a near-optimal setup.

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

“…Ref. [45] proposed a solution for determining the optimal placement of ADS-B receivers on the ground. In [45], a genetic algorithm was utilized to determine the optimal placement of ADS-B receivers on the ground in the vicinity of Frankfurt Airport in Germany.…”

“…[45] proposed a solution for determining the optimal placement of ADS-B receivers on the ground. In [45], a genetic algorithm was utilized to determine the optimal placement of ADS-B receivers on the ground in the vicinity of Frankfurt Airport in Germany. The authors first identified the required number of sensors to ensure adequate coverage of the small geographical area.…”

On-Line Optimal Ranging Sensor Deployment for Robotic Exploration

“…Notice that this marks a striking difference with respect to the known literature. Indeed, existing solutions, e.g., [29], [30], cannot change the nodes infrastructure at runtime based on robot needs nor can adequately leverage the ratio of information versus uncertainty that a new added anchor injects in the multilateration problem. Moreover, our solution is robotcentered: existing solutions usually try to optimize the entire region as a whole, with evident computational burden issues and difficulties in unknown or partially known environments, while our solution is extremely light in terms of computing power and can be computed onboard the vehicle while it explores the (possibly unknown) environments.…”

On-line Optimal Ranging Sensor Deployment for Robotic Exploration

“…Notice that this marks a striking difference with respect to the known literature. Indeed, existing solutions, e.g., [28], [29], cannot change the nodes infrastructure at runtime based on robot needs nor can adequately leverage the ratio of information versus uncertainty that a new added anchor injects in the multilateration problem. Moreover, our solution is robotcentered: existing solutions usually try to optimize the entire region as a whole, with evident computational burden issues and difficulties in unknown or partially known environments, while our solution is extremely light in terms of computing power and can be computed onboard the vehicle while it explores the (possibly unknown) environments.…”