Feasibility and accuracy of Received Signal Strength-based Multilateration for aircraft localization using crowdsourced data

Feasibility and accuracy of Received Signal Strength-based Multilateration for aircraft localization using crowdsourced data

Martjanova, Veronika (TU Delft Aerospace Engineering)

Sun, Junzi (mentor)
Hoekstra, J.M. (graduation committee)

Delft University of Technology

Aerospace Engineering

2024-04-12

To verify the aircraft position provided by Automatic Dependent Surveillance-Broadcast (ADS-B)
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained.

http://resolver.tudelft.nl/uuid:94205e7b-a9ab-47ac-a3c6-a71aec2aa516

Student theses

master thesis

© 2024 Veronika Martjanova