ISBN 9783843948135

978-3-8439-4813-5, Reihe Elektrotechnik

Alexandra Filip-Dhaubhadel
L-Band Digital Aeronautical Communication System (LDACS)-Based Non-Cooperative Passive Multistatic Radar for Civil Aviation Surveillance

247 Seiten, Dissertation Technische Universität Chemnitz (2020), Softcover, A5

Zusammenfassung / Abstract

In this thesis, the feasibility of setting up a passive multistatic radar system while using the future L-band digital aeronautical communication system (LDACS) signals as signals of opportunity is investigated. The motivation for this work is the need to provide accurate backup non-cooperative surveillance services for civil aviation purposes. The aim of this work is therefore to assess the suitability and limitations of the LDACS communication system for radar purposes. Particularly critical in this respect are: i) the low transmit power of the LDACS system and ii) the fact that the LDACS signals are not optimized for radar purposes.

The envisioned LDACS surveillance parameters are first introduced and the radar coverage and detection performance are evaluated. The ambiguity function (AF) of the LDACS signal is then studied. The derived closed-form expressions enable a direct assessment of the signal ambiguities while also allowing for the multistatic radar AF evaluation. Furthermore, the modified Cramér-Rao bound on the joint estimation of target location and velocity is derived.

Motivated by the need to improve the signal-to-noise ratio (SNR) of the reflected LDACS signals, the last part of this thesis is dedicated to assessing the limits on the achievable processing gain. The integration time is generally limited by the target movement and its resulting migration out of a radar cell of interest. To enable a coherent SNR increase while using a long integration time, a joint range and Doppler migration compensation approach is first developed using state-of-the-art techniques. This approach is however shown to have limitations, in particular for low SNR and multi-target scenarios. To address these shortcomings, a novel super-resolution sparse Bayesian learning framework is developed and allows to perform target detection and parameter estimation jointly while accurately accounting for the underlying migration effects.