PhD thesis : Navigation using GNSS reflectometry as a passive radar to detect and to localize different targets

The proposed PhD thesis is in the field of Navigation using GNSS reflectometry as a passive radar to detect and to localize different targets.

There is a new emerging trend on the application of reflectometry in combination with GNSS signals to create a Passive Radar. A passive GNSS radar can be defined as a surveillance system created from a GNSS receiver by capturing and processing the GNSS signals scattered by the targets (desired to be detected/identified). The growing interest of passive radar lies on its passive property: no specific signal is transmitted by the radar (as it is the case for typical active radar) but rather existing positioning signal, in this case GNSS signals, are used. Passive radars are very relevant on several applications: for undetected survey around a receiver (in space or not), for UAV/plane monitoring in airport or in city, for space debris monitoring without perturbation of other space missions, and for the monitoring of boats not equipped with an AIS system (small boat) and for prohibited fishing detection.

The lack of emitted signal presents some advantages and some challenges. The main advantages are an a priori reduction of the system cost due to the lack of transmitter and to not create additional radio frequency interferences to the other systems (especially near and airport). The challenges are a low received signal power (due to the inherent low received power of a GNSS signal), the differentiation between Line-of-Sight (LOS) signals received directly by the GNSS from the satellites and the target scattered signals as well as with respect to multipath generated by other non-desired sources (buildings, trees, etc.).

The goal of this study is to develop passive radar techniques based on the processing of GNSS signals (MEO/LEO) for detecting/localizing targets close to a (reference) receiver. For a given receiver design and a given target (boat, plane, etc.), the detection/localization performance will be evaluated from simulations and real measurements. Moreover, the proposed technique performance will be compared to SAR (Synthetic-Aperture Radar) image techniques performance.

Additionally, this PhD thesis aims to estimate and to demonstrate the performance and limitation of GNSS Passive Radar system under at least two receivers’ assumption: a high-performance receiver equivalent to ground station deployment with a high number of antenna and no constraint on the computation effort, and a low performance receiver, equivalent to payload, with a limited array size.

The GNSS passive radar concept has been already demonstrated for UAV or boat detection in [1][2]. However, those techniques are usually based on a simple receiver with one antenna, and the use of only one signal / one satellite for the detection / localization of large targets.

In this PhD thesis, the main idea is to improve those techniques by introducing several improvements:

·        To use all the current, or future, GNSS signals (MEO satellite and also LEO-PNT satellites) available in all GNSS frequency bands (L1, L2, E5, E6, S bands, etc.). The use of more than one type of signal will improve the information diversity of the measurements and will allow the detection/localization even if one type of signal is not available or is corrupted. 

·        Advanced array antennas will be used to obtain polarization diversity and Angle of Arrival (AoA) information at the receiver. The polarization diversity will allow to differentiate between the LOS and the reflected signal (object to detect/localize) since the reflection on the object should change the GNSS signal’s polarization. Moreover, the AoA will also allow to different between the LOS signal (high elevation) from the reflected signal (low elevations).

·        State-of-the-art propagation models will also be used to facilitate the signal processing, and to help in the separation/identification of the reflection from a target, and the reflection from the local environment such as ground reflection (sea, floor, etc.), close hill, etc.

Moreover, advanced synchronization techniques must be used to acquire and track the target reflected signals. These reflected signals can have very low power, which is one of the main challenges of the passive radar. Therefore, long-coherent integration time techniques will also be used in this PhD.

One advanced synchronization technique that is expected to work very well in this application is the vector tracking technique; the main reason is that this technique allows the synchronization process of the signals to directly benefit in a jointly centralized way from the improvements 1), 2) and 3). Indeed, a vector tracking is a tracking solution where the generation of the local replica (to generate the signal’s carrier phase, carrier frequency or code delay) is driven directly from the position estimated by the navigation solution Kalman filter. Therefore, the addition of extra information (polarization, AoA) and more signals measurements (current of future GNSS) could be easily exploited and centralized in the Kalman filter. Additionally, the vector tracking technique has demonstrated to work well with low C/N_0; besides, the Kalman filter could be modified to consider the movement of the (reference) receiver (if any) or of a potential predicted trajectory of the target movement to increase the coherent integration time.

The focus of the PhD thesis will be centered around the synchronization algorithms of the target reflected signals and in the position estimation (if the vector tracking algorithm includes the target position estimation). Angle of arrival estimation through antenna array will also be explored. However, the exploitation of the polarization diversity and propagation channel will remain at state-of-the-art level.     

• Master’s degree in electrical/telecommunications/electronic engineering (or equivalent) with background in estimation theory and signal processing.

• Knowledge in GNSS and aerospace systems will be appreciated.