Normalized GNSS Interference Pattern Technique

It is well known that water level and snow height can be monitored with the ground reflectometry GNSS-R approach [1, 2]. In this approach the antenna situated on a mast, receives a direct GNSS signal coming from the satellite and a nadir signal reflected by the observed surface. Assuming that the antenna position is known we can compute the position of the surface of reflection. For water level monitoring and snow determination, this approach provides precise localization and dating of the measures that allows to process spatio-temporal comparison of water level and snow cover, respectively. These parameters are very important for flood monitoring, avalanche prevention, as well as for hydroelectic companies. Furthermore the approach is noninvasive and can be easily implemented on a portable instrument and embedded in a vehicle with a mast. The Interference Pattern Technique considers the behavior of the SNR of the received GNSS signal as a function of the satellite elevation [1]. The received signal is indeed the integration by the antenna of the direct and nadir reflected GNSS signals. Due to their different phase variations, the SNR oscillates at a rate proportional to the height between the antenna and the surface of specular reflection. Unfortunately the measurement is typically very long because it needs to process the SNR for high satellite elevation variations. We indeed need to observe a sufficient number of SNR oscillations to estimate the frequency and derive the surface height. In order to reduce the estimation time to a fraction of one period of the SNR variation, we propose to normalize the measures. The normalization consists in varying the antenna height of a value dh in order to read the minimum and maximum value of SNR for a given satellite elevation, and then in processing with these values the SNR measured for different satellite elevations. We show in this paper that the normalization allows to compute the cosine of the phase delay between the direct and reflected signals and to estimate the signal frequency on a fraction of a period. We also derive the minimum antenna variation range dh as a function of the satellite elevation. We deduce from this function the minimum time of observation as a function of the satellite elevation rate. We derive the exact evolution of the SNR as a function of the signals parameters (Doppler frequency, code delay, CN0) of the visible satellites [3]. The proposed method is assessed on real and synthetic signals.

Doppler-based positioning using LEO augmented satellite constellation - Simulations of perspectives

Alice Andreetti

The Global Navigation Satellite System (GNSS) refers to a constellation of satellites emitting signals from space, used to provide Position, Navigation and Timing (PNT) services to the receivers on Earth. Nowadays, the GNSS is exploited in a wide range of applications among which ground mapping, transportation, machine control, timing, surveying, defense, or aerial photogrammetry [1]. However, as we become more dependent on GNSS technology, we also become more vulnerable to its limitations [2]. The GNSS operates with satellites orbiting in Medium Earth Orbits (MEO, approximately 20200 [km] in altitude). On one hand, this great distance allows to have a large footprint of the satellite signal on Earth, but on the other hand, it requires the signal to cross a wide path in the atmosphere before reaching the receiver. This results in a decrease of signal strength, which is not an issue in open-sky condition, but becomes a limitation in deep attenuation environments, with little or no service in urban canyon, in dense cities or indoors [3]. In this contribution, a possible strategy to cope with this issue is proposed and evaluated through a simulation process. It implies the use of Low Earth Orbits (LEO) satellites as a support for the GNSS PNT service. Since nowadays no broadband big LEO constellation broadcasting navigation messages exist, in this project various LEO constellation setups have been simulated and tested. Because LEOs are much closer to Earth (200 to 1500 [km]), they move faster in the sky, passing overhead in minutes instead of hours like MEOs. This gives rise to the possibility to exploit the Doppler effect to accurately locate the user. Therefore, three different PNT algorithms, employing the Kalman filter, are implemented and the evaluation of the localization quality is made at four distinct locations on the globe. The results show that, for most of the cases, compared to the standard GPS pseudorange only PNT, the addition of LEO pseudoranges already improve the localization quality. The performance of the PNT algorithm increases even more with the exploitation of both pseudoranges and pseudorangerates (Doppler) measurements. Nevertheless, the choice of constellation parameters such as satellites altitude, total number of satellites, orbit inclination or the frequency of the emitted signal, has a great impact on the magnitude of the improvement in the localization quality. The factor that has the most significant influence seems to be the signal frequency and for that reason it must be carefully chosen: the higher the better. Moreover, it has been demonstrated that at high elevation cut-off angles (denied environments), the addition of LEO constellation is a valid option and allows us to obtain an accurate position even when the GNSS only can not provide a solution. Finally, the LEO only system seems to be a promising alternative to cope with GNSS jamming or spoofing. However, the optimal LEO parameters providing an accurate solution for all the location on the globe has not yet been identified. In conclusion, the result of this effort is the awareness that, once the optimal parameters for the LEO system have been found, it is a valid option to augment the GNSS, or even as a standalone backup, especially when the Doppler-based positioning is applied. Nevertheless, we are still far from that end and much more research needs to be done.

2020

Adaptive frequency tracking and application to biomedical signals

Yann Prudat

The main information of a signal resides in its frequency, its amplitude (or its power) and in their temporal evolution. Thus, a great number of methods for instantaneous frequency estimation have been proposed in the literature. Most of these algorithms use an adaptive filter-based (notch or bandpass filter) structure. In many applications, the information of interest is present in more than one signal. However, to our knowledge no algorithm able to track the frequency on several signals has been presented in the literature. Usually, frequency components are estimated and extracted on each signal independently. Artifacts or noise relative to a specific signal can thus disturb the frequency estimation process. Moreover, low amplitude components present in every signal (but non dominant) will not be estimated. The objective in the first part of this thesis is to develop different methods able to extend existing frequency tracking algorithm in order to improve the quality of the estimate, in terms of estimation variance and robustness with respect to noise. The proposed methods can be applied to algorithms using adaptive filters for the frequency estimation. For the multi-signal frequency tracking extension, these methods use the redundancy of information present in the signals under study. A first approach uses a unique filter for every signal. A set of weights is computed, depending on a measure of the estimate quality, and makes it possible to balance the influence of each signal on the tracking filter update. The second approach consists in using a different adaptive filter for each signal. A set of constraints links the central frequencies of each filter so that they are as similar as possible. Both methods yield frequency estimates more robust with respect to noise and more stable, without any decrease in estimation accuracy. For the harmonic frequency tracking, we propose a method using the information present in the harmonic component to improve the estimate of the fundamental frequency. The proposed methods also permit to extract the signal components corresponding to the estimated frequencies. These components are very useful for subsequent study. In the second part of this thesis, the algorithms developed in the first part are applied to biomedical signals. Two different applications are studied in this work : electrocardiograms and electroencephalograms. Firstly, a frequency tracking algorithm as well as its multi-signal extension are used to predict the success of electrical cardioversion attempts in patients suffering from atrial fibrillation. The instantaneous frequency is estimated using the algorithms and the corresponding signal component is extracted from electrocardiograms recorded prior to the attempt. With a few parameters computed on the estimated frequency and the corresponding signal component, we were able to predict the result of the cardioversion attempt on our database comprising 18 patients with a success rate of 94% for both algorithms. We think that this result can be very useful for helping the clinician to choose the appropriate therapy for atrial fibrillation management. The developed algorithms are also used to track the oscillatory components present in electroencephalograms. The performance of the basic algorithm is illustrated using single-trial electroencephalogram signals from a visual evoked potential experiment. The algorithm is used to track the gamma component (30-50 Hz). It is able to successfully track the spectral component in spite of the fact that large amplitude variations are present in the signal. A complex version of the multi-signal extension is also used to have an algorithm able to track multiple frequency components on multiple signals. The performance of this algorithm is also illustrated with single-trial electroencephalogram signals. It was shown to be able to correctly track up to four frequency components simultaneously. The quality of the estimation is improved using multiple lead signals.

EPFL2009

Doppler-based positioning using LEO augmented satellite constellation - Simulations of perspectives

Alice Andreetti

2020

Adaptive frequency tracking and application to biomedical signals

Yann Prudat

EPFL2009