The aim of this paper was to propose a way of improving the positioning performance of the GPS system through hybridization with distance measurements derived from GSM power measurements. Both GPS and GSM measurements were generated using simulation models. The algorithm chosen to perform the hybridization is a particle filter. Simulations showed that while accuracy can only be slightly improved, a position solution can be obtained even when the GPS system is not available, thus considerably improving availability.

The trajectory of Space Vehicles (SVs) derives due to external perturbations, such as the variation in the gravitation fields (earth, moon and sun) or the solar pressure effect. The station keeping of SVs is the operation that keeps the satellites in a predefined window. This operation implies that the SV position is known. Actual positioning systems for SVs are mainly based on ground equipment, that means heavy infrastructures. Autonomous positioning and navigation systems using Global Navigation Satellite System (GNSS) can then represent a great reduction in platform design and operating costs. This paper presents the configurations for Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) missions. These configurations have been modeled from real mission parameters with the help of a dedicated software.

Header Compression techniques are now widely used in wireless and satellite communications. The main drawback of these techniques is to weaken the transmission against bit error or packet losses. Indeed, a corrupted or missing header can lead to a non-decompression of consecutive packets and then to a disconnection until the reception of a non-compressed packet. The parameters of the header compression system should then be carefully determined. In this paper, we first review the main header compression protocols standardized for a unidirectional link. This analysis allows us to build a simple generic header compression model depending on few parameters characterizing a header compression protocol. The evaluation of this model in cases corresponding to particular applications allows us to draw some first lessons for the use of header compression in Satellite communications.

This paper shows the interest of an interpolation method based on parametric modeling to retrieve missing samples in ECG signals. This problem occurs more and more with the emergence of telemedicine applications. The different links (fixed access network (PSTN), mobile access network (GSM/GPRS and future UMTS) or satellite interfacing (DVB- RCS technology)) involved in e-health applications are liable to induce errors on the transmitted data. These errors/losses can occur anytime and anywhere (according to the channel availability, memory overflows, protocols, etc) during a transmission process. Therefore the recovering of missing samples for biomedical signals is of great interest. The method used in this paper is based on a left-sided and right-sided autoregressive model, i.e., the interpolation algorithm uses the samples before and after the sequence of missing samples. An objective measure is used to assess the method performance. Results show that this interpolation method represents a really suitable technique to ECG signal reconstruction in a possible corrupted transmission.

In the past, in order for GPS (Global Positioning System) to work accurately, the presence of an unobstructed LOS (Line-Of-Sight) signal was necessary. Weak signals were not suitable for use because they may have large associated noise and other errors. The expansion of GPS to LBS (Location-Based Services) and other navigation applications all over the world, such as the E-911 and the E-112 mandates in the United States and Europe respectively, changed the paradigm. Consequently a dramatic increase in the need for more and more performant positioning techniques is expected, especially in urban and indoor environments. These rising localization requirements pose a particularly difficult challenge for GPS receivers design. The thesis objective is to evaluate and enhance existing GPS signal acquisition techniques for positioning goals in harsh environments, in the context of AGPS (Assisted GPS). The AGPS system assumes that the GPS receiver is connected to or introduced in a mobile phone. This allows for the transfer of AD (Assistance Data) to the GPS receiver via the GSM (Global System for Mobile communications) cellular network. Amongst others, the AD provides the GPS receiver with the list of visible satellites and estimates of their Dopplers and code delays, thus reducing the search window of these parameters. This work consists in exploring different GPS signal acquisition to reduce the acquisition time or TTFF (Time To First Fix), without affecting the receiver sensitivity. This is done after a prior study of the GPS radio channel. The study starts out with a revue of the GPS system and the GPS transmitted and received signal structure. The acquisition process is then described in details: the classical acquisition is first described in order to proceed afterwards with the impact of the propagation environment on this stage of the signal processing. For this purpose, harsh environments (urban and indoor) are modelled and analysed. This analysis enables to study the problems which encounter the radio frequency signal propagation through such environments. Note that the urban channel is studied using an existing statistical model developed by Alexander Steingass and Andreas Lehner at the DLR (German Aerospace Center) [Steingass et al., 2005]. On the other hand, an indoor channel model was developed by the ESA (European Space Agency) in the frame of a project entitled “Navigation signal measurement campaign for critical environments” and presented in [Pérez-Fontán et al, 2004]. But this model considers a time invariant statistical channel. Consequently, we developed an Indoor model which rather considers a time variant channel, by taking into account temporal variations of some channel parameters, like the transfer function delay and phase. The initial values are however based on the statistical distributions provided by the ESA model. The channels are analysed is terms of multipaths, cross-correlations and signal masking. The multipaths replicas are particularly disturbing in urban environments while the cross-correlations and masking effects are more disturbing in indoor environments. These phenomena may induce errors in the final solution calculated by the receiver. In order to avoid this error, one solution consists in increasing the signal observation duration in order to enhance the signal to noise ratio. But this generally implies longer acquisition time, thus affecting the receiver performance, commercially speaking. Indeed, the time requirements are as important as sensitivity requirements for GPS users. However, these two requirements are not generally compatible with each other. Consequently, an ideal solution consists in reducing the acquisition time without greatly affecting the receiver sensitivity. Accordingly, such advanced methods for acquisition signal processing are described next. Most of these methods aim at reducing the total acquisition time, rather than enhancing the receiver sensitivity. This means however that longer signal blocks can be processed (thus enhancing sensitivity) without affecting the global processing duration. At first, each of these methods is evaluated through the description of its advantages and drawbacks. A performance evaluation of these algorithms, using signals generated with a Spirent STR4500, ensues as a final step of this study.

It has already been shown that spectral estimation can be improved when applied to subband outputs of an adapted filterbank rather than to the original fullband signal. In the present paper, this procedure is applied jointly to a novel predictive autoregressive (AR) model. The model exploits time-shifting and is therefore referred to as time-shift AR (TSAR) model. Estimators are proposed for the unknown TS-AR parameters and the spectrum of the observed signal. The TS-AR model yields improved spectrum estimation by taking advantage of the correlation between subseries that arises after decimation. Simulation results on signals with continuous and line spectra that demonstrate the performance of the proposed method are provided.

Zerotree based coders have shown a good ability to be successfully adapted to 3D image coding. This paper focuses on the adaptation of EZW for the compression of hyperspectral images with reduced complexity. The subordinate pass is removed so that the location of significant coefficients does not need to be kept in memory. To compensate the quality loss due to this removal, a signed binary digit representation is used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

Ionosphere is a dispersive medium that can strongly affect GPS and GALILEO signals. Ionospheric delay affecting the GPS and GALILEO single frequency pseudorange measurements is the largest source of ranging error. In addition, this perturbation is difficult to model and thus difficult to predict. Nominal dual frequency measurements provide a good estimation of ionospheric delay. In addition, the combination of GPS and GALILEO navigation signals at the receiver level is expected to provide important improvements for civil aviation. It could, potentially with augmentations, provide better accuracy and availability of ionospheric correction measurements. Indeed, GPS users will be able to combine GPS L1 and L5 frequencies, and future GALILEO signals will bring their contribution as some of them will be transmitted at the same frequencies as the GPS signals. However, if affected by radio frequency interference, a receiver can lose one or more frequencies leading to the use of only one frequency to estimate ionospheric code delay. Therefore, it is felt by the authors as an important task to investigate the performance of techniques trying to sustain multi-frequency performance when a multi-constellation receiver installed in an aircraft loses dual frequency capability, during critical phases of flight. After a loss of several frequencies leading to a single frequency degraded mode, a receiver can use code and carrier phase pseudoranges made on only one carrier frequency to estimate the ionospheric delay. To achieve this estimation, the receiver can use the difference between code and carrier phase measurements. Indeed, this quantity can be modelled as twice the ionospheric delay plus noise, multipath, and the carrier phase ambiguity. The ionospheric delay can then be extracted from this, provided the ambiguity is properly removed. This can be achieved after convergence of a Kalman Filter for example, but then cycle slips need to be monitored. The probability of a cycle slip to occur is low but not negligible for civil aviation purposes. Several causes of cycle slips may be identified. For instance multipath, dynamics, signal blockage and ionospheric scintillation may be sources of this type of rupture in carrier phase measurements. Cycle slips may have random magnitudes. Those ones have to be detected and corrected with a performance compliant with civil aviation requirements for integrity, continuity, accuracy and availability. This problem of cycle slip detection is a priority before analyzing the accuracy of the single frequency iono corrected pseudorange. We propose to follow the methodology exposed below to assess the performance of potential algorithms of detection (and estimation) of cycle slips. First, the cycle slip detection and correction ability will be defined by the smallest cycle slip detectable with a required probability of missed detection. This smallest detectable cycle slip implies a bias on position error depending on geometry. Therefore, availability of protection against cycle slips compatible with APV 1 and APV 2 for instance, depends on geometry and must be computed at every second. The main goal of this paper is to know exactly the impact of the capability of cycle slip detection algorithms on the availability of reliable single frequency iono corrected pseudoranges.

Hyperspectral images present some specific characteristics that should be used by an efficient compressionsystem. This thesis focuses on the definition and the optimization of a full wavelet compression system for hyperspectral images. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Zerotree based compression algorithms are among the best for image compression. Therefore, in this work, efficient compression methods based on zerotree coding (EZW, SPIHT) are adapted on a near-optimal wavelet decomposition for hyperspectral images. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images. End users of hyperspectral images are often interested only in some specific features of the image (resolution, location) which depend on the application. A further adaptation of the proposed hyperspectral image compression algorithm is presented to allow random access to some part of the image, whether spatial or spectral. Resolution scalability is also available, enabling the decoding of different resolution images from the compressed bitstream of the hyperspectral data while reading a minimum amount of bits from the coded data. Final spatial and spectral resolutions are chosen independantly. Finally, any lossless compression method cannot be characterized without the definition of a distortion measure. Therefore, a group of five quality criteria presenting a good complementarity is defined. The purpose is to make sure the compression algorithm does not impact significantly the data quality. A new method using these five criteria shows a good ability to discriminate between different degradations. Application of this method to the newly defined algorithm shows that the degradation remains low for compression rate around 1.0 bit per pixel per band.