433 research outputs found
Matched direction detectors and estimators for array processing with subspace steering vector uncertainties
In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Cramer-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations
Analysis of the Bayesian Cramer-Rao lower bound in astrometry: Studying the impact of prior information in the location of an object
Context. The best precision that can be achieved to estimate the location of
a stellar-like object is a topic of permanent interest in the astrometric
community.
Aims. We analyse bounds for the best position estimation of a stellar-like
object on a CCD detector array in a Bayesian setting where the position is
unknown, but where we have access to a prior distribution. In contrast to a
parametric setting where we estimate a parameter from observations, the
Bayesian approach estimates a random object (i.e., the position is a random
variable) from observations that are statistically dependent on the position.
Methods. We characterize the Bayesian Cramer-Rao (CR) that bounds the minimum
mean square error (MMSE) of the best estimator of the position of a point
source on a linear CCD-like detector, as a function of the properties of
detector, the source, and the background.
Results. We quantify and analyse the increase in astrometric performance from
the use of a prior distribution of the object position, which is not available
in the classical parametric setting. This gain is shown to be significant for
various observational regimes, in particular in the case of faint objects or
when the observations are taken under poor conditions. Furthermore, we present
numerical evidence that the MMSE estimator of this problem tightly achieves the
Bayesian CR bound. This is a remarkable result, demonstrating that all the
performance gains presented in our analysis can be achieved with the MMSE
estimator.
Conclusions The Bayesian CR bound can be used as a benchmark indicator of the
expected maximum positional precision of a set of astrometric measurements in
which prior information can be incorporated. This bound can be achieved through
the conditional mean estimator, in contrast to the parametric case where no
unbiased estimator precisely reaches the CR bound.Comment: 17 pages, 12 figures. Accepted for publication on Astronomy &
Astrophysic
Fourier Analysis of Gapped Time Series: Improved Estimates of Solar and Stellar Oscillation Parameters
Quantitative helio- and asteroseismology require very precise measurements of
the frequencies, amplitudes, and lifetimes of the global modes of stellar
oscillation. It is common knowledge that the precision of these measurements
depends on the total length (T), quality, and completeness of the observations.
Except in a few simple cases, the effect of gaps in the data on measurement
precision is poorly understood, in particular in Fourier space where the
convolution of the observable with the observation window introduces
correlations between different frequencies. Here we describe and implement a
rather general method to retrieve maximum likelihood estimates of the
oscillation parameters, taking into account the proper statistics of the
observations. Our fitting method applies in complex Fourier space and exploits
the phase information. We consider both solar-like stochastic oscillations and
long-lived harmonic oscillations, plus random noise. Using numerical
simulations, we demonstrate the existence of cases for which our improved
fitting method is less biased and has a greater precision than when the
frequency correlations are ignored. This is especially true of low
signal-to-noise solar-like oscillations. For example, we discuss a case where
the precision on the mode frequency estimate is increased by a factor of five,
for a duty cycle of 15%. In the case of long-lived sinusoidal oscillations, a
proper treatment of the frequency correlations does not provide any significant
improvement; nevertheless we confirm that the mode frequency can be measured
from gapped data at a much better precision than the 1/T Rayleigh resolution.Comment: Accepted for publication in Solar Physics Topical Issue
"Helioseismology, Asteroseismology, and MHD Connections
Estimation of Radio Channel Parameters
Kurzfassung
Diese Dissertation behandelt die SchÀtzung der Modellparameter einer
Momentanaufnahme des Mobilfunkkanals. Das besondere Augenmerk liegt zum einen
auf der Entwicklung eines generischen Datenmodells fĂŒr den gemessenen Funkkanal,
welches fĂŒr die hochauflösende ParameterschĂ€tzung geeignet ist. Der zweite
Schwerpunkt dieser Arbeit ist die Entwicklung eines robusten ParameterschÀtzers
fĂŒr die Bestimmung der Parameter des entworfenen Modells aus Funkkanalmessdaten.
Entsprechend dieser logischen Abfolge ist auch der Aufbau dieser Arbeit.
Im ersten Teil wird ausgehend von einem aus der Literatur bekannten
strahlenoptischen Modell eine algebraisch handhabbare Darstellung von
beobachteten Wellenausbreitungspfaden entwickelt. Das mathematische Modell
erlaubt die Beschreibung von SISO (single-input-single-output)-
Ăbertragungssystemen, also von Systemen mit einer Sendeantenne und einer
Empfangsantenne, als auch die Beschreibung von solchen Systemen mit mehreren
Sende- und/oder Empfangsantennen. Diese Systeme werden im Allgemeinen auch als
SIMO- (single-input-multiple-output), MISO- (multiple-input-single-output) oder
MIMO-Systeme (multiple-input-multiple-output) bezeichnet. Im Gegensatz zu
bekannten Konzepten enthĂ€lt das entwickelte Modell keine Restriktionen bezĂŒglich
der modellierbaren Antennenarrayarchitekturen. Dies ist besonders wichtig in
Hinblick auf die möglichst vollstÀndige Erfassung der rÀumlichen Struktur des
Funkkanals. Die FlexibilitĂ€t des Modells ist eine Grundvoraussetzung fĂŒr die
optimale Anpassung der Antennenstruktur an die Messaufgabe. Eine solche
angepasste Antennenarraystruktur ist zum Beispiel eine zylindrische Anordnung
von Antennenelementen. Sie ist gut geeignet fĂŒr die Erfassung der rĂ€umlichen
Struktur des Funkkanals (Azimut und Elevation) in so genannten Outdoor-
Funkszenarien. Weiterhin wird im ersten Teil eine neue Komponente des
Funkkanaldatenmodells eingefĂŒhrt, welche den Beitrag verteilter (diffuser)
Streuungen zur FunkĂŒbertragung beschreibt. Die neue Modellkomponente spielt eine
SchlĂŒsselrolle bei der Entwicklung eines robusten ParameterschĂ€tzers im
Hauptteil dieser Arbeit. Die fehlende Modellierung der verteilten Streuungen ist
eine der Hauptursachen fĂŒr die begrenzte Anwendbarkeit und die oft kritisierte
fehlende Robustheit von hochauflösenden FunkkanalparameterschÀtzern, die in der
Literatur etabliert sind. Das neue Datenmodell beschreibt die so genannten
dominanten Ausbreitungspfade durch eine deterministische Abbildung der
Pfadparameter auf den gemessenen Funkkanal. Der Beitrag der verteilten
Streuungen wird mit Hilfe eines zirkularen mittelwertfreien GauĂschen Prozesses
beschrieben. Die Modellparameter der verteilten Streuungen beschreiben dabei die
Kovarianzmatrix dieses Prozesses. Basierend auf dem entwickelten Datenmodell
wird im Anschluss kurz ĂŒber aktuelle Konzepte fĂŒr FunkkanalmessgerĂ€te, so
genannte Channel-Sounder, diskutiert.
Im zweiten Teil dieser Arbeit werden in erster Linie AusdrĂŒcke zur Bestimmung
der erzielbaren Messgenauigkeit eines Channel-Sounders abgeleitet. Zu diesem
Zweck wird die untere Schranke fĂŒr die Varianz der geschĂ€tzten Modellparameter,
das heiĂt der Messwerte, bestimmt. Als Grundlage fĂŒr die VarianzabschĂ€tzung wird
das aus der ParameterschÀtztheorie bekannte Konzept der Cramér-Rao-Schranke
angewandt. Im Rahmen der Ableitung der CramĂ©r-Rao-Schranke werden auĂerdem
wichtige Gesichtspunkte fĂŒr die Entwicklung eines effizienten ParameterschĂ€tzers
diskutiert.
Im dritten Teil der Arbeit wird ein SchĂ€tzer fĂŒr die Bestimmung der
Ausbreitungspfadparameter nach dem Maximum-Likelihood-Prinzip entworfen. Nach
einer kurzen Ăbersicht ĂŒber existierende Konzepte zur hochauflösenden
FunkkanalparameterschÀtzung wird die vorliegende SchÀtzaufgabe analysiert und in
Hinsicht ihres Typs klassifiziert. Unter der Voraussetzung, dass die Parameter
der verteilten Streuungen bekannt sind, lÀsst sich zeigen, daà sich die
SchÀtzung der Parameter der Ausbreitungspfade als ein nichtlineares gewichtetes
kleinstes Fehlerquadratproblem auffassen lÀsst. Basierend auf dieser Erkenntnis
wird ein generischer Algorithmus zur Bestimmung einer globalen Startlösung fĂŒr
die Parameter eines Ausbreitungspfades vorgeschlagen. Hierbei wird von dem
Konzept der Structure-Least-Squares (SLS)-Probleme Gebrauch gemacht, um die
KomplexitÀt des SchÀtzproblems zu reduzieren. Im folgenden Teil dieses
Abschnitts wird basierend auf aus der Literatur bekannten robusten numerischen
Algorithmen ein SchÀtzer zur genauen Bestimmung der Ausbreitungspfadparameter
abgeleitet. Im letzten Teil dieses Abschnitts wird die Anwendung
unterraumbasierter SchÀtzer zur Bestimmung der Ausbreitungspfadparameter
diskutiert. Es wird ein speichereffizienter Algorithmus zur SignalraumschÀtzung
entwickelt. Dieser Algorithmus ist eine Grundvoraussetzung fĂŒr die Anwendung von
mehrdimensionalen ParameterschÀtzern wie zum Beispiel des R-D unitary ESPRIT
(Estimation of Signal Parameters via Rotational Invariance Techniques) zur
Bestimmung von Funkkanalparametern aus MIMO-Funkkanalmessungen. Traditionelle
Verfahren zur SignalraumschÀtzung sind hier im Allgemeinen nicht anwendbar, da
sie einen zu groĂen Speicheraufwand erfordern. AuĂerdem wird in diesem Teil
gezeigt, dass ESPRIT-Algorithmen auch zur ParameterschÀtzung von Daten mit so
genannter versteckter Rotations-Invarianzstruktur eingesetzt werden können. Als
Beispiel wird ein ESPRIT-basierter Algorithmus zur RichtungsschÀtzung in
Verbindung mit multibeam-Antennenarrays (CUBA) abgeleitet.
Im letzten Teil dieser Arbeit wird ein Maximum-Likelihood-SchĂ€tzer fĂŒr die neue
Komponente des Funkkanals, welche die verteilten Streuungen beschreibt,
entworfen. Ausgehend vom Konzept des iterativen Maximum-Likelihood-SchÀtzers
wird ein Algorithmus entwickelt, der hinreichend geringe numerische KomplexitÀt
besitzt, so dass er praktisch anwendbar ist. In erster Linie wird dabei von der
Toeplitzstruktur der zu schÀtzenden Kovarianzmatrix Gebrauch gemacht. Aufbauend
auf dem SchĂ€tzer fĂŒr die Parameter der Ausbreitungspfade und dem SchĂ€tzer fĂŒr
die Parameter der verteilten Streuungen wird ein Maximum-Likelihood-SchÀtzer
entwickelt (RIMAX), der alle Parameter des in Teil I entwickelten Modells der
Funkanalmessung im Verbund schÀtzt. Neben den geschÀtzten Parametern des
Datenmodells liefert der SchÀtzer zusÀtzlich ZuverlÀssigkeitsinformationen.
Diese werden unter anderem zur Bestimmung der Modellordnung, das heiĂt zur
Bestimmung der Anzahl der dominanten Ausbreitungspfade, herangezogen. AuĂerdem
stellen die ZuverlÀssigkeitsinformationen aber auch ein wichtiges SchÀtzergebnis
dar. Die ZuverlÀssigkeitsinformationen machen die weitere Verarbeitung und
Wertung der Messergebnisse möglich.The theme of this thesis is the estimation of model parameters of a radio channel snapshot. The main focus was the development of a general data model for the measured radio channel, suitable for both high resolution channel parameter estimation on the one hand, and the development of a robust parameter estimator
for the parameters of the designed parametric radio channel model, in line with this logical work flow is this thesis.
In the first part of this work an algebraic representation of observed
propagation paths is developed using a ray-optical model known from literature. The algebraic framework is suitable for the description of SISO (single-input-single-output) radio transmission systems. A SISO system uses one antenna as the transmitter (Tx) and one antenna as the receiver (Rx). The derived expression for the propagation paths is also suitable to describe SIMO (single-input-multiple-output), MISO (multiple-input-single-output), and MIMO (multiple-input-multiple-output) radio channel measurements. In contrast to other models used for high resolution channel parameter estimation the derived model makes no
restriction regarding the structure of the antenna array used throughout the measurement. This is important since the ultimate goal in radio channel sounding is the complete description of the spatial (angular) structure of the radio channel at Tx and Rx. The flexibility of the data model is a prerequisite for the optimisation of the antenna array structure with respect to the measurement
task. Such an optimised antenna structure is a stacked uniform circular beam array, i.e., a cylindrical arrangement of antenna elements. This antenna array configuration is well suited for the measurement of the spatial structure of the radio channel at Tx and/or Rx in outdoor-scenarios. Furthermore, a new component
of the radio channel model is introduced in the first part of this work. It describes the contribution of distributed (diffuse) scattering to the radio transmission. The new component is key for the development of a robust radio channel parameter estimator, which is derived in the main part of this work. The ignorance of the contribution of distributed scattering to radio propagation is one of the main reasons why high-resolution radio channel parameter estimators fail in practice. Since the underlying data model is wrong the estimators produce erroneous results. The improved model describes the so called dominant propagation paths by a deterministic mapping of the propagation path parameters
to the channel observation. The contribution of the distributed scattering is modelled as a zero-mean circular Gaussian process. The parameters of the distributed scattering process determine the structure of the covariance matrix of this process. Based on this data model current concepts for radio channel sounding devices are discussed.
In the second part of this work expressions for the accuracy achievable by a radio channel sounder are derived. To this end the lower bound on the variance of the measurements i.e. the parameter estimates is derived. As a basis for this evaluation the concept of the Cramér-Rao lower bound is employed. On the way to
the Cramér-Rao lower bound for all channel model parameters, important issues for the development of an appropriate parameter estimator are discussed. Among other things the coupling of model parameters is also discussed.
In the third part of this thesis, an estimator, for the propagation path parameters is derived. For the estimator the 'maximum-likelihood' approach is employed. After a short overview of existing high-resolution channel parameter estimators the estimation problem is classified. It is shown, that the estimation of the parameters of the propagation paths can be understood as a
nonlinear weighted least squares problem, provided the parameters of the distributed scattering process are known. Based on this observation a general algorithm for the estimation of raw parameters for the observed propagation paths is developed. The algorithm uses the concept of structured-least-squares (SLS) and compressed maximum likelihood to reduce the numerical complexity of the estimation problem. A robust estimator for the precise estimation of the propagation path parameters is derived. The estimator is based on concepts well known from nonlinear local optimisation theory. In the last part of this chapter the application of subspace based parameter estimation algorithms for path
parameter estimation is discussed. A memory efficient estimator for the signal subspace needed by, e.g., R-D unitary ESPRIT is derived. This algorithm is a prerequisite for the application of signal subspace based algorithms to MIMO-channel sounding measurements. Standard algorithms for signal subspace estimation (economy size SVD, singular value decomposition) are not suitable
since they require an amount of memory which is too large. Furthermore, it is shown that ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) based algorithms can also be employed for parameter estimation from data having hidden rotation invariance structure. As an example an ESPRIT
algorithm for angle estimation using circular uniform beam arrays (circular multi-beam antennas) is derived.
In the final part of this work a maximum likelihood estimator for the new component of the channel model is developed. Starting with the concept of iterative maximum likelihood estimation, an algorithm is developed having a low computational complexity. The low complexity of the algorithm is achieved by exploiting the Toeplitz-structure of the covariance matrix to estimate. Using
the estimator for the (concentrated, dominant, specular-alike) propagation paths and the parametric estimator for the covariance matrix of the process describing the distributed diffuse scattering a joint estimator for all channel parameter is derived (RIMAX). The estimator is a 'maximum likelihood' estimator and uses the genuine SAGE concept to reduce the computational complexity. The estimator provides additional information about the reliability of the estimated channel parameters. This reliability information is used to determine an appropriate model for the observation. Furthermore, the reliability information i.e. the estimate of the covariance matrix of all parameter estimates is also an important parameter estimation result. This information is a prerequisite for further processing and evaluation of the measured channel parameters
A review of closed-form Cramér-Rao Bounds for DOA estimation in the presence of Gaussian noise under a unified framework
The Cramér-Rao Bound (CRB) for direction of arrival (DOA) estimation has been extensively studied over the past four decades, with a plethora of CRB expressions reported for various parametric models. In the literature, there are different methods to derive a closed-form CRB expression, but many derivations tend to involve intricate matrix manipulations which appear difficult to understand. Starting from the Slepian-Bangs formula and following the simplest derivation approach, this paper reviews a number of closed-form Gaussian CRB expressions for the DOA parameter under a unified framework, based on which all the specific CRB presentations can be derived concisely. The results cover three scenarios: narrowband complex circular signals, narrowband complex noncircular signals, and wideband signals. Three signal models are considered: the deterministic model, the stochastic Gaussian model, and the stochastic Gaussian model with the a priori knowledge that the sources are spatially uncorrelated. Moreover, three Gaussian noise models distinguished by the structure of the noise covariance matrix are concerned: spatially uncorrelated noise with unknown either identical or distinct variances at different sensors, and arbitrary unknown noise. In each scenario, a unified framework for the DOA-related block of the deterministic/stochastic CRB is developed, which encompasses one class of closed-form deterministic CRB expressions and two classes of stochastic ones under the three noise models. Comparisons among different CRBs across classes and scenarios are presented, yielding a series of equalities and inequalities which reflect the benchmark for the estimation efficiency under various situations. Furthermore, validity of all CRB expressions are examined, with some specific results for linear arrays provided, leading to several upper bounds on the number of resolvable Gaussian sources in the underdetermined case
ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems
This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channel
estimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basic
system parameters such as number of codes, spreading factors, or traffic to training power ratio.Peer Reviewe
Time-delay interferometric ranging for LISA: Statistical analysis of bias-free ranging using laser noise minimization
Die Laser Interferometer Space Antenna (LISA) ist eine Mission der europĂ€ischen Weltraumagentur (ESA) zur Detektion von Gravitationswellen im Frequenzbereich zwischen 10^-4 Hz und 1 Hz. Gravitationswellen induzieren relative AbstandsĂ€nderungen, die LISA mithilfe von Laserinterferometrie mit PicometerprĂ€zision misst. Ein groĂes Problem hierbei ist das Frequenzrauschen der Laser. Um dieses zu unterdrĂŒcken, ist es notwendig, mithilfe eines Algorithmus namens TDI (engl. time-delay interferometry), virtuelle Interferometer mit gleichlangen Armen zu konstruieren, wie z.B. das klassische Michelson-Interferometer.
In dieser Arbeit untersuchen wir die Performanz von TDI unter realistischen Bedingungen und identifizieren verschiedene Kopplungsmechanismen des Laserfrequenzrauschens. Als erstes betrachten wir die Datenverarbeitung an Bord der Satelliten, die benötigt wird, um die Abtastrate der interferometrischen Messungen zu reduzieren. HierfĂŒr sind Anti-Alias-Filter vorgesehen, die der Faltung von Laserrauschleistung in das Beobachtungsband vorbeugen. AuĂerdem wirkt sich die Ebenheit der Filter auf die EffektivitĂ€t von TDI aus (engl. flexing-filtering-effect). Dieser Effekt ist bereits in der Literatur beschrieben und wir demonstrieren in dieser Arbeit die Möglichkeit, ihn mithilfe von Kompensationsfiltern effektiv zu reduzieren. Als zweites betrachten wir Kopplungsmechanismen von Laserfrequenzrauschen im TDI-Algorithmus selbst. Fehler in der Interpolation der interferometrischen Messungen und Ungenauigkeiten in den absoluten Abstandsmessungen zwischen den Satelliten fĂŒhren ebenfalls zu einer unzureichenden Reduzierung des Laserfrequenzrauschens. Wir beschreiben die oben genannten Kopplungsmechanismen analytisch und validieren die zugrundeliegenden Modelle mithilfe von numerischen Simulationen. Das tiefere VerstĂ€ndnis dieser Residuen ermöglicht es uns, geeignete instrumentelle Parameter zu wĂ€hlen, die von hoher Relevanz fĂŒr das Missionsdesign von LISA sind.
Des Weiteren beschĂ€ftigen wir uns in dieser Arbeit mit der möglichst genauen Bestimmung der absoluten AbstĂ€nden zwischen den Satelliten, die fĂŒr den TDI Algorithmus erforderlich sind. HierfĂŒr werden die Abstandsinformationen aus den SeitenbĂ€ndern und der PRN-Modulation (engl. pseudo-random noise) kombiniert. Wir zeigen, dass die PRN-Messung von systematischen Verzerrungen betroffen ist, die zu Laserrauschresiduen in den TDI-Variablen fĂŒhren. Um diesen Fehler zu korrigieren, schlagen wir als zusĂ€tzliche Abstandsmessung TDI-Ranging (TDI-R) vor. TDI-R ist zwar ungenauer, aber frei von systematischen Verzerrungen und kann daher zur Kalibrierung der PRN-Messungen herangezogen werden. Wir prĂ€sentieren in dieser Arbeit eine ausfĂŒhrliche statistische Studie, um die Performanz von TDI-R zu charakterisieren. DafĂŒr formulieren wir die Likelihood-Funktion der interferometrischen Messungen und berechnen die Fisher-Informationsmatrix, um die theoretisch mögliche untere Grenze der SchĂ€tzvarianz zu finden. Diese verhĂ€lt sich invers proportional zur Integrationszeit und dem VerhĂ€ltnis von SekundĂ€rrauschleistung, die die interferometrische Messung fundamental limitiert, und Laserrauschleistung. ZusĂ€tzlich validieren wir die analytische untere Grenze der SchĂ€tzvarianz mithilfe von numerischen Simulationen und zeigen damit, dass unsere Implementierung von TDI-R optimal ist. Der entwickelte TDI-R-Algorithmus wird Teil der Datenverarbeitungspipeline sein und KonsistenzprĂŒfungen und Kalibrierung der primĂ€ren Abstandsmessmethoden ermöglichen.The Laser Interferometer Space Antenna (LISA) is a future ESA-led space-based observatory to explore the gravitational universe in the frequency band between 10^-4 Hz and 1 Hz. LISA implements picometer-precise inter-satellite ranging to measure tiny ripples in spacetime induced by gravitational waves (GWs). However, the single-link measurements are dominated by laser frequency noise, which is about nine orders of magnitude larger than the GW signals. Therefore, in post-processing, the time-delay interferometry (TDI) algorithm is used to synthesize virtual equal-arm interferometers to suppress laser frequency noise.
In this work we identify several laser frequency noise coupling channels that limit the performance of TDI. First, the on-board processing, which is used to decimate the sampling rate from tens of megahertz down to the telemetry rate of a few hertz, requires careful design. Appropriate anti-aliasing filters must be implemented to mitigate folding of laser noise power into the observation band. Furthermore, the flatness of these filters is important to limit the impact of the flexing-filtering effect. We demonstrate that this effect can be effectively reduced by using compensation filters on ground. Second, the post-processing delays applied in TDI are subject to interpolation and ranging errors. We study these laser and timing noise residuals analytically and perform simulations to validate the models numerically. Our findings have direct implications for the design of the LISA instrument as we identify the instrumental parameters that are essential for successful laser noise suppression and provide methods for designing appropriate filters for the on-board processing.
In addition, we discuss a dedicated ranging processing pipeline that produces high-precision range estimates that are the input for TDI by combining the sideband and pseudo-random noise (PRN) ranges. We show in this thesis that biases in the PRN measurements limit the laser noise suppression performance. Therefore, we propose time-delay interferometric ranging (TDI-R) as a third ranging sensor to estimate bias-free ranges that can be used to calibrate the biases in the PRN measurements.
We present a thorough statistical study of TDI-R to evaluate its performance. Therefore, we formulate the likelihood function of the interferometric data and use the Fisher information formalism to find a lower bound on the estimation variance of the inter-satellite ranges. We find that the ranging uncertainty is proportional to the inverse of the integration time and the ratio of secondary noise power, that limits the interferometric readout, to the laser noise power. To validate our findings we implement prototype TDI-R pipelines and perform numerical simulations. We show that we are able to formulate optimal estimators of the unbiased range that reach the Cramér-Rao lower bound previously expressed analytically. The developed TDI-R pipeline will be integrated into the ranging processing pipeline to perform consistency checks and ensure well-calibrated inter-satellite ranges
Performance Limits of GNSS Code-Based Precise Positioning: GPS, Galileo & Meta-Signals
This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the CramĂ©râRao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques
Mod-phi convergence I: Normality zones and precise deviations
In this paper, we use the framework of mod- convergence to prove
precise large or moderate deviations for quite general sequences of real valued
random variables , which can be lattice or
non-lattice distributed. We establish precise estimates of the fluctuations
, instead of the usual estimates for the rate of
exponential decay . Our approach provides us with a
systematic way to characterise the normality zone, that is the zone in which
the Gaussian approximation for the tails is still valid. Besides, the residue
function measures the extent to which this approximation fails to hold at the
edge of the normality zone.
The first sections of the article are devoted to a proof of these abstract
results and comparisons with existing results. We then propose new examples
covered by this theory and coming from various areas of mathematics: classical
probability theory, number theory (statistics of additive arithmetic
functions), combinatorics (statistics of random permutations), random matrix
theory (characteristic polynomials of random matrices in compact Lie groups),
graph theory (number of subgraphs in a random Erd\H{o}s-R\'enyi graph), and
non-commutative probability theory (asymptotics of random character values of
symmetric groups). In particular, we complete our theory of precise deviations
by a concrete method of cumulants and dependency graphs, which applies to many
examples of sums of "weakly dependent" random variables. The large number as
well as the variety of examples hint at a universality class for second order
fluctuations.Comment: 103 pages. New (final) version: multiple small improvements ; a new
section on mod-Gaussian convergence coming from the factorization of the
generating function ; the multi-dimensional results have been moved to a
forthcoming paper ; and the introduction has been reworke
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