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The derivation of estimation lower bounds is paramount to design and assess the performance of new estimators. A lot of effort has been devoted to the joint distance-velocity estimation problem, but very few works deal with acceleration, being a key aspect in several high-dynamics applications. Considering a generic band-limited signal formulation, in this contribution we derive a new closed-form Cramer-Rao bound (CRB) expression for joint time-delay/Doppler/acceleration estimation. This new formulation, especially easy to use, depends only on the baseband signal samples, and can be exploited for several purposes including estimator assessment (i.e., for signal design or to derive performance loss metrics with respect to the best (lowest) CRB). These results are illustrated and validated with two representative band-limited signals, namely, a GPS L1 C/A signal and a linear frequency modulated chirp signal.

A plethora of applications require precise and reliable positioning and navigation, for which Global Navigation Satellite Systems (GNSS) are the main source of information. The main drawback of GNSS is their degraded performance in harsh environments. A possible way to overcome such limitation is to resort to cooperative positioning (CP) strategies, where a set of receivers share information to improve their position estimates. In this contribution we propose a CP GNSS method, relying on Multidimensional Scaling (MDS), to cope with the main limitation of standard CP solutions: allow for a subset of receivers to be in GNSS-denied conditions. The main parameters impacting the overall system performance are discussed, and the performance of the GNSS-MDS approach compared to standalone and cooperative GNSS state-of-the art techniques.

GNSS carrier phase observations are fundamental for safety-critical applications where the requirements of accuracy and availability are stringent. Upon the deployment of new GNSS constellation and frequencies, the large number of observations can lead to a decreased probability of successfully mapping the real-valued carrier phase ambiguities to integer ones. Partial Ambiguity Resolution (PAR) relaxes the condition of fixing the complete set of ambiguities and finds instead the subset which maximizes the success rate or grants a low failure rate. This work introduces Precision-Driven (PD) PAR, a technique for which a constraint on the formal precision of the fixed solution is added, and the subset selection is realized based on the projection of the ambiguities into the position domain. The performance characterization is realized on a synthetic scenario, where observations from a triple-frequency, triple-constellation are employed and the estimation is realized on a snapshot (memoryless) manner.

Standard state estimation techniques, either for linear or nonlinear systems, typically assume a perfect knowledge of the system, i.e., known process and measurement functions, and the corresponding noise statistics. The filter performance strongly depends on such knowledge and a possible model mismatch may lead to a significant performance degradation, but closed-form expressions for the resulting bias and covariance are not available. In this contribution, we explore the impact in extended Kalman filter approximations of a parametric model mismatch on both system functions and provide expressions for the estimator bias and covariance error induced by the mismatch. An illustrative example is shown to support the discussion.

In standard two-step Global Navigation Satellite Systems (GNSS) receiver architectures the precision on the position, velocity and time estimates is driven by the precision on the intermediate parameters, i.e., delays and Dopplers. The estimation of the time-delay is in turn driven by the baseband signal resolution, that is, by the type of broadcasted signals. Among the different GNSS signals available the so-called AltBOC modulated signal, appearing in the Galileo E5 band and the new GNSS meta-signal concept, is the one which may provide the better time-delay precision. In order to meet the constraints of safety-critical applications such as Intelligent Transportation Systems or automated aircraft landing, it is fundamental to known the ultimate code-based precision achievable by standalone GNSS receivers. The main goal of this contribution is to assess the time-delay precision of AltBOC type signals. The analysis is performed by resorting to a new compact closed-form Cramer-Rao bound expression for time-delay estimation which only depends on the signal samples. In addition, the corresponding time-delay maximum likelihood estimate is also provided to assess the minimum signal-to-noise ratio that allows to be in optimal receiver operation.

In previous works, new families of Pseudo-Random Noise (PRN) codes of length 1023 chips were proposed in order to ease the acquisition engine. These studies analyzed several metrics for code design in order to improve the acquisition but no analysis was conducted on the estimation performance, which in turn drives the final position, velocity and timing estimates. The main goal of this contribution is to assess if these new PRN codes designed to improve the acquisition engine lose in achievable time-delay estimation performance with respect to the standard GPS L1 C/A Gold codes. The analysis is performed by resorting to a new compact closed-form Cramer-Rao bound expression for time-delay estimation which only depends on the signal samples. In addition, the corresponding time-delay maximum likelihood estimate is also provided to assess the minimum signal-to-noise ratio that allows to be in optimal receiver operation. GNSS, time-delay estimation, band-limited signals, Cramer-Rao bound, signal acquistion.

One of the main challenges in nonlinear Bayesian filtering is the so-called curse of dimensionality, that is, the computational complexity increase and associated performance degradation in high-dimensional systems. In the context of particle filtering (PF), a possible solution to mitigate such performance loss is the multiple PF (MPF) approach, where the original state is partitioned into several lower dimensional subspaces, and a set of interconnected PFs are used to characterize the marginal subspace posteriors. Two key issues are: i) how to partition the state, which is application dependent, and ii) how to let the filters (i.e., subspaces) fuse or merge depending on the time-varying conditions of the system, in order to improve the overall estimation performance. We propose a probabilistic approach to the adaptive state-partitioning problem within the MPF, which is based on the computation of subspace second order statistics. An illustrative multiple target tracking example is considered to support the discussion.

During the last decade, because of the growing needs for indoor navigation, many authors tends to improve the processing gain of the GNSS receivers by increasing the observation time. In this paper, we show that this gain can also be improved by considering the Doppler information during the trilateration step. Indeed, whereas this information is not enough precise to be taken into account for small observation time, it becomes more relevant as the integration time increases. Moreover, the Doppler information is less sensitive to multipath when the receiver is static. For all these reasons, the Doppler information should be exploited in indoor environments. In this paper, we show how to exploit the Doppler information in an optimal sense to get a more precise position. The proposed Doppler-aided algorithm is shown to be a corrected version of the standard trilateration procedure that uses only the pseudo-ranges. Numerical simulations attest to the validity of this approach in indoor scenarios.

Localization in large sensor networks requires decentralized computationally efficient filtering solutions. To model challenging indoor propagation conditions, including non-line-of-sight conditions and other channel variations, it may be necessary to consider non-Gaussian distributed errors. In this case, Gaussian filters cannot be considered as is and particle filters do not meet the system requirements on computational cost and/or available memory. In this article we explore decentralized Gaussian information filtering strategies under skew-Laplace errors, exploiting the hierarchically Gaussian formulation of such distribution. An illustrative example is considered to show the performance and support the discussion.

Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored in order to provide a robust solution to the Global Navigation Satellite Systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey most popular approaches for robust regression and how they can be adapted into a general methodology for robust SPP.

In estimating an unknown parameter vector in a linear regression model, it is common to use linearly constrained minimum variance estimators (LCMVEs). For a long time, LCMVEs were studied in the context of stationary constraints under both stationary or non-stationary environments. Recently, a new family of non-stationary constraints leading to Kalman-like recursive LCMVEs has been introduced for fully coherent signal (FCS) sources. A noteworthy feature of this family is to allow the possibility of, at each new observation, incorporating new constraints. This article extends these results to the case of partially coherent signal (PCS) sources. Indeed, without ad hoc modifications of the Kalman-like recursion, estimation of the amplitudes of PCS sources exhibit a performance breakdown even for a slight loss of coherence. Last but not least, it is shown that PCS sources introduce a lower limit in the achievable performance in the large sample regime.

Standard filtering techniques, such as Kalman, sigma-point or particle filters, assume a perfect knowledge of the system. This implies that both process and measurement functions, and the system noise statistics, are assumed known and fit the reality. Regarding the noise statistics this involves knowing not only the distributions but also their parameters. In this contribution, we explore the impact of system model mismatch and uncertain noise statistics parameters into linear minimum mean square error estimators for linear discrete state-space models. Illustrative examples are shown to support the discussion.

Indoor target tracking appears in several engineering problems and is a key enabler to a myriad of new applications. Localization in such global navigation satellite system (GNSS)-denied environments typically relies on the use of existing infrastructures and already deployed technologies. In this paper, we are interested in received signal strength (RSS)-based multiple target tracking (MTT) in wireless sensor networks (WSN). From an estimation standpoint, two problems arise: i) standard Bayesian filtering techniques are not able to cope with high-dimensional systems, and ii) WSN are typically built with resource-constrained low-cost sensors, which implies the need for distributed algorithms. A possible solution is to use a multiple Bayesian filtering approach, where the state-space is partitioned in several lower dimensional sub-spaces, and then a set of parallel filters are used to characterize the marginal subspace posteriors. In this work, we propose a new distributed multiple Gaussian filtering (MGF) formulation, to solve both the curse-of-dimensionality in high-dimensional systems and the need of distributed algorithms in network localization applications.

For linear discrete state-space (LDSS) models, under certain conditions, the Wiener filter (WF) has a convenient recursive predictor/corrector format, aka the Kalman filter (KF). As a contribution to Wiener filtering and Kalman filtering in the context of LDSS models, the paper derives the family of linear constraints for which the linearly constrained WF (LCWF) can be computed recursively in the form of a KF, leading to a linearly constrained KF (LCKF). Among other things, as exemplified by an array processing example, the LCKF may provide alternative solutions to H∞ filter and unbiased finite impulse response filter to robustify the KF, which performance are sensible to misspecified noise or uncertainties in the system matrices.

The multiple Bayesian filtering approach is based on the partitioning of the state-space in several lower dimensional subspaces, combined with a set of parallel filters that characterize the marginal subspace posteriors. This solution has been shown to perform well and solve some of the problems typically suffered by standard Bayesian filters, such as the curse-of-dimensionality, in some scenarios. An inherent problem in the application of multiple Gaussian filters (MGF) and multiple particle filters (MPF) proposed in the literature is how to partition the state-space. A closed answer does not exist because this is an application-dependent problem. In this contribution we further elaborate on the multiple filtering approach, and propose a probabilistic adaptive state-partitioning strategy based on the crosscorrelation computed at each filter.

One of the more challenging propagation conditions in Global Navigation Satellite Systems (GNSS), clearly affecting high-precision receivers and safety critical applications, is ionospheric scintillation. One of the main disadvantages of standard phase tracking architectures is the estimation vs mitigation tradeoff, that is, it is impossible to distinguish between the line-of-sight (LOS) phase contribution of interest and phase variations induced by the ionosphere. A possible solution is to consider a state-space formulation of the problem, where the scintillation amplitude and phase are estimated together with the LOS phase variations. The current Kalman filter-based techniques exploiting this idea consider an off-line scintillation model fitting, thus not suitable for real-life applications. In this contribution we propose an adaptive model learning strategy to be able to cope with time-varying unknown scintillation conditions. The performance of the new approach is shown using real ionospheric scintillation data recorded over Hanoi in 2015.

Wireless localization by time-of-arrival (TOA) measurements is typically corrupted by non-line-of-sight (NLOS) conditions, causing biased range measurements that can degrade the overall positioning performance of the system. In this article, we propose a localization algorithm that is able to mitigate the impact of NLOS observations by employing a heavy-tailed noise statistical model. Modeling the observation noise by a skew t-distribution allows us to, on the one hand, employ a computationally light sigma-point Kalman filtering method while, on the other hand, be able to effectively characterize the positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions. Numerical results show the enhanced performance of such approach.

A phaseguide-based optofluidic router (PHASOR) is presented. By using phaseguides, DI-water can be pinned in the required angle so that light incident undergoes total internal reflection and can be reflected towards one of the six output channels without movable parts. In addition, the PHASOR is coupled to two enzymatically-functionalized photonic lab on a chip (PhLoC). Using real rat serum samples, we were able to monitor L-Lactate and Glucose in real time using a single light source.

Ionospheric scintillation is the name given to the disturbance caused by electron density irregularities along the propagation path of electromagnetic waves through the ionosphere. These non-nominal propagation conditions mainly cause carrier phase variations and amplitude fades. It is important to point out that both degradations are correlated, and thus deep amplitude fades and rapid phase variations (the so-called canonical fades) occur in a simultaneous and random manner. Regarding the carrier synchronization problem under harsh propagation conditions, such as high dynamics, multipath effects or ionospheric scintillation, canonical fades make the latter the most challenging scenario, which can be considered as a benchmark on the performance of robust carrier tracking techniques. This phenomenon particularly affects satellite-based positioning systems in the equatorial regions and at high latitudes. In this work, both amplitude and phase variations due to scintillation are first modeled using an autoregressive (AR) model, and then included into the system state-space formulation. Therefore, a Kalman filter (KF) based solution can be aware of both dynamics and scintillation phase evolutions. This arises as the natural solution to mitigate those undesired propagation effects. Moreover, in order to counteract the main drawbacks of standard KF-based tracking solutions, an extended KF (EKF) architecture is considered, tracking both the phase dynamics, scintillation phase and amplitude. This implies directly operating with the baseband received signal's complex samples, avoiding the use discriminators and thus its saturation and the loss of Gaussianity. Simulation results are provided to support the theoretical discussion and to show the performance improvements of such new approach.

In this work an hybrid electronic tongue for the analysis of wine is presented. Its main novelty is the joint use of different kind of sensorspotentiometric ISFETs, thin film metal microelectrodes as amperometric and conductivity sensors and an optofluidic systemthus allowing an unprecedented measurement of information of a specific sample. Moreover, this electronic tongue is completed with the use of data processing tools like Principal Component Analysis (PCA) Partial Least Square (PLS) and Soft Independent Modeling Class Analogy (SIMCA). The so-called hybrid electronic tongue, here developed, has been applied to classify the wine according to its distinct features like grape variety, geographic origin, year and organoleptic characteristics. This approach has also allowed performing quantitative measurements of parameters of interest in quality control, such as alcoholic degree, pH, ions, total acidity, glycerol and color. Keywords—hybrid electronic tongue; microelectrodes; optofluidic system; wine; quality control

International audience; In many real-life Bayesian estimation problems, it is appropriate to consider non-Gaussian noise distributions to model the existence of outliers, impulsive behaviors or heavy-tailed physical phenomena in the measurements. Moreover, the complete knowledge of the system dynamics uses to be limited, as well as for the process and measurement noise statistics. In this paper, we propose an adaptive recursive Gaussian sum filter that addresses the adaptive Bayesian filtering problem, tackling efficiently nonlinear behaviors while being robust to the weak knowledge of the system. The new method is based on the relationship between the measurement noise parameters and the innovations sequence, used to recursively infer the Gaussian mixture model noise parameters. Numerical results exhibit enhanced robustness against both non-Gaussian noise and unknown parameters. Simulation results are provided to show that good performance can be attained when compared to the standard known statistics case.

This contribution deals with robust carrier phase tracking in Global Navigation Satellite Systems, where the ultimate goal is to obtain accurate and robust phase estimates under non-nominal conditions, such as high dynamics, strong fading and ionospheric scintillation. Within this framework, an Interacting Multiple Model approach, using a bank of parallel Kalman-based filters with different dynamic state models, is proposed to cope with signals corrupted by severe ionospheric scintillation. In the proposed formulation, the time-varying and correlated scintillation phase is introduced into the dynamic system using an AR(1) model. Simulation results are provided to show the enhanced robustness and improved accuracy of the proposed approach, with respect to state-of-the-art carrier phase tracking techniques.

We report the first experimental realization of an analogous of the Stimulated Raman Adiabatic Passage (STIRAP) technique [1] working as a light spectral filter [2,3]. The system consists of three identical coupled total internal reflection silicon oxide waveguides designed such that there is a counterintuitive variation of the evanescent couplings between the waveguides along the propagation direction (see Fig.1(a)). If couplings are strong and wide enough along the propagation direction, which only occurs for long enough wavelengths, this geometry allows for an adiabatic following of a supermode that implies only the outermost waveguides. Thus, if light is injected into the right waveguide of our device, after propagating along the system, long wavelengths are transferred into the left output whereas short wavelengths end up into the central and right outputs. In this way, the system operates as a simultaneous high and low-pass spectral filter in the visible range. We refer to this device as Spatial Adiabatic Passage (SAP) filter. Regarding the filtering efficiency, the measured stopband reaches values up to -11dB for the left output, and around -20dB for the right plus central outputs. The passband values are close to 0dB for both cases. Fig. 1(b) shows the experimentally measured intensities at the output of the left waveguide and the sum of the outputs of the right and central waveguides as the transmittance relative to the total output intensity for one of the fabricated devices.

International audience; This paper considers the problem of ultra--tight GNSS/INS integration. We propose a new approach, deriving the direct relation between Inertial Measurement Unit (IMU) measurements and synchronization parameters, used in the trilateration algorithm to compute the position of the receiver. We take into account the IMU's eventual biased behavior by introducing it into the state representation. We use a recently--developed, square-root derivative--free Gaussian nonlinear filter to solve the estimation problem.

International audience; This paper addresses the joint path delay and time-varying complex gain estimation for continuous phase modulation (CPM) over a time-selective slowly varying flat Rayleigh fading channel. We propose an expectation-maximization (EM) algorithm for path delay estimation in a Kalman smoother framework. The time-varying complex gain is modeled by a first order autoregressive (AR) process. Such a modeling yields to the representation of the problemby a dynamic bayesian system in a state-space form that allows the application of EMalgorithm in the context of unobserved data for obtaining an estimate of the path delay. This is used with Kalman smoother for state estimation. We derive analytically a closed form expression of the modified hybrid Cram'er-Rao bound (MHCRB) for path delay and complex gain parameters. Finally, some numerical examples are presented to illustrate the performance of the proposed algorithm compared to the conventional generalized correlation method and to the MHCRB.