In GNSS-denied and poorly-known environments, reliable autonomous navigation is a major challenge, as conventional data fusion algorithms require an extensive knowledge of their surroundings to accurately estimate the vehicle state. To address this issue, we propose to use an adaptive Gaussian process regression to model an approximation of the environment solely based on scarce and noisy samples. This paper takes advantage of the flexibility of Gaussian processes to dynamically model the underlying terrain by adapting the process to the relevant data at each step. To this end, we propose to locally fit the Gaussian process and perform regression by using only a subset of data points selected according to a proximity criterion. The developed method employs a regularised particle filter to effectively estimate the system state using the output of the regression. By integrating Gaussian process-based terrain predictions, the particle filter can effectively compensate for the lack of precise terrain information, thus enhancing navigation performance in GNSS-denied scenarios. To evaluate the effectiveness of the proposed approach, simulations were performed in terrain-aided navigation of an unmanned aerial vehicle. Comparative analysis with existing navigation methods illustrates the superiority of the proposed approach in terms of accuracy and robustness.

This paper addresses the problem of tracking a cluster of space debris sufficiently close to each other to be considered as a single ex-tended object. State-of-the-art random-matrix methods estimate the kinematics of the object centroid by assuming that its shape is elliptic and that the observations are randomly distributed within this ellipsoid. However, space debris, whose motion is driven by the gravitational force, spread out into a "banana"-like-shaped cluster. In this paper, we propose a novel Lie-group based parameterization to intrinsically capture the "banana"-like shape. More precisely, we first formulate the centroid tracking problem as filtering on Lie groups. Then, we derive an iterated extended Kalman filter on Lie groups to perform the estimation.

In some multi-object tracking scenarios such as convoys or road constrained motions, it can be advantageous to track groups of targets sharing common motion characteristics, even if they are not necessarily close to each other. The objective is twofold: reducing the computational cost while increasing the accuracies of the individual trajectory estimates. In a previous communication, we introduced a generic model based on hierarchical random finite sets (RFSs) to represent these types of multigroup multi-target scenarios. A first RFS is used to represent the multi-group state: the number of groups, their common motion characteristics and their target compositions are assumed to be random variables. Then, for each group, a second layer of RFSs represents the multi-target state assuming that the number of targets inside a group and their trajectories are also random variables. The main contribution of this paper is to derive a filter dedicated to the state estimation of hierarchical RFSs from sequential sensor measurements. The proposed solution is based on the labeled multi-Bernoulli filter to estimate the group characteristics, which interacts with a bank of probability hypothesis density filters to address the multi-target layer.

In multi-object tracking, some target groups can share a coordinated motion. They can for instance form a convoy or follow a road network. In any case, the target trajectories can be modeled by using the group motion characteristics and the self-properties of the targets. For this purpose, we introduce a new model: a hierarchical random finite set (RFS). A first RFS is considered to represent the target groups. Their numbers, their compositions and their common motion characteristics are assumed to be random. Each group is itself represented as an RFS for which the target number and the target states such as the position and velocity are random. We also propose a metric to compare two hierarchical RFSs taking into account all the sources of uncertainties (the cardinality of both the groups and the targets within the groups and the unknown motion parameters).

BACKGROUND: In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data. RESULTS: We applied an experimental design that uses a serial dilution to generate known relative protein concentrations and estimated these concentrations by two processing algorithms, a classical and a more recent one. The extended method allowed estimating the variances explained by the dilution and the technical process by each algorithm in an experiment with 9 proteins: L-FABP, 14.3.3 sigma, Calgi, Def.A6, Villin, Calmo, I-FABP, Peroxi-5, and S100A14. Whereas, the recent algorithm gave a higher dilution variance and a lower technical variance than the classical one in two proteins with three peptides (L-FABP and Villin), there were no significant difference between the two algorithms on all proteins. CONCLUSIONS: The extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design.

BACKGROUND: Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks. RESULTS: We developed a new peak deconvolution procedure. The paper describes the method derivation and discusses some of its interpretations. The algorithm is then described in a pseudo-code form where the required optimization procedure is detailed. For synthetic data the method is compared to a more conventional approach. The new method reduces artifacts caused by the usual two-steps procedure, baseline removal then peak extraction. Finally some results on real linear MALDI-ToF spectra are provided. CONCLUSIONS: We introduced a new method for peak picking, where peak deconvolution and baseline computation are performed jointly. On simulated data we showed that this global approach performs better than a classical one where baseline and peaks are processed sequentially. A dedicated experiment has been conducted on real spectra. In this study a collection of spectra of spiked proteins were acquired and then analyzed. Better performances of the proposed method, in term of accuracy and reproductibility, have been observed and validated by an extended statistical analysis.

In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model impacts the trajectory estimation accuracy. To select an appropriate set of motion models, a joint tracking and classification (JTC) algorithm can be used. The principle is to consider different target classes depending on their maneuvrability, each of them being associated to a set of motion models. In this context, additional information such as a target length extent measurement can improve both classification and trajectory estimation. Therefore, we propose a multiple-model Bernoulli filter to perform JTC. To jointly estimate the trajectory and the target length which is constant, a Rao-Blackwellized approach is considered. Another contribution is that a bank of probabilistic data association filters is run instead of Kalman filters to account for false detections.

A generalized Swendsen-Wang (GSW) algorithm is proposed for the joint segmentation of a set of multiple images sharing, in part, an unknown number of common classes. The class labels are a priori modeled by a combination of the hierarchical Dirichlet process (HDP) and the Potts model. The HDP allows the number of regions in each image and classes to be automatically inferred while the Potts model ensures spatially consistent segmentations. Compared to a classical Gibbs sampler, the GSW ensures a better exploration of the posterior distribution of the labels. To avoid label switching issues, the best partition is estimated using the Dahl's criterion.

In this paper, we are interested in Visual Indoor Localization (VIL) for challenging video sequences coming from a single monocular camera where the person wearing the camera performs daily living activities (see Fig.1(a)). The difficulty of this problem resides in the fact that: i) handheld objects are frequently interposed between the camera and the environment; ii) strong motion blur and differences in illumination occur; iii) the environment changes between the images of the database and the video frames to localize, and the viewpoints can be significantly different. We wish to develop a method that: relies only on the images coming from the wearable camera, i.e no other sensor such as Inertial Measurement Units should be used estimates the camera position with a sub-meter level accuracy as well as its orientation is consistent with the topology of the environment, i.e the camera trajectory should not cross walls is able to detect when the data is not sufficient to disambiguate the situation, i.e when the posterior distribution of the camera trajectory is multimodal and/or too dispersed.

The paper presents a method for estimating the parameter of a Potts model jointly with the unknowns of an image segmentation problem. The method addresses piecewise constant images degraded by additive noise. The proposed solution follows a Bayesian approach, that yields the posterior law for all the unknowns (labels, gray levels, noise level and Potts parameter). It is explored by means of MCMC stochastic sampling, more precisely, by Gibbs algorithm. The estimates are then computed from these samples. The estimation of the Potts parameter is challenging due to the intractable normalizing constant of the model. The proposed solution is based on pre-computing the value of this normalizing constant for different image dimensions and number of classes, this being the novelty of this paper. The segmentation results are as satisfying as those obtained when tuning the parameter by hand.

Autoregressive (AR) and time-varying AR (TVAR) models are widely used in various applications, from speech processing to biomedical signal analysis. Various dissimilarity measures such as the Itakura divergence have been proposed to compare two AR models. However, they do not take into account the variances of the driving processes and only apply to stationary processes. More generally, the comparison between Gaussian processes is based on the Kullback-Leibler (KL) divergence but only asymptotic expressions are classically used. In this letter, we suggest analyzing the similarities of two TVAR models, sample after sample, by recursively computing the Jeffrey's divergence between the joint distributions of the successive values of each TVAR model. Then, we show that, under some assumptions, this divergence tends to the Itakura divergence in the stationary case.

The paper proposes a variable selection method for proteomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (1) we do not impose ad-hoc relationships between the variables such as a logistic regression model and (2) we account for instrumental variability through measurement noise. We are then dealing with indirect observations of a mixture of distributions and it results in intricate probability distributions. A closed-form expression of the posterior distributions cannot be derived. Thus, we discuss several approximations and study the robustness to the noise level. Finally, the method is evaluated both on simulated and clinical data.

In this paper, we are interested in estimating global motions (homographies, 3D rotations, 3D Euclidean motions, etc.) as well as the covariance of the estimation errors from relative measurements by exploiting the Lie group structure of the motions. We propose a generative model based on the formulation of a concentrated Gaussian distribution on matrix Lie groups. In this context, the global motion estimation problem reduces to the minimization of the sum of squared intrinsic invariant (w.r.t the right action of the Lie group on itself) errors. We derive an iterated extended Kalman filter on matrix Lie groups from the Gauss-Newton formalism on matrix Lie groups, which exhibits a low computational complexity. Experimental results on simulated data, in the context of a consistent pose registration problem, show that the proposed algorithm significantly outperforms the state of the art approaches.

The paper presents a variable selection method for biomarker discovery in proteomics. More specifically, it finds the most adequate variables among a given set in order to discriminate between two groups (healthy and pathological). This approach is developped within a Bayesian framework and relies on an optimal strategy that results in the choice of the most a posteriori probable model. The calculation of the posterior probabilities requiresmarginalization of unknown parameters. It is the main difficulty and a contribution of the paper is to provide a closed-form expression. The originality of the work is twofold: (1) we relax the standard hypothesis of linear regression models and (2) we present a multivariate test which directly accommodates possible correlations between the biomarkers. The effectiveness of the method is assessed through a simulated study and shows results in accordance with the theoritical optimality.

The performance of GPS is strongly degraded in a multipath environment. The multipath impact the distribution of the additive noise corrupting the distance measurements between the satellites and the GPS receiver. In this paper, this distribution is assumed unknown and modeled in a flexible way by using the Bayesian non parametric framework and more precisely the Dirichlet process mixtures. Nevertheless, these latter depend on the so-called scale parameter which can be difficult to tune a priori. The originality of our approach consists in adapting a recent version of the online EM algorithm, developed by Cappé for hidden Markov models, to compute a maximum a posteriori estimate of the scale parameter. Then, as the proposed model is non linear and non Gaussian, the EM-based scale parameter estimation is coupled with a Rao-Blackwellized particle filter for the joint estimation of the mobile location and the distance measurement noise distribution.

Today in GPS navigation, an accuracy from 5 to 10 m can be achieved, but performance can be strongly degraded in a multipath environment. Multipath can introduce large errors when measuring the distance between the satellites and the GPS receiver. They are commonly modeled by additive-measurement noise variance jumps affecting GPS measurements if there is a direct path between the satellites and the receiver and by additive-measurement noise mean-value jumps otherwise. If two signals from satellites have close directions of arrival, they are very likely to be simultaneously degraded by multipath. Therefore, in this letter we suggest taking into account the spatial dependencies between GPS measurements when modeling multipath occurrence/disappearance. For that purpose, we use a probabilistic tool, namely copulas. Then, as the proposed model is strongly nonlinear and non-Gaussian, we jointly estimate the mobile location and perform the multipath detection/estimation by using particle filtering.

In this paper, we propose a novel algorithm to detect/compensate on line interference effects when integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). The GNSS/INS coupling is usually performed by an Extended Kalman Filter (EKF) which yields an accurate and robust localization. How ever, interference cause the GNSS measurement noise to increase unexpectedly, hence degrade file positioning accuracy. In this context, our contribution is twofold. We first study the impact of the GNSS noise inflation on the covariance of the EKF outputs so as to compute a least square estimate of the potential variance jumps. Then, this estimation is used in a Bayesian test which decides whether interference are corrupting the GNSS signal or not. It al lows us to estimate their times of occurrence as well. In this way, the impaired measurements can be discarded while their impact on the navigation solution can be compensated. The results show the performance of the proposed approach on simulated data.

We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.

In this paper, we propose a novel algorithm for the detection of faulty measurements in Global Positioning System (GPS) navigation. In this context, satellite failures result in measurement biases which greatly impair positioning accuracy. Among the different algorithms developed to solve the navigation problem while detecting the biased measurements, the generalized likelihood ratio (GLR) offers a good compromise between accuracy and computational complexity. This algorithm not only estimates the times of occurrence of the failures but also computes a least square (LS) estimate of their amplitudes. However, due to interference or multipath, the GPS noise may not be exactly Gaussian distributed. Therefore, LS estimation may have poor accuracy. To overcome this difficulty, we propose to replace the LS estimator by an M-estimator in the GLR implementation. Such algorithms are well-known to be more robust to outliers.

This paper deals with the joint estimation of Rayleigh fading channels and symbols in a MC-DS-CDMA system. Formerly, particle filtering has been introduced as a set of promising methods to solve communication problems. PF consists in simulating possible values of the unkwnown parameters and selecting the most likely candidates with regard to the received signal. Here, the Rao-Blackwellized particle filter (RBPF) is used to significantly decrease the variance of the channel/symbol estimates. Our contribution is twofold. Firstly, sinusoidal stochastic models have been shown to better represent the statistical properties of Rayleigh channels than classical autoregressive models. Therefore, the proposed RBPF estimator is based on these models which are expressed as the sum of two sinusoids in quadrature at the maximum Doppler frequency with autoregressive processes as amplitudes. The model parameters are unknown and need to be estimated. Since PFs are not well-suited to recover non-varying parameters, we propose to cross-couple the RBPF with a Kalman filter which makes use of the RBPF ouputs to sequentially update the parameters. Secondly, the choice of an efficient proposal distribution to simulate the particles is crucial for PF performance. We suggest using a suboptimal distribution which simulates likely values of the symbols at a reasonable computational cost.

In civil aviation applications, satellite failures yield unacceptable positioning errors when using the Global Positioning System (GPS). To ensure the user security, the navigation system has to fulfill stringent performance requirements. Thus, detecting and excluding the faulty GPS measurements is necessary prior to estimating the mobile location. Classical fault detection algorithms based on Kalman filters (KF) are sensitive to the choice of an appropriate motion model for the mobile. To overcome this difficulty, we propose in this paper a new fault detection algorithm wherein the KF are replaced by multiple model algorithms. In this way, both the false alarm rate and the position mean square error are shown to be decreased.

Whereas lecturing is the most widely used mode of instruction, we have explored small-group learning projects to make signal processing more appealing at the University and in Engineering schools in Bordeaux (France). The projects cover a wide range of applications, from audio processing to mobile communication system analysis and can be based on problems suggested by industrial partners. After a state of the art in the area, the students develop signal processing algorithms and usually deliver new softwares. At the end of the semester, they provide a final 8-page report and present their work during a one-day workshop. The projects enable the students to experience cooperative works, which is mostly done in industry. In addition, they help the students to see more easily the links between the courses they follow, including project managing. The students are hence involved in dialog inside their own group, in writing English reports as well as in problem solving, analysis and synthesis. This paper presents four examples dealing with speech processing, spectral analysis, Universal Mobile Telecommunications System (UMTS) mobile positioning and orthogonal frequency division multiple access (OFDMA) synchronization.

In urban areas, multipath (MP) is one of the main error sources when tracking signals used in global navigation satellite systems. The received signals subjected to MP are the sum of several delayed replicas leading to biased estimations. This paper studies a sequential Monte Carlo (SMC) algorithm which mitigates MP effects. The proposed algorithm is based on a state-space model associated to a multicorrelator GPS receiver and on a Rao Blackwellized technique which allows to achieve good performance.

Multipaths cause major impairments to navigation with the Global Positioning System (GPS). Indeed, non-line-of-sight (NLOS) propagation is well known to bias GPS position estimates. A recent methodology has been proposed to overcome this limitation by estimating simultaneously the kinematic states and the multipath biases all along the observation interval. However, multipaths clearly occur relatively infrequently during time intervals of fixed duration. The paper studies a particle filtering algorithm for joint detection and estimation of multipath biases. A Rao-Blackwellized approach allows estimation of the kinematic states by extended Kalman filters, whereas multipath detection is achieved by an appropriate fixed lag particle filter.

The localization performance of a navigation system can be improved by coupling different types of sensors. The paper focuses on INS-GPS integration. INS and GPS measurements allow a non-linear state space model, which is appropriate to particle filtering, to be defined. This model being conditionally linear Gaussian, a Rao-Blackwellization procedure can be applied to reduce the variance of the estimates.