Dual-function radar communication (DFRC) systems have gained popularity because they integrate radar and communication functionalities into a single piece of hardware, enabling both functions to be performed simultaneously. Estimating the target parameters, specifically the direction of arrival, the range, and the velocity, is one of the main goals when dealing with DFRC systems. In this work, the target parameters are estimated independently using the popular Capon method with a multiple-input multiple-output DFRC system based on orthogonal frequency division multiplexing as the transmitted waveform. To this end, we show that a suitable waveform has to be designed to carry out the independent estimation of the range and the velocity and explain how to proceed for the a posteriori pairing of the estimations. The performance of the approach is compared with the modified Cramer-Rao bound. In addition, this method is compared with the existing subspace method and the variant of the Capon method proposed by Borgiotti-Lagunas.

In this paper, we propose to share our positive experience about the organization of the yearly European Defence Challenge, from the design to the execution. After a preliminary phase during which the students can follow various webinars, this challenge operates with the following two steps: during the first one, student teams submit a one-page note about a given topic related to the Defence domain. The proposals are evaluated by different experts in the field. Then, some student teams are selected to move on to the second phase of the challenge during which they develop their ideas in a 10-page proposal. Additional events punctuate the challenge, from a webinar at the beginning of the second phase to the award ceremony with invited speakers having different backgrounds, from industry to economics passing by politics. This challenge is hence a good way for students to define their professional plans by discovering a domain, its activities and its stakeholders. This is also a good opportunity to strengthen the synergies between the higher-education institutions and companies and more generally to facilitate the interactions between the actors of an ecosystem.

In this paper, we present the main steps of the methodology we followed to design and prototype a module dedicated to artificial intelligence (AI) for Defence for students at the level 6 in the European Qualifications Framework (EQF). Based on a top-down approach for the design taking into account the market needs, this module is based on seasonal schools, project-based learning and invited speakers coming from the local ecosystem sharing their experiences with AI. This combination aims at attracting students to engineering in the field of Defence. Finally, it should be noted that a part of this module can be done in a hybrid way.

Among the approaches used to jointly estimate the directions of arrival (DOAs) of K targets in a multiple-input multiple-output dual-function radar communication system, the method based on the identification of the K largest local maxima of the modulus of the Fourier transform (FT) of the signal received by the antennas has the advantage of having a low computational cost. However, the local maxima do not necessarily correspond to the values of interest. To avoid this problem, we present an operation mode making it possible to address the estimations of the DOAs separately. To this end, we propose to design a waveform reducing the magnitudes of the signals back-scattered by the targets except the one located in a specific zone. Then, this operation mode is extended to address the case of a received signal disturbed by an additive white Gaussian noise. Finally, simulation results confirm that this approach improves the standard approach based on the FT.

In this paper, we aim at analyzing the differences between three families of divergences used to compare probability density functions of Gaussian random vectors storing k consecutive samples of wide-sense stationary ARMA processes. There may be various applications: signal classification, statistical change detection, etc. Among the families that are studied, we propose to look at the α-divergence, the β-divergence and the γ-divergence. We first provide the expression of the divergences in the Gaussian case and then express their divergence increments, i.e. the differences between the divergences computed for k + 1 and k consecutive samples. Finally, we analyze how these divergence increments evolve when k increases and tends to infinity.

Proposed early in the 90ies, the detrended fluctuation analysis (DFA) can be used to estimate the Hurst exponent and has been proven relevant in various applications, from economics to biomedical. For the last years, variants have been proposed. They differ in the way to estimate the trend of the centered integrated signal. In this paper, we recall the main principles of some of these methods, provide explanations on the behaviours of the algorithms and analyze the relevance of new variants based on the Savitzky-Golay filter, also known as the LOESS approach, and the LOWESS. They bridge the gap between the DFA and the detrending moving average (DMA). We hence show that the LOESS-based method is a generalization of the DMA.

In this paper, we first propose to analyze the properties of the Kullback-Leibler divergence between wide-sense stationary random processes that can be modeled by ARMA or ARFIMA processes. This study makes it possible to introduce a new feature useful to compare two random processes and called "the asymptotic KL increment". The latter depends on various parameters such as the inverse filters associated to the random processes. An interpretation of "the asymptotic KL increment" is also given. Then, we propose to use it in order to compare the inter-beat intervals, which characterize the cardiac rhythm, when the subjects are either in a calm and soothing situation or under stress.

The purpose of this paper is to share our positive experience about the collaboration launched a few years ago between UTP (Panama) and Bordeaux INP (France) in the field of signal processing. This collaboration involves research and education activities. This has led to numerous internships of French students in Panama, mobilities of researchers, common research papers, and the 1stdouble diploma signed between France and a country of Central America. Thus, this paper presents the various aspects of the collaboration.

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).

This paper deals with model comparison based on the Jeffrey's divergence (JD). More particularly, after providing the JD between the joint distributions of k consecutive values of a white noise and the ones of a real moving-average or autoregressive model, the JD between real 1st-order MA and real 1st-order AR models is studied. Except when the 1stMA parameter is equal to 1, we show that, after a transient period, the JD between both models is incremented by a constant value that depends on the model parameters while k is incremented by 1. The JD is hence characterized by this increment and it is not necessary to consider a lot of samples.

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.

This paper deals with a phase-coded waveform which combines two binary phase codes, each impacting on specific properties of the radar receiving channel. After giving a detailed analysis of the expression of the received signal after processing when the Gaussian clutter is modeled by a pth-order autoregressive process, we focus our attention on the choice of the two phase codes: one aims at increasing the unambiguous range whereas the other is chosen by taking into account several criteria such as the detection performance. For this purpose, we suggest determining the Pareto fronts of 1st, 2ndand 3rdorders by means of an exhaustive search. Given the three Pareto fronts for different types of clutters simulated by making the AR parameters vary, we provide an automatic way to determine, before embedding it, the most robust phase codes using a fuzzy logic operator.

In the field of cognitive radio (CR), radio frequency (RF) transceivers must be efficient to save the terminal battery autonomy. Therefore, when designing the CR power amplifier (CR-PA), an obvious objective is to optimize efficiency over a large bandwidth. As a consequence, the CR-PA operates in its non-linear region and then frequency-dependent distortions are generated. This issue is all the more critical as one deals with high peak-to-average power ratio (PAPR) like those of OFDM signals. In this paper, we develop a digital post-distortion and detection technique in order to compensate the non-linearities generated by the CR-PA. It is based on a dynamic Volterra model to take into account the non-linear behavior of the CR-PA. The key feature of the proposed technique is the joint estimations of the model parameters and the CR-PA input samples. For this reason, an extended Kalman filter (EKF) is considered. However, as the model parameters can vary over time, several EKFs are combined by means of an interacting multiple model (IMM) algorithm. Simulation results confirm the relevance of the proposed postdistortion and detection technique.

This study deals with an interweave cognitive‐radio (CR) system, where an uplink orthogonal frequency‐division multiple access system is considered for the primary users (PUs). Our purpose is to estimate the PU carrier frequency offsets (PU‐CFOs) as well as the channels to estimate the transmitted symbols. However, in wideband wireless communications, the PU received‐signal spectrum usually exhibits a localised fading because of the multipath propagation channel. When the PU faded frequencies are used by the CR system, the PU system is contaminated by a CR narrowband interference (CR‐NBI). In that case, if a Kalman filter (KF)‐based approach is used for the estimations of the CFOs and the channels, the state‐space representation does not necessarily take into account the CR‐NBI; this hence has a negative impact on the algorithm performance. Therefore the authors propose to make this solution more robust to the CR‐NBI by detecting when it appears and disappears to avoid using the data disturbed by the CR‐NBI. The authors’ contribution is to assume that the CR‐NBI is because of the transmission of contiguous blocks and to propose various criteria based on the covariance matrix of the KF innovation.

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.

For a battery driven terminal, the power amplifier (PA) efficiency must be optimized. Consequently, non-linearities may appear at the PA output in the transmission chain. To compensate these distortions, one solution consists of using a digital detector based on a Volterra model of both the PA and the channel and a Kalman filter (KF) based algorithm to jointly estimate the Volterra kernels and the transmitted symbols. Here, we suggest addressing this issue when dealing with cognitive radio (CR). In this case, additional constraints must be taken into account. Since the CR terminal may switch from one sub-band to another, the PA non-linearities may vary over time. Therefore, we propose to design a digital detector based on an interacting multiple model combining various KF based estimators using different model parameter dynamics. This makes it possible to track the time variations of the Volterra kernels while keeping accurate estimates when those parameters are static. Furthermore, the single and multicarrier cases are addressed and validated by simulation results. Our solution corresponds to a compromise between computational cost and bit-error-rate performance.

Estimating the autoregressive parameters from noisy observations has been addressed by various authors for the last decades. Although several on-line or off-line approaches have been proposed when the additive noise is white, few papers deal with the additive moving average noise. In this paper, we suggest estimating the model parameters by using the prediction error method. Despite its high computational cost, the method has the advantage of being efficient in the Gaussian case. A comparative study with existing methods is then carried out and points out the efficiency of our approach especially when the number of samples is small.

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.

This paper deals with a system combining orthogonal frequency division multiplexing (OFDM) and interleave-division multiple access (IDMA). OFDM makes the system robust against intersymbol interference, whereas IDMA combats the multiple access interference. Nevertheless, two problems have to be solved: 1/ carrier frequency offset (CFO) must be estimated/corrected to guarantee the orthogonality between subcarriers. 2/ the conventional IDMA receiver requires a priori knowledge of the channel. Therefore, the CFO and the channel must be estimated. Our contribution is twofold. Firstly, we suggest a sigma point Kalman filter to solve the estimation issue. In addition, we propose a new scheme where a CFO correction is no longer necessary, unlike common OFDM approaches. We show that without a CFO correction, the transmitted bits can be recovered by modifying the conventional IDMA receiver. When considering an OFDM-IDMA network over Rayleigh fading channels, simulation results show the efficiency of the proposed algorithm.

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.

A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measurement noise, using standard least squares methods leads to a weight-estimation bias. In this paper, we propose to jointly estimate the TVAR parameters and the measurement-noise variance from noisy observations by means of a generalized eigenvalue decomposition. It extends to the TVAR case an off-line method that was initially proposed for AR parameter estimation from noisy observations. A comparative study is then carried out with existing methods such as the recursive errors-in-variable approach and Kalman based algorithms.

In Cognitive Radio (CR) systems, spectrum sensing plays a key role to determine the free frequency bands. However, when the primary-user (PU) signal spectrum exhibits localized fading, PU detection cannot be guaranteed. In addition, as the CR may use the PU faded frequencies, the PU spectrum can be disturbed by a narrow-band interference (NBI) and synchronization algorithms used for the PU carrier frequency offset (CFO) estimation suffer degradations. In this paper, we propose a new scheme that jointly allows the CR-NBI to be detected and the PU-CFOs and the channels to be estimated in an orthogonal frequency division multiple access (OFDMA) system. It combines a sigma point Kalman filter and a test aiming at detecting a variation of the measurement-noise covariance matrix. Simulation results confirm that the proposed algorithm can accurately detect the CR-NBI and estimate the PU-CFOs.

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.

Space time adaptive processing (STAP) is useful in radar processing to detect a target by filtering the clutter and the additive thermal noise. A derived version based on a multichannel autoregressive (M-AR) model of the clutter has the advantage of reducing the computational cost. Nevertheless, the estimation of the AR matrix parameters is a key issue because the clutter is not Gaussian in real cases. When dealing with an off-line solution, the multichannel least squares method (MLS) can be considered, but the estimation of the disturbance covariance matrix is required. In this paper, we suggest using the so-called fixed point method since it has “matrix- and texture-constant false alarm rate” property (matrix-CFAR and texture-CFAR) and it provides an unbiased and consistent estimate in a non-Gaussian case. A comparative study is then carried out between off-line M-AR based STAP methods and it points out the relevance of the solution we propose.

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.

AR modeling is used in a wide range of applications from speech processing to Rayleigh fading channel simulation. When the observations are disturbed by an additive white noise, the standard least squares estimation of the AR parameters is biased. Some authors of this paper recently reformulated this problem as an errors-in-variables (EIV) issue and proposed an off-line solution, which outperforms other existing methods. Nevertheless, its computational cost may be high. In this paper, we present a blind recursive EIV method that can be implemented for real-time applications. It has the advantage of converging faster than the noise-compensated LMS based solutions. In addition, unlike EKF or Sigma Point Kalman filter, it does not require a priori knowledge such as the variances of the driving process and the additive noise. The approach is first tested with synthetic data; then, its relevance is illustrated in the field of radar sea clutter rejection.

This letter deals with a new Rayleigh channel simulator. In many cases, the channel is modeled by an autoregressive process, but this choice is not well suited due to the band limitation of the theoretical Rayleigh channel power spectrum density. Therefore, we suggest using a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled by autoregressive processes. Unlike the autoregressive channel modeling, this simulator has the advantage of exhibiting the power spectrum density (PSD) peaks at the maximum Doppler frequency. Since estimating the autoregressive parameters of the amplitudes directly using a matrix approach from the theoretical Rayleigh channel autocorrelation leads to numerical errors, we propose two alternative methods using either the asymptotic behavior of the Bessel function or genetic algorithms.

In this paper, we propose a new pilot-aided channel estimator. Among the existing approaches, some are based on adaptive algorithms, but they are outperformed by methods where the channel is modeled by an AR or an ARMA process. In that case, estimating the model parameters from noisy observations and selecting the model orders are challenging problems. To avoid them, we propose to view the channel estimation as a realization issue. By taking advantage of the subspace methods for identification, the proposed estimator provides the system matrices in the state-space representation of the channel directly from the output observations. At that stage, the channel process can be estimated using a Kalman filter. This method has the advantage of being non-iterative and avoiding an a priori model for the channel.

In this paper, we propose a new Rayleigh channel simulator. Modeling the channel by an AR process leads to numerical problems due to the bandlimitation of the theoretical power density spectrum (PSD) of a Rayleigh channel. Therefore, we suggest modeling the channel by a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled as AR processes. To estimate the AR parameters of the amplitudes, we take advantage of the asymptotic behavior of the first-kind zero-order Bessel function. We show that unlike an AR channel modeling, this simulator has the advantage of exhibiting the PSD peaks at the maximum Doppler frequency, for any AR process order.

This letter deals with the identification of time-varying Rayleigh fading channels using a training sequence-based approach. When the fading channel is approximated by an autoregressive (AR) process, it can be estimated by means of Kalman filtering, for instance. However, this method requires the estimations of both the AR parameters and the noise variances in the state-space representation of the system. For this purpose, the existing noise compensated approaches could be considered, but they usually require a long observation window and do not necessarily provide reliable estimates when the signal-to-noise ratio is low. Therefore, we propose to view the channel identification as an errors-in-variables (EIV) issue. The method consists in searching the noise variances that enable specific noise compensated autocorrelation matrices of observations to be positive semidefinite. In addition, the AR parameters can be estimated from the null spaces of these matrices. Simulation results confirm the effectiveness of this approach, especially in presence of a high amount of noise.

The paper deals with the design of a receiver for synchronous multi-carrier direct-sequence code division multiple access (MC-DS-CDMA) systems, in time-varying frequency selective fading channels. We consider a structure operating in two steps; after channel compensation and time alignment along each carrier, the resulting signals are combined and define the input of the blind adaptive multiuser detector. Our contribution is twofold. First, we propose a generalization of the blind least mean squares (LMS) algorithm on the basis of multiple delayed input signal vectors, which has the advantage of improving the convergence features in a high multiple access interference (MAI) environment and time-varying fading scenario. In addition, we carry out a comparative study between the proposed algorithm and previously published blind LMS and blind Kalman filter algorithms. Simulation results show that the proposed algorithm can trade-off performance with complexity. In other words, it can have a better performance than the existing blind LMS algorithm and less computational complexity than the blind Kalman filter algorithm.

Among parametric methods for speech enhancement, one consists in combining an autoregressive model for speech and a Kalman filter. This filtering is optimal in the H/sub 2/ sense providing the initial state vector, the input and the observation vectors in the state space representation of the system are independent, white and Gaussian. However, these assumptions do not necessarily hold when processing speech. In this paper, we propose to investigate an alternative approach, which is based on H/sub /spl infin// filtering and hence does not depend on these restrictive assumptions. In that setting, the purpose is to minimize the worst possible effects of the noises and system uncertainties on the estimation error. A comparative study between Kalman and H/sub /spl infin// filtering is carried out, when the additive colored noise can be modeled by a moving average (MA) process.

In the framework of speech enhancement, many approaches have been developed when the speech signal is only corrupted by additive noise. However, in an auditorium, when echoes appear, spatial transformations between the sources and the microphones must be considered. For this reason, we propose to deal with speech contaminated by both convolutive and additive coloured noise. The two-microphone based noise canceller we present operates as follows: firstly, a prewhitening step is carried out. Secondly, the blind deconvolution method we use makes it possible to estimate the finite impulse responses (FIR), their orders and the variances of the additive noise, which is a great advantage. Then, the filtered versions of speech, estimated by means of a subspace method, are used to retrieve the original speech.

When a single sequence of noisy observations is available, the autoregressive (AR)-model based methods using Kalman-filter make it possible to enhance speech. However, the estimation of the AR parameters is required, but is still a challenging problem as the signal is corrupted by an additive noise. In this paper, we propose to both estimate the signal and the AR parameters by developing a recursive instrumental variable-based approach. Avoiding a non linear approach such as the EKF, this method involves two conditionally linked Kalman filters running in parallel. Once a new observation is available, the first filter uses the latest estimated AR parameters to estimate the signal, while the second filter uses the estimated signal to update the AR parameters. A comparative study between existing speech enhancement methods is completed.

We investigate various adaptive filters to design a minimum mean square error (MMSE) receiver, when considering asynchronous uplink multi-carrier direct-sequence code division multiple access (MC-DS-CDMA) systems, over a frequency selective fading channel. Two design structures are considered: the so called "separate detection" consists in using a particular adaptive filter structure for each carrier, whereas the so called "joint detection" is based on a joint structure defined by the concatenation of the adaptive filter weights dedicated to each carrier. We carry out a comparative study between both structures with various adaptive filters such as recursive least square (RLS), Normalized least mean square (NLMS) and affirm projection algorithm (APA). The simulation results show that a compromise has to be found between performance and computational cost. The so called "joint detection" scheme provides the best performance in terms of bit error rate (BER). Although NLMS has the lowest computational cost, it is outperformed by APA and RLS when considering the convergence rate or/and excess mean square error. Besides, the RLS provides the best performance but at the price of high computational complexity. Therefore, we propose to use APA in a joint structure to design the receiver.

Kalman algorithms have been widely applied, for instance in single-channel speech enhancement. However, when carrying out Kalman smoothing, computational cost and data storage requirements are two specific problems. A dual-filter-based smoother is proposed and used in the framework of speech enhancement. Our approach comprises a forward-in-time Kalman filter and a backward-in-time Kalman filter. Both filters are based on their respective forward-in-time linear prediction (LP) model and backward-in-time LP model. This method does not require as large a storage space as a standard Kalman smoother does. The algorithm is evaluated by considering a speech signal embedded in a white Gaussian noise. Simulation results show that the proposed algorithm provides a higher improvement of signal-to-noise ratio (SNR) than Kalman filtering.

This paper deals with Kalman filter-based enhancement of a speech signal contaminated by a white noise, using a single microphone system. Such a problem can be stated as a realization issue in the framework of identification. For such a purpose we propose to identify the state space model by using subspace non-iterative algorithms based on orthogonal projections. Unlike estimate-maximize (EM)-based algorithms, this approach provides, in a single iteration from noisy observations, the matrices related to state space model and the covariance matrices that are necessary to perform Kalman filtering. In addition no voice activity detector is required unlike existing methods. Both methods proposed here are compared with classical approaches.

A great deal of attention has been paid to speech enhancement using a single microphone system. The various approaches, based on the Kalman filter, operate in two steps: (1) the noise variances and the parameters of the speech model are estimated, and (2) the speech signal is retrieved using standard Kalman filtering. This letter presents an alternative solution that does not require the explicit estimation of the noise and the driving process variances. This deals with a new formulation of the approach proposed within a control literature framework by Mehra (1970).