A new approach to maneuvring target tracking in passive multisensor environment

International audienceThis paper present a new approach to the multisensor Bearing-Only Tracking applications (BOT). Usually, a centralized data fusion scheme which involves a stacked vector of all the sensor measurements is applied using a single estimation filter which copes with the non-linear relation between the states and the measurements. The aforementioned approach is asymptotically optimal but suffers from the computational burden due to the augmented measurement vector and transmission aleas like delays generated by the bottleneck that occurs at the fusion center. Alternatively, since the Cartesian target positions can be determined by fusing at least 2 infrared sensor measurements in 2D case, one can use a local linear filter to estimate the target motion parameters, then a state fusion formula based on the Likelihood of the expected overall local measurements is applied to obtain the global estimate. The simulation results show that the proposed approach performance is equivalent to the centralized fusion schema in terms of tracking accuracy but exhibits the advantages of the decentralized fusion schema like parallel processing architecture and robustness against transmission delays. In addition, the low complexity of the obtained algorithm is well suited for real-time applications

A new approach in distributed multisensor tracking systems based on Kalman filter methods

International audienceIn multisensor tracking systems, the state fusion also known as track to track fusion is a crucial issue where the derivation of the best track combination is obtained according to a stochastic criteria in a minimum variance sense. Recently, sub-optimal weighted combination fusion algorithms involving matrices and scalars were developed. However, hence they only depend on the initial parameters of the system motion model and noise characteristics, these techniques are not robust against erroneous measures and unstable environment. To overcome this drawbacks, this work introduces a new approach to the optimal decentralized state fusion that copes with erroneous observations and system shortcomings. The simulations results show the effectiveness of the proposed approach. Moreover, the reduced complexity of the designed algorithm is well suited for real-time implementation

Estimation of amplitude, phase and unbalance parameters in three phase systems: analytical solutions, efficient implementation and performance analysis

International audienceThis paper focuses on the estimation of the instantaneous amplitude, phase and unbalance parameters in three-phase power systems. Due to the particular structure of three-phase systems, we demonstrate that the Maximum Likelihood Estimates (MLEs) of the unknown parameters have simple closed form expressions and can be easily implemented without matrix algebra libraries. We also derive and analyse the Cram'er-Rao Bounds (CRBs) for the considered estimation problem. The performance of the proposed approach is evaluated using synthetic signals compliant with the IEEE Standard C37.118. Simulation results show that the proposed estimators outperform other techniques and reach the CRB under certain condition

Blind system identification using cross-relation methods : further results and developments

International audienceWe consider the problem of blind identification of FIR systems using the cross-relations (CR) method first introduced in [1]. Our contribution in this paper are as follows: (i) We introduce an extended formulation of the CR identification criterion which generalizes the standard CR criterion used in [2]. It can be shown that many existing multichannel blind identification methods belong to the class of generalized CR methods. (ii) We introduce a new identification method referred to as Minimum Cross-Relations (MCR) method which exploits with minimum redundancy the spatial diversity among the channel outputs. Simulation-based performance analysis of the MCR method and comparisons with CR method are also presented. (iii) Then, we present a modified version of the MCR referred to as the "unbiased MCR" (UMCR) method that leads to unbiased estimation of the channel parameters and better estimation performances without need of noise whitening as in the MCR. (iv) Finally, we discuss the multi-input case and show how additional difficulties arise due to the non-linear parameterization of the noise vectors in terms of the channel parameters

Sparsity-Based Algorithms for Blind Separation of Convolutive Mixtures with Application to EMG Signals

International audienceIn this paper we propose two iterative algorithms for the blind separation of convolutive mixtures of sparse signals. The first one, called Iterative Sparse Blind Separation (ISBS), minimizes a sparsity cost function using an approximate Newton technique. The second algorithm, referred to as Givens-based Sparse Blind Separation (GSBS) computes the separation matrix as a product of a whitening matrix and a unitary matrix estimated, via a Jacobi-like process, as the product of Givens rotations which minimize the sparsity cost function. The two sparsity based algorithms show significantly improved performance with respect to the time coherence based SOBI algorithm as illustrated by the simulation results and comparative study provided at the end of the paper

Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

International audienceThis paper considers the blind separation of non-stationary sources in the underdetermined case, when there are more sources than sensors. A general framework for this problem is to work on sources that are sparse in some signal representation domain. Recently, two methods have been proposed with respect to the time-frequency (TF) domain. The first uses quadratic time-frequency distributions (TFDs) and a clustering approach, and the second uses a linear TFD. Both of these methods assume that the sources are disjoint in the TF domain; i.e. there is at most one source present at a point in the TF domain. In this paper, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present at a point is strictly less than the number of sensors. The separation can still be achieved thanks to subspace projection that allows us to identify the sources present and to estimate their corresponding TFD values. In particular, we propose two subspace-based algorithms for TF-nondisjoint sources, one uses quadratic TFDs and the other a linear TFD. Another contribution of this paper is a new estimation procedure for the mixing matrix. Finally, then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones

Maximum Likelihood Source Separation By the Expectation-Maximization Technique: Deterministic and Stochastic Implementation.

This paper deals with the source separation problem which consists in the separation of a mixture of independent sources without a priori knowledge on the mixing matrix. When the source distributions are known in advance, this problem can be solved via the Maximum Likelihood (ML) approach by maximizing the data likelihood function using (i) the ExpectationMaximization (EM) algorithm and (ii) a stochastic version of it, the SEM. Two important features of our algorithm are that (a) the covariance of the additive noise can be estimated as a regular parameter, (b) in the case of discrete sources, it is possible to separate more sources than sensors. The effectiveness of this method is illustrated by numerical simulations. I. Introduction When an array of m sensors samples the fields radiated by n narrow band sources its output is classically modeled as an instantaneous spatial mixture of a random vector made of m one-dimensional components, possibly corrupted by additive noise. The sourc..