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

Phasor estimation using conditional maximum likelihood: Strengths and limitations

International audienceThis paper focuses on the estimation of the phasor parameters in three-phase power systems for smart grid monitoring. Specifically, it investigates the use of the Conditional Maximum Likelihood (ML) for phasor parameter estimation. The contribution of this paper is twofold. First, it presents the condition on the signal model for identifiability of the phasor parameters. Then, it shows that the Conditional Maximum Likelihood estimator has a simple closed form expression, which can be determined from simple geometrical properties. Simulation results illustrate the effectiveness of the proposed approach for the estimation of the phasor amplitude and angle shift under dynamic conditions

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

An analysis of the elastic properties of a porous aluminium oxide film by means of indentation techniques

The elastic modulus of thin films can be directly determined by instrumented indentation when the indenter penetration does not exceed a fraction of the film thickness, depending on the mechanical properties of both film and substrate. When it is not possible, application of models for separating the contribution of the substrate is necessary. In this work, the robustness of several models is analyzed in the case of the elastic modulus determination of a porous aluminium oxide film produced by anodization of an aluminium alloy. Instrumented indentation tests employing a Berkovich indenter were performe data nanometric scale, which allowed a direct determination of the film elastic modulus, whose value was found to be approximately 11 GPa. However, at a micrometric scale the elastic modulus tends toward the value corresponding to the substrate, of approximately 73 GPa. The objective of the present work is to apply different models for testing their consistency over the complete set of indentation data obtained from both classical tests in microindentation and the continuous stiffness measurement mode in nanoindentation. This approach shows the continuity between the two scales of measurement thus allowing a better representation of the elastic modulus variation between two limits corresponding to the substrate and film elastic moduli. Gao's function proved to be the best to represen the elastic modulus variation

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

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

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