Delay/Doppler radar altimetry has been receiving an increasing interest, especially since the launch of the first altimeter in 2010 . It aims at reducing the measurement noise and increasing the along-track resolution in comparison with conventional pulse limited altimetry. A semi-analytical model was recently introduced for this new generation of delay/Doppler altimeters. The first contribution of this paper is the derivation of the Cram´er-Rao bounds (CRBs) associated with the parameters of this recent delay/Doppler model. These bounds are then compared with those obtained for conventional altimetry. The second contribution of this paper is the derivation of a new weighted least squares estimator based on the semianalytical delay/Doppler model. The performance of this estimator is very promising when compared to other more classical estimators and to the corresponding CRBs.

The concept of delay/Doppler radar altimeter has been under study since the mid 90’s, aiming at reducing the measurement noise (by increasing the number of observations) and increasing the along-track resolution (by using the information of the Doppler frequency) in comparison with the conventional pulse limited altimeters. This paper introduces a semi-analytical model for delay/Doppler altimetry. The performance of the proposed model and the resulting estimation strategy are evaluated via simulations conducted on synthetic and real data. A comparison between conventional altimetry and delay/Doppler altimetry provides very promising results in favor of the proposed model.

Spectral analysis along with the detection of harmonics and modulation sidebands are key elements in condition monitoring systems. Several spectral analysis tools are already able to detect spectral components present in a signal. The challenge is therefore to complete this spectral analysis with a method able to identify harmonic series and modulation sidebands. Compared to the state of the art, the method proposed takes the uncertainty of the frequency estimation into account. The identification is automatically done without any a priori, the search of harmonics is exhaustive and moreover the identification of all the modulation sidebands of each harmonic is done regardless of their energy level. The identified series are characterized by criteria which reflect their relevance and which allow the association of series in families, characteristic of a same physical process. This method is applied on real-world current and vibration data, more or less rich in their spectral content. The identification of sidebands is a strong indicator of failures in mechanical systems. The detection and tracking of these modulations from a very low energy level is an asset for earlier detection of the failure. The proposed method is validated by comparison with expert diagnosis in the concerned fields.

Vibration data acquired during system monitoring periods are rich in harmonics characterizing the presence of several mechanical parts in the system. Periodic variations of the torque or of the load create modulation sidebands around those harmonics. Even if the energy impact of the sidebands is small compared to the total energy of the signal, they are strong indicators of failures in mechanical systems. Unfortunately, these effects are of little concern in most condition monitoring systems. When considering the problem from a signal processing point of view, the demodulation of those sidebands allows for a time visualization of the modulating functions which are a precise image of the torque or the load variations. This demodulation can be done on the analytical signal directly derived from the original data. But to do that, data and specifically its spectrum should respect some constraints. The purpose of this paper is to underline those often neglected constraints. In particular, the respect of the non-overlapping condition in the Bedrosian theorem is discussed for signals and modulation rates that can be encountered on rotating machines. The respect of the constraints depends on the monitored phenomenon (e.g., gear mesh, rotating shaft), the modulation phenomenon (e.g., belt frequency, rotor current) and the type of medium (e.g., vibrations, electrical current). In the case where the constraints are not satisfied, we explain the consequences in terms of signal processing. These results are illustrated by an industrial case study.

The concept of delay/Doppler radar altimeter has been under study since the mid 90’s, aiming at reducing the measurement noise and increasing the along-track resolution in comparison with the conventional pulse limited altimeters. This paper introduces an analytical model of the mean backscattered power waveform acquired by a radar altimeter operating in SAR mode, as well as an associated least squares estimation algorithm. As for conventional altimetry, the mean power can be expressed as the convolution of three terms: the flat sea surface response, the sea wave height probability density function and the time/frequency impulse response of the radar. An important contribution of our work has been to derive an analytical formula for the flat sea surface response associated with Doppler altimetry. The double convolution defining the mean power can then be computed numerically. The resulting single-look model depends on three parameters: the epoch, the sea surface wave height and the amplitude. A multi-look model is obtained by summing all the reflected power from the along track beam surface of interest after applying appropriate delay compensation. The second contribution of our work concerns the estimation of the parameters associated with the single and multi-look analytical Doppler models. A least squares approach is investigated by means of the Levenberg-Marquardt algorithm (which does not need computing the exact model derivatives). Simulations conducted on synthetic altimetric waveforms allow the performance of the proposed estimation algorithm to be appreciated. The proposed analytical model (and the associated estimation algorithm) are finally compared with other models developed by the Centre National d'Etudes Spatiales (CNES) and the company Collecte Localisation Satellites (CLS) both located in Toulouse, France. The analysis of a huge number of Cryosat waveforms shows an improvement in parameter estimation when compared to the conventional LRM mode altimeter. These results are very promising.

The delineation of P and T waves is important for the interpretation of ECG signals. In this work, we propose a sequential Bayesian detection-estimation algorithm for simultaneous P and T wave detection, delineation, and waveform estimation on a beat-to-beat basis. Our method is based on a dynamic model which exploits the sequential nature of the ECG by introducing a random walk model to the waveforms. The core of the method is a marginalized particle filter that efficiently resolves the unknown parameters of the dynamic model. The proposed algorithm is evaluated on the annotated QT database and compared with state-of-the-art methods. Its on-line characteristic is ideally suited for real-time ECG monitoring and arrhythmia analysis.

Global aircraft optimization is a main concern for future and upcoming programs. In particular, great research efforts are dedicated to Electrical Flight Control Systems (EFCS). Obviously, their reliability increases with the redundancy of the flight parameter sensors. However, physical redundancy, obtained by increasing the number of sensors, penalizes the aircraft weight and cost. This paper proposes a sensor failure detection method based on analytic redundancy. The flight parameter of interest is modelled as a linear function of independent sensor measurements on a sliding observation window. The Partial Least Squares (PLS) algorithm is used to estimate regression coefficients on this window. The PLS computes the solution via an iterative processing, and thus can be implemented in the flight control computer for a real time use. Two different failure detection strategies based on the behaviour of the regression coefficients are proposed. Simulation results show that the proposed method leads to robust detections.

Electrical Flight Control Systems (EFCS) now constitute an industrial standard for commercial applications, ensuring a more sophisticated control of the aircraft and flight envelope protection functions. In the scope of a global optimization towards extended system availability and more easy-to-handle aircraft, sophisticated tools are necessary to diagnose sensor behaviour and to prevent from faulty measurements. In this context, a signal processing approach using Partial Least Squares (PLS) has been proposed to increase the EFCS autonomy. This algorithm establishes a linear relation between observed flight parameters - issued from possibly faulty sensors - and independent sensor measurements thanks to an iterative process. A monitoring strategy based on the regression coefficient dynamics, provided by the PLS, has been implemented and tested on real flight data and through an Airbus high-fidelity simulator, including realistic failure scenarios. The simulation results demonstrate the method performance robustness, in compliance with current real-time constraints. This paper focuses on the application of the method to the industrial context. A special attention is dedicated to the simulation process and to the performance analysis.

The development of robust ECG denoising techniques is important for automatic diagnoses of cardiac diseases. Based on a previously suggested nonlinear dynamic model for the generation of realistic synthetic ECG, we introduce a modified ECG dynamical model with 18 state variables to further include morphology variations. A marginalized particle filter is proposed for tracking this modified nonlinear state-space model which has linear substructures. Quantitative evaluations on the MIT-BIH database show that the proposed algorithm outperforms the extended Kalman filter-based algorithms and can better handle non-Gaussian distributions.

In the framework of the aircraft global optimization, for future and upcoming programs, the main objective of current research is to increase aircraft maneuverability and thus Electrical Flight Control System (EFCS) autonomy. A possible solution is to increase the number of redundant flight parameter sensors. This paper proposes an algorithm using PLS (Partial Least Squares) to estimate a flight parameter from independent sensor measurements. The estimates are then used as so-called “software” or “virtual” sensors. This algorithm is based on an iterative processing and thus can be used in real time in the embedded flight computer. Furthermore, the resulting flight parameter estimates can be used to detect failures. In that way, different detection strategies are proposed and results show that this method can lead to robust detections.