Increasing the coherent integration for acquisition of weak GNSS signals in urban and indoor environments is limited by several challenges. Besides of the possible sign reversal of the navigation data bit every 20ms, Doppler shift increases with the integration time. Inaccurate estimate of this offset may severely reduce the correlation power and prevent the receiver from detecting the presence of low-power signals. Standard acquisition approaches are not efficient for performing long coherent integrations. Furthermore, they achieve their limitations in high dynamics, where the signal frequency cannot be considered constant over the entire period of observation. In this paper, Frequency Offset Correction (FOC) loops are explored to compensate for the Doppler shift during the correlation process, thus enhancing stand-alone receivers’ sensitivity. The estimated frequency offset during the previous coherent integrations is used in a feedback scheme for self-aiding to extend the coherent integration time. We propose a new FOC loop architecture for compensating the frequency of each sample of the input signal. State of the art frequency compensation methods correct for the frequency drift on the post-correlation output for fine acquisition. At this stage, the correlation loss is already large enough to prevent the gain from accumulating longer signal.

Coastal altimetric waveforms may be corrupted by peaks. A simple parametric model was recently introduced to model peaky altimetric waveforms. This model assumes that the received altimetric waveform is the sum of a Brown echo and a Gaussian peak. This model has provided interesting results for symmetric peaks affecting altimetric signals. However, it is not appropriate for altimetric signals corrupted by asymmetric peaks. This paper introduces a Brown with asymmetric Gaussian peak model for altimetric waveforms. The parameters of this model are estimated by a maximum likelihood estimator. The performance of the proposed model and the resulting estimation strategy are evaluated via simulations conducted on synthetic and real data.

The delineation of P and T waves is important for the medical interpretation of ECG signals. We propose a Bayesian algorithm for simultaneous detection, delineation, and estimation of P and T waves. A block Gibbs sampler exploits the strong local dependencies in ECG signals by imposing block constraints on the P and T wave locations. The proposed algorithm is evaluated on the annotated QT database and compared with two classical algorithms.

In this paper, SAR processing algorithms for automotive applications are presented and illustrated on data from non-trivial test scenes. The chosen application is parking lot detection. Laboratory results obtained with a teaching sonar experiment emphasize the resolution improvement introduced with range-Doppler SAR processing. A similar improvement is then confirmed through full scale measurements performed with an automotive radar prototype operating at 77GHz in very close range conditions, typical of parking lot detection. The collected data allows a performance comparison between different SAR processing algorithms for realistic targets.

This paper introduces a new error correction strategy using cyclic redundancy checks (CRC) for a trellis coded system in the presence of bit stuffing. The proposed receiver is designed to simultaneously demodulate, decode and correct the received message in the presence of bit stuffing. It is based on a Viterbi algorithm exploiting the conditional transitions of an appropriate extended trellis. The receiver is evaluated with automatic identification system (AIS) messages constructed with a 16 bit CRC and a Gaussian Minimum Shift Keying (GMSK) modulation. The stuffed bits are inserted after any sequence of five consecutive bits 1 as requested by the AIS recommendation. Simulation results illustrate the algorithm performance in terms of packet error rate. A gain of more than 2,5 dB is obtained when compared to the conventional GMSK receiver.

The problem of detecting T-wave alternans (TWA) in ECG signals has received considerable attention in the biomedical community. This paper introduces a Bayesian model for the T waves contained in ECG signals. A block Gibbs sampler was recently studied to estimate the parameters of this Bayesian model (including wave locations, amplitudes and shapes). This paper shows that the samples generated by this Gibbs sampler can be used efficiently for TWA detection via different statistical tests constructed from odd and even T-wave amplitude samples. The proposed algorithm is evaluated on real ECG signals subjected to synthetic TWA and compared with two classical algorithms.

Space Wire is a wormhole network standard scheduled to be used as the sole on-board network for future satellites by the ESA. As the network will be shared by real-time and non real-time traffic, network designers require a tool to check that temporal constraints are verified for all the urgent messages. Previously, we proposed a model for a best-effort worm-hole network based on Network Calculus theory. This model is based on new network element that we call the Wormhole Section. Here, we show how to combine Wormhole Section elements to compute the end-to-end service curve offered to a flow and illustrate its use on a industrial case study.

Coastal altimetric waveforms may be corrupted by peaks. A simple parametric model was recently introduced to model peaky altimetric waveforms. This model assumes that the received altimetric waveform is the sum of a Brown echo and a Gaussian peak. This model has provided interesting results for symmetric peaks affecting altimetric signals. However, it is not appropriate for altimetric signals corrupted by asymmetric peaks. This paper studies a Brown with asymmetric Gaussian peak model for parameter estimation of altimetric waveforms. The parameter estimation problem is solved by a maximum likelihood estimator relying on an optimization algorithm. The performance of the proposed model and the resulting estimation strategy is evaluated via simulations conducted on synthetic and real data.

This paper proposes a spectral- and power- efficient code division multiplexing (CDM) scheme based on code shift keying (CSK) for the down-link of a communication system subject to additive Gaussian noise (e.g., the forward link of a satellite communication system). In a CSK system, each active user is assigned a set of sequences taken from Gold codes and their cyclic shifts. Spectral efficiency can be achieved by multiplexing several CSK modulators. We first define the corresponding Maximum-Likelihood (ML) decoder, showing that it can be efficiently implemented through a Sphere Decoding (SD) algorithm. An approximate performance analysis of the proposed scheme is given, and validated through numerical simulations, showing that larger spectral efficiencies also lead to increased power efficiencies.