We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors.

This paper addresses the problem of demodulating signals transmitted in the automatic identification system. The main characteristics of such signals consist of two points: (i) they are modulated using a trellis-coded modulation, more precisely a Gaussian minimum shift keying modulation; and (ii) they are submitted to a bit stuffing procedure, which makes more difficult the detection of the transmitted information bits. This paper presents several demodulation algorithms developed in different contexts: mono-user and multi-user transmissions, and known/unknown phase shift. The proposed receiver uses the cyclic redundancy check (CRC) present in the automatic identification system signals for error correction and not for error detection only. By using this CRC, a particular Viterbi algorithm, on the basis of a so-called extended trellis, is developed. This trellis is defined by extended states composed of a trellis code state and a CRC state. Moreover, specific conditional transitions are defined to take into account the possible presence of stuffing bits. The algorithms proposed in the multi-user scenario present a small increase of computation complexity with respect to the mono-user algorithms. Some performance results are presented for several scenarios in the context of the automatic identification system and compared with those of existing techniques developed in similar scenarios.

In the signal theory framework the "analytic signal” is a particular polar representation of a signal f (t). It defines an envelope (a modulus) and a phase (an argument) and these parameters are basic in communications. In this paper, we show that amplitude demodulations generally change envelopes and reduced phases. Consequently we show that recent results in laser communications about these parameters are erroneous.

This paper derives new closed-form expressions for the masses of negative multinomial distributions. These masses can be maximized to determine the maximum likelihood estimator of its unknown parameters. An application to polarimetric image processing is investigated. We study the maximum likelihood estimators of the polarization degree of polarimetric images using different combinations of images.

In this article, we evaluate the impact of link layer retransmissions on the performance of TCP in the context of aeronautical communications.We present the architecture of aeronautical networks, which is manly driven by an important channel access delay, and the various retransmission strategies that can be implemented at both link and transport layers. We consider a worst case scenario to illustrate the benefits provided by the ARQ scheme at the link layer in terms of transmission delay.We evaluate the trade-off between allowing a fast data transmission and a low usage of satellite capacity by adjusting link layer parameters.

We present an NS-2 module, Physical Channel Access (PCA), to simulate different access methods on a link shared with Multi-Frequency Time Division Multiple Access (MF-TDMA). This technique is widely used in various network technologies, such as satellite communication. In this context, different access methods at the gateway induce different queuing delays and available capacities, which strongly impact transport layer performance. Depending on QoS requirements, design of new congestion and ow control mechanisms and/or access methods requires evaluation through simulations. PCA module emulates the delays that packets will experience using the shared link, based on descriptive parameters of lower layers characteristics. Though PCA has been developed with DVB-RCS2 considerations in mind (for which we present a use case), other MF-TDMA-based applications can easily be simulated by adapting input parameters. Moreover, the presented implementation details highlight the main methods that might need modifications to implement more specific functionality or emulate other similar access methods (e.g., OFDMA).

This paper addresses the problem of estimating the Potts parameter  jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on  requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method, the estimation of  is conducted using a likelihood-free Metropolis–Hastings algorithm. Experimental results obtained for synthetic data show that estimating  jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of . On the other hand, choosing an incorrect value of  can degrade estimation performance significantly. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images.

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.