Video transmission is sensitive to losses due to high compression efficiency. To tolerate the quality degradation from losses, Forward Error Correction (FEC) and error resilience schemes are commonly used. In this paper, we evaluate the performance of error tolerance schemes with the latest video coding standard, H.264/AVC. The analysis in three zones of packet loss rates (PLR) shows that no FEC scheme outperforms the others in a wide PLR range. We also compare the equal and unequal FEC schemes with the Flexible Macroblock Ordering (FMO) error resilience mechanism and find that FMO performs well in moving videos while FEC codes are better in rather static videos. Our results and analysis would give insights to design flexible applications which are able to adapt to the network dynamics.

Knowledge of radar scattering characteristics is very important to the development of radar detection technology on aircraft wake vortices. In this paper, the high range resolution (HRR) profile of the wake vortex is investigated. The HRR profile is observed to have a particular structure, from which the wingspan of the aircraft can be easily identified. This characteristic could be very useful to identify the wake vortex from the ambient air. At the same time, the Bragg scattering, whose characteristic and applicability are further explored, is used to explain the mechanism of such particular HRR profiles.

SpaceWire is a standard for on-board satellite networks chosen by the ESA as the basis for multiplexing payload and control traffic on future data-handling architectures. However, network designers need tools to ensure that the network is able to deliver critical messages on time. Current research fails to address this needs for SpaceWire networks. On one hand, many papers only seek to determine probabilistic results for end-to-end delays on Wormhole networks like SpaceWire. This does not provide sufficient guarantee for critical traffic. On the other hand, a few papers give methods to determine maximum latencies on wormhole networks that, unlike SpaceWire, have dedicated real-time mechanisms built-in. Thus, in this paper, we propose an appropriate method to compute an upper-bound on the worst-case end-to-end delay of a packet in a SpaceWire network.

The problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge is addressed, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized in a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the nonhomogeneity via an original relation. In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous. Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of a priori and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique.

In a broadcast system using the hierarchical modulation, the system delivers several streams with different waveforms and required Signal-to-Noise Ratio (SNR), typically SD- TV and HD-TV. At the application layer, each stream is delivered with a particular rate. The physical layer must be defined in order to optimize the protection of each stream with respect to the double constraints of both the data rates and the SNR thresholds. We show in this paper that a standard like DVB-SH is not always well adapted to meet these system constraints in operational typical cases. After exposing the current limitations of a classical hierarchical modulation approach, we present two possible adaptations to address these operational requirements and offer more flexibility in hierarchical modulation design.

Moving Target Indicators (MTI) are airborne radar systems designed to detect and track moving vehicles or aircrafts. In this paper, we address the problem of detecting hazardous collision targets to avoid them. One of the best known solutions to solve this problem is given by the so-called Space-Time Adaptive Processing (STAP) algorithms which optimally filter the target signal from interference and noise exploiting the specific relationship between Direction Of Arrival (DOA) and Doppler for the ground clutter. However, these algorithms require an antenna array and multiple reception channels that increase cost and complexity. The authors propose an alternative solution using a single antenna only. In addition to the standard Doppler shift related to the radial speed, the orthoradial speed of any target can be estimated if using a long integration time. Dangerous targets and ground clutter have different signatures in the radial- rthoradial velocity plane. An optimal detector is then proposed based on the oblique projection onto the signal subspace orthogonal to the clutter subspace. The theoretical performances of this detector are derived and a realistic radar scene simulation shows the benefits of this new MTI detector.

Monitoring some types of events can be very challenging depending on where data is collected. Hence, the use of in-situ wireless sensor networks combined with observing satellites can be very useful. These two technologies are often used to monitor systems separately but it is rare that data collected each one is used mutually to consolidate a view of the system state. We propose an architecture composed of WSN and LEO satellites. This architecture has to handle links' disruptions. In this study, the architecture relies on DTN. The main issue of this architecture is the routing within the whole network. The scenario we interest in is based on wildfires monitoring and detection. © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

The affine combination of two adaptive filters that simultaneously adapt on the same inputs has been actively investigated. In these structures, the filter outputs are linearly combined to yield a performance that is better than that of either filter. Various decision rules can be used to determine the time-varying parameter for combining the filter outputs. A recently proposed scheme based on the ratio of error powers of the two filters has been shown by simulation to achieve nearly optimum performance. The purpose of this paper is to present a first analysis of the statistical behavior of this error power scheme for white Gaussian inputs. Expressions are derived for the mean behavior of the combination parameter and for the adaptive weight mean-square deviation. Monte Carlo simulations show good to excellent agreement with the theoretical predictions.

Many sampling formulas are available for processes in baseband (-a,a) at the Nyquist rate a/π. However signals of telecommunications have power spectra which occupate two bands or more. We know that PNS (periodic non-uniform sampling) allow an errorless reconstruction at rate smaller than the Nyquist one. For instance PNS2 can be used in the two-bands case (-a,-b)∪(b,a) at the Landau rate (a-b)/π We prove a set of formulas which are available in cases more general than the PNS2. They take into account two sampling sequences which can be periodic or not and with same mean rate or not.

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.