TOUATI Nadjah, TATKEU Charles, CHONAVEL Thierry, RIVENK Atika

**Design and performances evaluation of new costas-based radar waveforms with pulse coding diversity**. IET radar, sonar and navigation, june 2016, vol. 10, n° 5, pp. 877-891*Costas codes are a variant of pulse compression waveforms, largely studied for their attractive time-frequency properties. Their ‘thumbtack-like' ambiguity function (AF) makes them highly suitable for delay and Doppler estimation, in radar and sonar applications. However, this behaviour depends heavily on the length of the code: the improvement in delay-Doppler resolutions and AF sidelobes level needs an increase in the size of the code. In this study, designs that allow good performance without increasing the size of the code are proposed. They are based on a modification of Costas codes by widening frequency separation between hops and replacing rectangular pulses by other waveforms. This will lead to a removal of autocorrelation function grating lobes that normally appear when frequency separation is increased. The originality of the work lies in the proposal of diversified pulse waveforms, such as phase codes, Slepian sequences, and other Costas codes, to encode main Costas pulses. A performance comparison of the proposed approaches is supplied. Such waveforms could also be of interest for applications where waveform diversity is desired. *

HANNART Alexis, CARRASSI Alberto, BOCQUET Marc, GHIL Michael, NAVEAU Philippe, PULIDO Manuel, RUIZ Juan, TANDEO Pierre

**DADA: Data Assimilation for the Detection and Attribution of weather- and climate-related events**. Climatic Change, may 2016, vol. 136, n° 2, pp. 155-174*We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a "data assimilation-based detection and attribution" (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers. *

LE GALL Yann, DOSSO Stan, SOCHELEAU François-Xavier, BONNEL Julien

**Bayesian source localization with uncertain Green's function in an uncertain shallow water ocean**. Journal of the Acoustical Society of America, march 2016, vol. 139, n° 3, pp. 993*Matched-field acoustic source localization is a challenging task when environmental properties of the oceanic waveguide are not precisely known. Errors in the assumed environment (mismatch) can cause severe degradations in localization performance. This paper develops a Bayesian approach to improve robustness to environmental mismatch by considering the waveguide Green's function to be an uncertain random vector whose probability density accounts for environmental uncertainty. The posterior probability density is integrated over the Green's function probability density to obtain a joint marginal probability distribution for source range and depth, accounting for environmental uncertainty and quantifying localization uncertainty. Because brute-force integration in high dimensions can be costly, an efficient method is developed in which the multi-dimensional Green's function integration is approximated by one-dimensional integration over a suitably defined correlation measure. An approach to approximate the Green's function covariance matrix, which represents the environmental mismatch, is developed based on modal analysis. Examples are presented to illustrate the method and Monte-Carlo simulations are carried out to evaluate its performance relative to other methods. The proposed method gives efficient, reliable source localization and uncertainties with improved robustness toward environmental mismatch. *