The Establishment of Intelligent Detection Method and Monitoring System for Underwater Target Based on Imaging Sonar

For the practical requirement of underwater safety protection, the conception of target precautionary area is put forward combined with the technical characteristic of imaging sonar and the analysis of small underwater target imaging feature. And a detection method for underwater moving target based on image processing is build up, so that the intelligent detection and recognition of the underwater specific target is realized. Meanwhile, the intelligent detection and monitoring system of underwater target based on imaging sonar is designed and developed with the use of multi-level component-based architecture according to the practical application requirements. The system has obtained remarkable economic benefit in practical use and has good prospects for application

Improving Assimilation of SeaWiFS Data by the Application of Bias Correction with a Local SEIK Filter

hlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%

A Singular Evolutive Extended Kalman filter to assimilate ocean colour data in a coupled physical-biochemical model of the North Atlantic ocean

Within the European DIADEM project, a data assimilation system for coupled ocean circulation and marine ecosystem models has been implemented for the North Atlantic and the Nordic Seas. One objective of this project is to demonstrate the relevance of sophisticated methods to assimilate satellite data such as altimetry, surface temperature and ocean color, into realistic ocean models. In this paper, the singular evolutive extended Kalman (SEEK) filter, which is an advanced assimilation scheme where three-dimensional, multivariate error statistics are taken into account, is used to assimilate ocean color data into the biological component of the coupled system. The marine ecosystem model, derived from the FDM model [J. Mar. Res. 48 (1990) 591], includes 11 nitrogen and carbon compartments and describes the synthesis of organic matter in the euphotic zone, its consumption by animals of upper trophic levels, and the recycling of detritic material in the deep ocean. The circulation model coupled to the ecosystem is the Miami isopycnic coordinate ocean model (MICOM), which covers the Atlantic and the Arctic Oceans with an enhanced resolution in the North Atlantic basin. The model is forced with realistic ECMWF ocean/atmosphere fluxes, which permits to resolve the seasonal variability of the circulation and mixed layer properties. In the twin assimimation experiments reported here, the predictions of the coupled model are corrected every 10 days using pseudo-measurements of surface phytoplankton as a substitute to chlorophyll concentrations measured from space. The diagnostics of these experiments indicate that the assimilation is feasible with a reduced-order Kalman filter of small rank (of order 10) as long as a sufficiently good identification of the error structure is available. In addition, the control of non-observed quantities such as zooplankton and nitrate concentrations is made possible, owing to the multivariate nature of the analysis scheme. However, a too severe truncation of the error sub-space downgrades the propagation of surface information below the mixed layer. The reduction of the actual state vector to the surface layers is therefore investigated to improve the estimation process in the perspective of sea-viewing wide field-of-view sensor (SeaWiFS) data assimilation experiments

Sequential weak constraint parameter estimation in an ecosystem model

A Sequential Importance Resampling filter is applied to assimilate dataof the Bermuda Atlantic Time-series Study for the periodDecember 1988 to January 1994 into a 9-compartments ecosystemmodel. The filter provides an opportunity to combine state and parameterestimations. We detected notable seasonality of some model parameters.A filtered solution is in close agreement with the data and is superiorto that obtained with fixed model parameters. The seasonal dependenceof the initial slope of the P-I curve agrees with other known estimates.The seasonality of the phytoplankton specific mortality rate obtainedcan point out that either the phytoplankton mortality parameterizationhas to be improved or the Chl:C ratio varies in time. Being of the samecomputational cost as the Ensemble Kalman filter, the data assimilationapproach used can be implemented for on-line tuning and operationalprediction the ecosystem dynamics with a coupledhydrodynamical-ecosystem model