Abstract
In this paper we introduce a new identification algorithm for MIMO bilinear systems driven by white noise inputs. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state space approximations, thus considered a Picard based method. The key to the algorithm is the fact that the bilinear terms behave like white noise processes. Using a linear Kalman filter, the bilinear terms can be estimated and combined with the system inputs at each iteration, leading to a linear system which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a bilinear model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.

Abstract
In this paper we introduce an identification algorithm for MIMO bilinear systems subject to deterministic inputs. The new algorithm is based on an expanding dimensions concept, leading to a rectangular, dimension varying, linear system. In this framework the observability, controllability, and Markov parameters are similar to those of a time-varying system. The fact that the system is time invariant, leads to an equaivaleet linear deterministic subspace algorithm. Provided a rank condition is satisfied, the algorithm will produce unbiased parameter estimates. This rank condition can be guaranteed to hold if the ratio of the number of outputs to the number of inputs is larger than the system order. This is due to the typical exponential blow-out in the dimensions of the Hankel data matrices of bilinear systems, in particular for deterministic inputs since part of the input subspace cannot be projected out. Other algorithms in the literature, based on Walsh functions, require that the number of outputs is at least equal to the system order. For ease of notation and clarification, the algorithm is presented as an intersection based subspace algorithm. Numerical results show that the algorithm reproduces the system parameters very well, provided the rank condition is satisfied. When the rank condition is not satisfied, the algorithm will return biased parameter estimates, which is a typical bottleneck of bilinear system identification algorithms for deterministic inputs. © 2005 IEEE.

Abstract
In this paper we describe the algorithms used in the external tracking system of the Isurus AUV. By listening to the acoustic signals exchanged between the vehicle and the beacons of the acoustic navigation network, the tracking system is able to obtain distance measurements from the vehicle to each beacon, that are then used to compute the vehicle horizontal position. Several error sources make these measurements inadequate to be used for computing the vehicle position by a simple triangulation technique. The tracking algorithms described here are able to reject highly erroneous measurements, producing position estimates with a satisfactory degree of accuracy. Copyright © 2004 IFAC

Abstract
The number of successful missions with autonomous underwater vehicles (AUVs) has been increasing steadily during the last few years, validating this emerging technology as an e cient tool for underwater sampling. At the same time it has served to envisage their operation in more demanding scenarios. This paper presents results from environment monitoring missions with an AUV and new mission concepts for the operation of one or multiple AUVs. These new concepts are the natural evolution of previous work and also the response to the requirements of some of the identi ed scenarios. For each type of mission, solutions for navigation, control, coordination, and communications are presented. © 2004 IFAC

Abstract
This work presents a hybrid maneuver for gradient search with multiple AUV's. The mission consists in following a gradient field in order to locate the source of a hydrothermal vent or underwater freshwater source. The formation gradient search exploits the environment structuring by the phenomena to be studied. The ingredients for coordination are the payload data collected by each vehicle and their knowledge of the behaviour of other vehicles and detected formation distortions. Copyright © 2004 IFAC. Copyright © 2004 IFAC

Abstract
Mean sea level is a variable of considerable interest in meteorological and oceanographic studies, particularly long-term sea level variation and its relation to climate changes. This study concerns the analysis of monthly mean sea level data from tide gauge stations in the Northeast Atlantic with long and continuous records. Much research effort on mean sea level studies has been focused on identifying long-term linear trends, usually estimated through least-squares fitting of a deterministic function. Here, we estimate nonparametric and robust trends using lowess, a robust smoothing procedure based on locally weighted regression. This approach is more flexible than a linear trend to describe the deterministic part of the variation in tide gauge records, which has a complex structure. A common trend pattern of reduced sea levels around 1975 is found in all the analysed records and interpreted as the result of hydrological and atmospheric forcing associated with drought conditions at the tide gauge sites. This feature is overlooked by a linear regression model. Moreover, nonlinear deterministic behaviour in the time series, such as the one identified, introduces a bias in linear trends determined from short and noisy records.