Processing
Thursday 11th November 2010, 1500hrs–1600hrs
Chaired by Matthias Conrad Sensors Bias Estimation in Naval Multiplatforms Mrs Isabelle Sarbit, DCNS, France In naval multiplatforms multitargets context, the tactical situation assessment is usually performed assuming that input errors provided by sensor are Gaussian white noise with known standard deviation. Actually two kinds of additive bias must be taken into account: sensor measurement bias and navigation bias associated to platform location. In this paper we will consider that the two following hypothesis are satis ed all along the observation period: rst measurement bias and navigation bias are time-constant during the observation period, secondly every target is tracked by every sensor located on di erent platforms and the set of measurements acquired for those targets is available. We will present two di erent methods to estimate those biases based on linearised measurements and linear regression estimation. The rst method estimates the whole state vector (bias and target state), the second method performs only bias estimation regardless of target state estimation which makes Combat Management System implementation easier. Performance of both methods are evaluated by simulations. Target Motion Analysis (TMA) for Electronic Support Measure (ESM) Warfare: New functionality for Above Water Warfare (AWW): CMS-SETIS Product Range Mr Adrien Nègre, DCNS, France The work presented in this paper is a new functionality for our AWW CMS-SETIS product range which provides a Target Motion Analysis framework for ESM measures. Two modes are possible: localization or tracking with constraint. The first mode is fully automatic and corresponds to fixed targets. After an ESM track is selected by the operator, the algorithm provides the target location in the (x,y) plan centred on the ownship with its 95% confidence ellipse. The information delivered is refreshed at a frame rate chosen by the operator and a warning is set up in case of mismatch, i.e. if the target cannot be considered as fixed. The second mode, tracking, requires an input from the operator who has to fix a constraint either on the distance, velocity or heading angle of the target in the form of an interval. Note that a mix on two or three of these (d,V,c) constraints is also possible. The output of the algorithm is the target position with its velocity vector plotted along on the (x,y) plan. A range interval is also set around the estimated position, corresponding to the entered constraints. The underlying hypothesis on the target is that it evolves with a uniform motion. A flag is also set up if this hypothesis is no longer valid, i.e. when the target undergoes some manoeuvre. In this case, the algorithm is re-initialised. We will first briefly describe the algorithm used to obtain our results, present some results obtained on simulated data and show some picture of the designed MMI of this new functionality.
MAST timetable
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Diamond Industry Patron
Intensive networking outisde the conference sessions.
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Professor Manell Zakharia
Naval College, Brest, France
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