S. Andrew Gadsden

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Target tracking algorithms are important for a number of applications, including: physics, air traffic control, ground vehicle monitoring, and processing medical images. The probabilistic data association algorithm, in conjunction with the Kalman filter (KF), is one of the most popular and wellstudied strategies. The relatively new smooth variable structure(More)
In this paper, an augmented form of the smooth variable structure filter (SVSF) is proposed. The SVSF is a state estimation strategy based on variable structure and sliding mode concepts. It uses a smoothing boundary to remove chattering (excessive switching along an estimated state trajectory). In its current form, the SVSF defines the boundary layer by an(More)
A multilayered neural network is a multi-input, multi-output (MIMO) nonlinear system in which training can be regarded as a nonlinear parameter estimation problem by estimating the network weights. In this paper, the relatively new smooth variable structure filter (SVSF) is used for the training of a nonlinear multilayered feed forward network. The SVSF is(More)
The smooth variable structure filter (SVSF) is a recently proposed method that is used for estimation purposes, such as fault detection [1-2]. The SVSF demonstrates good results and robustness when it is applied to linear and nonlinear systems that are fully measured. However, the results differ when some of the states are not measured. In this case, the(More)