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Creators/Authors contains: "Deshpande, Vedang"

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  1. ; (, American Control Conference)
    null (Ed.)
    We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor con- figuration, optimal precision, and robustness in the presence of model uncertainty. The problem is formulated in the H∞ optimal observer design framework. We consider two types of uncertainties in the system, i.e. structured affine and un- structured uncertainties. The objective is to design an observer with a given H∞ performance index with minimal number of sensors and minimal precision values, while guaranteeing the performance for all admissible uncertainties. The problem is posed as a convex optimization problem subject to linear matrix inequalities. Numerical simulations demonstrate the application of the theoretical results presented in this work. 
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  2. ; (, IEEE American Control Conference)
    null (Ed.)
    In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving as noncooperative targets, is considered. The focus is on determining the optimal sensor precisions while simultaneously promoting sparseness in the sensor measurements to guarantee a specified estimation performance. The problem is formulated in the discrete-time centralized Kalman filtering framework. A semidefinite program subject to linear matrix inequalities is solved to minimize the trace of precision matrix which is defined to be the inverse of sensor noise covariance matrix. Simulation results expose a trade-off between sensor precisions and sensing frequency. 
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  3. ; (, IEEE Control Systems Letters)
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  4. ; ; (, Journal of Computational Physics)
    null (Ed.)
    Full Text Available 
  5. ; ; (, IEEE Control Systems Letters)
    Full Text Available 
  6. ; (, IFAC World Congress)
    Accepted Manuscript Full Text Available
  7. ; (, American Control Conference)
    Full Text Available 
  8. ; ; ; (, SciTech 2020 Forum)
    null (Ed.)
    We discuss a novel method to train a neural network from noisy data, using Optimal Transport based filtering. We show a comparative study of this methodology with three other filters: the Extended Kalman filter, the Ensemble Kalman filter, and the Unscented Kalman filter, that can also be used for the purpose of training a neural network. We empirically establish that Optimal Transport based filter performs better than the other three filters with respect to root mean square error measure, for non-Gaussian noise in the output. We demonstrate the efficacy of utilizing the Optimal Transport based filtering for neural network training in the context of predicting Mackey-Glass chaotic time series data. 
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