46 citations · 137 across the 35 of their papers we have counts for
14 papers · 1 filter
Sparse Sensing and Optimal Precision: Robust Optimal Observer Design with Model Uncertainty
Vedang M. Deshpande, Raktim Bhattacharya
We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model u…
A Lagrangian Method for Constrained Dynamics in Tensegrity Systems with Compressible Bars
Shao-Chen Hsu, Vaishnav Tadiparthi, Raktim Bhattacharya
This paper presents a Lagrangian approach to simulating multibody dynamics in a tensegrity framework with an ability to tackle holonomic constraint violations in an energy-preservi…
Modeling and Optimal Control of Hybrid UAVs with Wind Disturbance
Sunsoo Kim, Niladri Das, Raktim Bhattacharya
This paper addresses modeling and control of a six-degree-of-freedom unmanned aerial vehicle capable of vertical take-off and landing in the presence of wind disturbances. We desig…
Utility and Privacy in Object Tracking from Video Stream using Kalman Filter
Niladri Das, Raktim Bhattacharya
Tracking objects in Computer Vision is a hard problem. Privacy and utility concerns adds an extra layer of complexity over this problem. In this work we consider the problem of mai…
Computationally Efficient Attitude Estimation with Extended Filtering
Sunsoo Kim, Vaishnav Tadiparthi, Raktim Bhattacharya
Accurate state estimation using low-cost MEMS (Micro Electro- Mechanical Systems) sensors present on Commercial-off-the-shelf (COTS) drones is a challenging problem. Most UAV syste…
Data-driven Solution of Stochastic Differential Equations Using Maximum Entropy Basis Functions
Vedang M. Deshpande, Raktim Bhattacharya
In this paper we present a data-driven approach for uncertainty propagation. In particular, we consider stochastic differential equations with parametric uncertainty. Solution of t…
Robust LQR for Uncertain Discrete-Time Systems using Polynomial Chaos
Vaishnav Tadiparthi, Raktim Bhattacharya
In this paper, a polynomial chaos based framework for designing controllers for discrete time linear systems with probabilistic parameters is presented. Conditions for exponential-…
Nonlinear Attitude Estimation for Small UAVs with Low Power Microprocessors
Sunsoo Kim, Vaishnav Tadiparthi, Raktim Bhattacharya
Among algorithms used for sensor fusion for attitude estimation in unmanned aerial vehicles, the Extended Kalman Filter (EKF) is the most commonly used for estimation. In this pape…
H2 Optimized PID Control of Quad-Copter Platform with Wind Disturbance
Sunsoo Kim, Vedang Deshpande, Raktim Bhattacharya
Proportional-Integral-Derivative (PID) scheme is the most commonly used algorithm for designing the controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a…
Optimal Transport Based Filtering with Nonlinear State Equality Constraints
Niladri Das, Raktim Bhattacharya
In this work we propose a framework to address the issue of state dependent nonlinear equality-constrained state estimation using Bayesian filtering. This framework is constructed…
Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering
Niladri Das, Raktim Bhattacharya
We consider the problem of selecting an optimal set of sensor precisions to estimate the states of a non-linear dynamical system using an Ensemble Kalman filter and an Unscented Ka…
Kalman Filtering with Probabilistic Uncertainty in System Parameters
Sunsoo Kim, Vedang M. Deshpande, Raktim Bhattacharya
In this paper, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete time systems,…
Sparse Sensing and Optimal Precision: An Integrated Framework for Optimal Observer Design
Vedang M. Deshpande, Raktim Bhattacharya
In this paper, we simultaneously determine the optimal sensor precision and the observer gain, which achieves the specified accuracy in the state estimates. Along with the unknown…
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates
Niladri Das, Raktim Bhattacharya
In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, w…