By Professor Andrew Dempster

Preprints

Xie R; Dempster AG, 2022, Efficient low-thrust trajectory data generation based on generative adversarial network, http://dx.doi.org/10.48550/arxiv.2209.06427

Ihsan U; Wang Z; Malaney R; Dempster A; Yan S, 2019, Artificial Intelligence and Location Verification in Vehicular Networks, http://dx.doi.org/10.1109/GLOBECOM38437.2019.9014171

Cheong JW; Dempster AG, 2018, Cross Correlation-based Direct Positioning for Wideband Sources using Phased Arrays, http://arxiv.org/abs/1809.04207v1

Biswas S; Qiao L; Dempster A, 2016, Computationally Efficient Unscented Kalman Filtering Techniques for Launch Vehicle Navigation using a Space-borne GPS Receiver, http://arxiv.org/abs/1611.09701v1

Biswas S; Qiao L; Dempster A, 2016, A Novel a priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency, http://dx.doi.org/10.1109/TAC.2016.2599291

Tek FB; Dempster AG; Kale İ, 2016, Adaptive Gray World-Based Color Normalization of Thin Blood Film Images, http://dx.doi.org/10.48550/arxiv.1607.04032

Biswas S; Qiao L; Dempster A, 2016, Position and Velocity estimation of Re-entry Vehicles using Fast Unscented Kalman Filters

Back to profile page