activity
20182023
most citedSpace-time wave packets localized in all dimensions

100 citations · 155 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2023

Kalman Filter Auto-tuning through Enforcing Chi-Squared Normalized Error Distributions with Bayesian Optimization

Zhaozhong Chen, Harel Biggie, Nisar Ahmed +2

The nonlinear and stochastic relationship between noise covariance parameter values and state estimator performance makes optimal filter tuning a very challenging problem. Popular…

eess.SP2022★ 39 cited

Enhanced Atmospheric Turbulence Resiliency with Successive Interference Cancellation DSP in Mode Division Multiplexing Free-Space Optical Links

Yiming Li, Zhaozhong Chen, Zhouyi Hu +5

We experimentally demonstrate the enhanced atmospheric turbulence resiliency in a 137.8 Gbit/s/mode mode-division multiplexing free-space optical communication link through the app…

physics.optics2022

Vector space-time wave packets

Murat Yessenov, Zhaozhong Chen, Martin P. J. Lavery +1

Space-time wave packets (STWPs) are propagation-invariant pulsed beams whose characteristics stem from the tight association between their spatial and temporal degrees of freedom.…

physics.optics2021★ 100 cited

Space-time wave packets localized in all dimensions

Murat Yessenov, Justin Free, Zhaozhong Chen +4

Optical wave packets that are localized in space and time, but nevertheless overcome diffraction and travel rigidly in free space, are a long sought-after field structure with appl…

eess.SY2021

Time Dependence in Kalman Filter Tuning

Zhaozhong Chen, Christoffer Heckman, Simon Julier +1

In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional app…

eess.SY2019★ 10 cited

Kalman Filter Tuning with Bayesian Optimization

Zhaozhong Chen, Nisar Ahmed, Simon Julier +1

Many state estimation algorithms must be tuned given the state space process and observation models, the process and observation noise parameters must be chosen. Conventional tunin…

stat.ML2018★ 6 cited

Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization

Zhaozhong Chen, Christoffer Heckman, Simon Julier +1

Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time an…