TY - GEN
T1 - Earth-Mover's distance as a tracking regularizer
AU - Charles, Adam S.
AU - Bertrand, Nicholas P.
AU - Lee, John
AU - Rozell, Christopher J.
N1 - Funding Information:
This work was supported in part by NSF grant CCF-1409422 and the James S. McDonnell Foundation.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - Tracking time-varying signals is an important part of many engineering systems. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. Leveraging sparsity, however, depends heavily on gridding the space, treating the signal as a collection of active or inactive pixels in an image, rather than traditional methods which track the continuous spatial coordinates. Using the dynamics constraint in this setting is challenging, as a model which approximately predicts target location may result in seemingly large errors, as measured by the ℓp-norm typically used in such algorithms. To take advantage of approximate spatial priors without introducing unnecessary penalties, we present a tracking algorithm using the earth-mover's distance (EMD) as an alternate dynamics regularization term. We note that while requiring a higher computational burden, the EMD can more effectively utilize target location prediction when the space is gridded.
AB - Tracking time-varying signals is an important part of many engineering systems. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. Leveraging sparsity, however, depends heavily on gridding the space, treating the signal as a collection of active or inactive pixels in an image, rather than traditional methods which track the continuous spatial coordinates. Using the dynamics constraint in this setting is challenging, as a model which approximately predicts target location may result in seemingly large errors, as measured by the ℓp-norm typically used in such algorithms. To take advantage of approximate spatial priors without introducing unnecessary penalties, we present a tracking algorithm using the earth-mover's distance (EMD) as an alternate dynamics regularization term. We note that while requiring a higher computational burden, the EMD can more effectively utilize target location prediction when the space is gridded.
KW - Compressive Sensing
KW - Dynamic Filtering
KW - Earth-mover's Distance
KW - Kalman Filtering
UR - http://www.scopus.com/inward/record.url?scp=85050682215&partnerID=8YFLogxK
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U2 - 10.1109/CAMSAP.2017.8313061
DO - 10.1109/CAMSAP.2017.8313061
M3 - Conference contribution
AN - SCOPUS:85050682215
T3 - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
SP - 1
EP - 5
BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Y2 - 10 December 2017 through 13 December 2017
ER -