TY - GEN
T1 - Sparse Dynamic Filtering via Earth Mover's Distance Regularization
AU - Bertrand, Nicholas P.
AU - Lee, John
AU - Charles, Adam S.
AU - Dunn, Pavel
AU - Rozell, Christopher J.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Tracking time-varying signals is an important task for practical systems working with large discretized domains. Under such settings, sparsity-based approaches improve tracking accuracy since typically few targets appear in the scene (i.e. few locations in the discretized space are occupied). Discretization introduces a unique challenge: the traditional Φ{p}-norm dynamic constraints produce significant errors when there is even a small spatial mismatch between the predicted and true state. To overcome this, we present a tracking algorithm leveraging concepts from optimal transport, namely utilizing the earth-movers distance (EMD) as a dynamic regularizer to the Φ{1}-regularized inference problem (i.e., LASSO [1], or BPDN [2]). We extend the problem formulation to complex valued signals and modify the optimization program to reduce the computational burden. We demonstrate the efficacy of our approach in imaging and frequency tracking applications.
AB - Tracking time-varying signals is an important task for practical systems working with large discretized domains. Under such settings, sparsity-based approaches improve tracking accuracy since typically few targets appear in the scene (i.e. few locations in the discretized space are occupied). Discretization introduces a unique challenge: the traditional Φ{p}-norm dynamic constraints produce significant errors when there is even a small spatial mismatch between the predicted and true state. To overcome this, we present a tracking algorithm leveraging concepts from optimal transport, namely utilizing the earth-movers distance (EMD) as a dynamic regularizer to the Φ{1}-regularized inference problem (i.e., LASSO [1], or BPDN [2]). We extend the problem formulation to complex valued signals and modify the optimization program to reduce the computational burden. We demonstrate the efficacy of our approach in imaging and frequency tracking applications.
KW - Compressive Sensing
KW - Dynamic Filtering
KW - Earth-mover's Distance
KW - Kalman Filtering
UR - http://www.scopus.com/inward/record.url?scp=85054229406&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054229406&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461503
DO - 10.1109/ICASSP.2018.8461503
M3 - Conference contribution
AN - SCOPUS:85054229406
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4334
EP - 4338
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -