Name
Abstract | ||
|---|---|---|
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 ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub>
-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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CAMSAP.2017.8313061 | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Keywords | Field | DocType |
Dynamic Filtering,Earth-mover's Distance,Compressive Sensing,Kalman Filtering | Signal processing,Earth mover's distance,Noise measurement,Spatial reference system,Computer science,Algorithm,Kalman filter,Regularization (mathematics),Pixel,Prior probability | Conference |
ISBN | Citations | PageRank |
978-1-5386-1252-1 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam S. Charles | 1 | 113 | 10.21 |
| Nicholas P. Bertrand | 2 | 0 | 1.01 |
| Lee, J. | 3 | 2 | 3.79 |
| Christopher Rozell | 4 | 472 | 45.93 |