Name
Abstract | ||
|---|---|---|
Device-free motion tracking with radio tomographic networks using received signal strength (RSS) measurements has attracted considerable research efforts. Since the motion scene to be reconstructed can often be assumed sparse, i.e., it consists only of several targets, the Compressed Sensing (CS) framework can be applied. We cast the motion tracking as a CS problem and employ an efficient algorithm, Orthogonal Matching Pursuit (OMP), for sparse recovery. Furthermore, we exploit a feedback structure which leads to a substantial reduction of the amount of measurements. The feedback structure utilizes the prior knowledge (locations of targets) in time sequence to predict next frame support. Compared with the least-square type methods, the proposed motion tracking based on feedback sparse recovery can directly determine where the targets are located in the network area and reduce the amount of measurements required for reliable tracking. Experimental results show its favorable performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/NAS.2011.9 | NAS |
Keywords | Field | DocType |
rf sensor networks,sparse recovery,tomography,rf tomograpy,radio tomographic network,motion tracking,target tracking,data compression,feedback sparse recovery,cs problem,reliable tracking,proposed motion,motion scene,compressed sensing,device-free motion tracking,device free motion tracking,feedback based sparse recovery,wireless sensor networks,compressed sensing framework,feedback structure,orthogonal matching pursuit,received signal strength measurements,iterative methods,comressed sensing,matching pursuit,wireless communication,wireless sensor network,sensor network,trajectory,radio frequency,least square | Matching pursuit,Computer vision,Wireless,Computer science,Iterative method,Artificial intelligence,Data compression,Wireless sensor network,Match moving,Trajectory,Compressed sensing | Conference |
ISBN | Citations | PageRank |
978-0-7695-4509-7 | 4 | 0.45 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He-Ping Song | 1 | 7 | 3.32 |
| Tong Liu | 2 | 4 | 0.45 |
| Xiaomu Luo | 3 | 36 | 2.32 |
| Guoli Wang | 4 | 221 | 21.26 |