Name
Abstract | ||
|---|---|---|
Networks of small low-cost sensors for target tracking are becoming increasingly important for many applications. A major problem is that these small sensors usually have limited observability due to power constraints and the transition between sensor observation and target states is nonlinear. As a consequence, nonlinear filtering techniques, such as particle filtering, are often chosen by researchers in this context. We focus on a network of sensors where each sensor provides binary data at each epoch: target present or target absent. At this point it is not clear that existing approaches can effectively handle the tracking of multiple targets using such networks. In addition, algorithmic computational complexity is an issue if particle filters are used. In this paper, we present a new method, the virtual measurement (VM) approach, for multi-target tracking using distributed binary sensor networks. The central idea of this approach is to define a mapping between the space of binary sensor observations and the so-called VM space, such that, any point within a VM space is a transform of the target state, as if it were generated by an equivalent "large sensor". With VMs, conventional multi-target tracking (MTT) algorithms can be used in a straightforward way for tracking multiple targets over the sensing field of binary sensor networks. Computer simulated examples of MTT demonstrate the effectiveness and robustness of the VM approach |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICIF.2006.301815 | Fusion |
Keywords | Field | DocType |
multitarget tracking,virtual measurement,tracking filters,power constraint,distributed binary sensor network,nonlinear filtering technique,target tracking,mtt algorithm,binary sensor networks,wireless communications,virtual measurement (vm) approach,vm space,integrated sensing and processing (isp),wireless sensor networks,distributed tracking,sensor observation,multi-target tracking,nonlinear filters,observability,filtering,nonlinear filter,wireless communication,computational complexity,sensor network,particle filter,computational modeling,particle filters,computer simulation,robustness | Observability,Computer science,Particle filter,Filter (signal processing),Robustness (computer science),Real-time computing,Binary data,Wireless sensor network,Computational complexity theory,Binary number | Conference |
ISBN | Citations | PageRank |
0-9721844-6-5 | 2 | 0.42 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuezhi Wang | 1 | 94 | 10.88 |
| B. Moran | 2 | 111 | 21.09 |