Range-based localization is a widely used technique for position estimation where distances are measured to anchors, nodes with known positions, and the position is analytically estimated. It offers the benefits of providing high localization accuracy and involving simple operation over multiple deployments. Examples are the Global Positioning System (GPS) and network-based cellular handset localization. Range-based localization is promising for a range of applications, such as robot deployment in emergency scenarios or monitoring industrial processes. However, the presence of clutter in some of these environments leads to a severe degradation of the localization accuracy due to non-line-of-sight (NLOS) signal propagation. Moreover, current literature in NLOS-mitigation techniques requires that the NLOS distances constitute only a minority of the total number of distances to anchors. The key ideas proposed in the dissertation are: 1) multi-hop localization offers significant advantages over single-hop localization in NLOS-prone environments; and 2) it is possible to further reduce position errors by carefully placing intermediate nodes among the clutter to minimize multi-hop distances between the anchors and the unlocalized node. We demonstrate that shortest path distance (SPD) based multi-hop localization algorithms, namely DV-Distance and MDS-MAP, perform the best among other competing techniques in NLOS-prone settings. However, with random node placement, these algorithms require large node densities to produce high localization accuracy. To tackle this, we show that the strategic placement of a relatively small number of nodes in the clutter can offer significant benefits. We propose two algorithms for node placement: first, the Optimal Placement for DV-Distance (OPDV) focuses on obtaining the optimal positions of the nodes for a known clutter topology; and second, the Adaptive Placement for DV-Distance (APDV) offers a distributed control technique that carefully moves nodes in the monitored area to achieve localization accuracies close to those achieved by OPDV. We evaluate both algorithms via extensive simulations, as well as demonstrate the APDV algorithm on a real robotic hardware platform. We finally demonstrate how the characteristics of the clutter topology influence single-hop and multi-hop distance errors, which in turn, impact the performance of the proposed algorithms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635196 |
| Date | January 2013 |
| Creators | Hussain, Muzammil |
| Contributors | Trigoni, Niki |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:0b1bf995-c9ed-43ed-ab30-811b2fdd2476 |
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