TABLE OF CONTENTS Generally speaking, there are two approaches to localization:
Coarse-grained localization using minimal information: These typically use a small set of discrete measurements, such as the information used to compute location. Minimal information could include binary proximity (can two nodes hear each other or not?), near far information (which of two nodes is closer to a given third node?), or cardinal direction information (is one node in the north, east, west, or south sector of the other given node?).
Fine-grained localization using detailed information: These are typically based on measurements, such as RF power, signal waveform, time stamps, etc., that are either real-valued or discrete with a large number of quantization levels. These include techniques based on radio signal strengths, timing information, and angulation.
The tradeoff that emerges between the two approaches is easy to see: while minimal information techniques are simpler to implement, and likely involve lower resource consumption and equipment costs, they provide lower accuracy than the detailed information techniques. We shall now describe specific techniques in detail.
We shall start first with the node localization problem involving a single unknown node and several reference nodes, and then discuss the problem of network localization where there are several unknown nodes in a multi-hop network.
