This thesis studies the problem of estimating the interior structure of a collapsed building using embedded Ultra-Wideband (UWB) radios as sensors. The two major sensing problems needed to build the mapping system are determining wall type and wall orientation. We develop sensing algorithms that determine (1) load-bearing wall composition, thickness, and location and (2) wall position within the indoor cavity. We use extensive experimentation and measurement to develop those algorithms.
In order to identify wall types and locations, our research approach uses Received Signal Strength (RSS) measurement between pairs of UWB radios. We create an extensive database of UWB signal propagation data through various wall types and thicknesses. Once the database is built, fingerprinting algorithms are developed which determine the best match between measurement data and database information. For wall mapping, we use measurement of Time of Arrival (ToA) and Angle of Arrival (AoA) between pairs of radios in the same cavity. Using this data and a novel algorithm, we demonstrate how to determine wall material type, thickness, location, and the topology of the wall.
Our research methodology utilizes experimental measurements to create the database of signal propagation through different wall materials. The work also performs measurements to determine wall position in simulated scenarios. We ran the developed algorithms over the measurement data and characterized the error behavior of the solutions.
The experimental test bed uses Time Domain UWB radios with a center frequency of 4.7 GHz and bandwidth of over 3.2 GHz. The software was provided by Time Domain as well, including Performance Analysis Tool, Ranging application, and AoA application. For wall type identification, we use the P200 radio. And for wall mapping, we built a special UWB radio with both angle and distance measurement capability using one P200 radio and one P210 radio.
In our experimental design for wall identification, we varied wall type and distance between the radios, while fixing the number of radios, transmit power and the number of antennas per radio. For wall mapping, we varied the locations of reference node sensors and receiver sensors on adjoining and opposite walls, while fixing cavity size, transmit power, and the number of antennas per radio.
As we present in following chapters, our algorithms have very small estimation errors and can precisely identify wall types and wall positions.
|Commitee:||Feng, Wu-chi, Hall, Douglas V., Li, Jingke|
|School:||Portland State University|
|School Location:||United States -- Oregon|
|Source:||DAI-B 79/11(E), Dissertation Abstracts International|
|Subjects:||Computer Engineering, Engineering, Computer science|
|Keywords:||3D positioning, Disaster recovery, Indoor locationing, Material characterization, Public safety, Through wall sensing|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be