RESEARCH COMMONS
LIBRARY

Localization In Noisy Environment Using Extended Kalman Filter

ResearchCommons/Manakin Repository

Localization In Noisy Environment Using Extended Kalman Filter

Show full item record

Title: Localization In Noisy Environment Using Extended Kalman Filter
Author: Patkar, Aneeket Suresh
Abstract: Localization is an important aspect of Wireless Sensor Networks. Information regarding the position of the sensor nodes is not always known. Without the position information of the sensors, the data reported by the sensors is of little use. Various approaches have been used to perform localization using some information about the sensor node. Potential field approach for localization, using distance information has been successfully tested with satisfactory results. However in case of noisy environment, the range measurements have greater inaccuracy. In such cases, localization using the above algorithm can provide some inaccuracy. To rectify such erroneous localization situations, Extended Kalman Filters are used to estimate the position. The Extended Kalman Filter has been used as the process for estimation of coordinates is a non-linear process. The EKF is a recursive filter which only needs the information from the previous state to predict the next state. My Contributions to the thesis : 1. Programmed the Cricket in TinyOS to store the distance measurement in arrays and then broadcast the same over radio to other crickets. The cricket programmed as a base station only listens on the radio and then send the received message to the PC over the serial UART. 2. Created a LabVIEW application which processes the message received from the cricket base station and deciphers the message to extract the distance measurements. The node id is used to identify the transmitting node and the distances are stored in corresponding arrays. The ranging information in then written into a file. 3. Created a LabVIEW application to read the distances stored in the file and then arrange the readings depending on the number of nodes. 4. Implemented the Extended Kalman Filter Localization algorithm for relative and absolute localization algorithm. 5. Implemented V-shaped swarm behavior demonstration using three Garcia as the robot and cricket as the motion guiding tool.
URI: http://hdl.handle.net/10106/775
Date: 2008-04-22
External Link: https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=27

Files in this item

Files Size Format View
umi-uta-1985.pdf 27.44Mb PDF View/Open
27.44Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record

Browse

My Account

Statistics

About Us