Position-adaptive direction finding for multi-platform RF emitter localization using extremum seeking control

Date of Award


Degree Name

Ph.D. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Raul Ordóñez


In recent years there has been growing interest in Ad-hoc and Wireless Sensor Networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the parameter of primary importance. WSNs are being used in a variety of ways - from reconnaissance and detection in military to biomedical applications and a wide variety of commercial endeavors. In recent years, position-based services have become more important. Thus, recent developments in communications and RF technology have enabled system concept formulations and designs for low-cost radar systems using state-of-the-art software radio modules, which are capable of local processing and wireless communication, a reality. Such nodes are called as sensor nodes. Each sensor node is capable of only a limited amount of processing. This research focused on the modeling and implementation of distributed, mobile radar sensor networks. In particular, we worked on the problem of Position-Adaptive Direction Finding (PADF), to determine the location of a non-collaborative transmitter, possibly hidden within a structure, by using a team of cooperative intelligent sensor networks. Our purpose is to further develop and refine position-adaptive RF sensing techniques based on the measurement and estimation of RF scattering metrics. Topics planned for this entrepreneurial research project are focused on the investigation, analysis/simulation, and development of real time multi-model (i.e., complex multipath) environments scattering decompositions for PADF geometries. PADF is based on the formulation and investigation of path-loss based RF scattering metrics (i.e., estimation of distributed Path Loss Exponent, or PLE) that are measured and estimated across multiple platforms in order to enable the robotic/intelligent position-adaptation (or self-adjustment) of the location of each platform. We provide a summary of recent experimental results in localization of a non-cooperative sensor node using static and mobile sensor networks. In this study we used IRIS wireless sensor nodes. In order to localize the transmitter, we used the Received Signal Strength Indicator (RSSI) data to approximate distance from the transmitter to the revolving receivers. We provided an algorithm for on-line estimation of the PLE that is used in modeling the distance based on RSSI measurements. The emitter position estimation is calculated based on surrounding sensors RSSI values using Least-Square Estimation (LSE). The PADF has been tested on a number of different configurations in the laboratory via the design and implementation of four IRIS wireless sensor nodes as receivers and one hidden sensor as a transmitter during the localization phase. The robustness of detecting the transmitter's position is initiated by getting the RSSI data through experiments and then data manipulation in MATLAB will determine the robustness of each node and ultimately that of each configuration. The parameters that are used in the functions are the median values of RSSI and rms values. From the result it is determined which configurations possess high robustness. High values obtained from the robustness function indicate high robustness, while low values indicate lower robustness. Finally, we present the experimental performance analysis on the application aspect. We apply Extremum Seeking Control (ESC) schemes by using the swarm seeking problem, where the goal is to design a control law for each individual sensor that can minimize the error metric by adapting the sensor positions in real-time, thereby minimizing the unknown estimation error. As a result we achieved source seeking and collision avoidance of the entire group of the sensor positions.


Wireless sensor networks Testing, Sensor networks Design and construction, Sensor networks Automatic control, Adaptive control systems, Signal processing

Rights Statement

Copyright © 2012, author