Application of Language and Symbolic Dynamics for Radar Functionality Optimization
Open Access
- Author:
- Singerman, Paul
- Graduate Program:
- Electrical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 07, 2022
- Committee Members:
- Gregory Huff, Major Field Member
Yan Li, Major Field Member
Ram Narayanan, Chair & Dissertation Advisor
Muralidhar Rangaswamy, Special Member
Asok Ray, Outside Unit & Field Member
Kultegin Aydin, Program Head/Chair - Keywords:
- radar
maneuver detection
target tracking
language
optimization
symbolic dynamics
information overload
information elasticity
fully adaptive radar
cognitive radar - Abstract:
- In order for a closed-loop radar system to adapt properly to changing operational conditions, the performance standards and requirements of the radar system must be known. Often, these performance standards are contained in statements made in language which describe the different goals of the radar system. In this work, the use of language-based cost functions (LBCFs) to encode performance standards made in imprecise language into an objective function which a fully adaptive radar (FAR) can use to optimize its performance in real time is developed and explored. To enable the LBCFs, a statement decomposer which takes in statements as inputs and yields the information required by LBCFs as outputs is proposed. A simulation study of a FAR tracking a running human is conducted with four different cost function methodologies: quadratic, weighted global criterion, fuzzy optimization, and LBCFs. Results show that when statements are properly formulated and presented the statement decomposer is able to successfully extract the relevant information. Results also show that when paired with a previously developed optimization routine, the LBCF system is capable of autonomously creating cost functions that outperform the other cost functions in the simulated FAR target tracking scenario. Also, in radar target tracking, knowledge of the true dynamics of target motion is paramount for accurate state estimates. Target maneuvers complicate this due to quick unknown changes in the target's dynamics. Many popular methods for detecting target maneuvers utilize an input estimation approach where the input to the target's state system is estimated. While input estimation methods work well they are limited to lower data rate systems due to their complexity. In this work, a new method of target maneuver detection utilizing symbolic dynamics is proposed. Symbolic dynamics has the advantage of being computationally simple due to the way it symbolizes and compresses the data. A new radar target maneuver detector leveraging symbolic dynamics is developed. Through two different simulations, the ability for the symbolic dynamics detector to be as fast as a simple chi-squared detector while able to detect maneuvers sooner and with higher accuracy is demonstrated.