Stochastic Optimization in Target Positioning and Location-based Applications

Abstract

Position information is important for various applications, including location-aware communications, autonomous driving, industrial internet of things (IoT). Geometry based techniques such as time-of-arrival (TOA), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are widely used and can be formed as optimization prob lems. In order to solve these optimization problems efficiently, stochastic optimization methods are discussed in this work in solving target positioning problems and tackling key issues in location-based applications. Firstly, the direction of arrival (DOA) estimation problem is studied in this work. Grid search is useful in the algorithms such as maximum likelihood estimator (MLE), MUltiple SIgnal Classification (MUSIC), etc. However, the computational cost is the main drawback. To speed up the search procedure, we implement random ferns to extract the features from the beampatterns of different DOAs and use these features to identify potential angle candidates. Then, we propose an ultrasonic air-writing system based on DOA estimation. In this application, stochastic optimization methods are implemented to solve gesture classification problems. This work shows that stochastic optimization methods are effective tools to address and benchmark practical positioning-related problems. Next, we discuss how to select antennas properly to reduce the expectation of DOA estimation error in a switch-based multiple-input-multiple-output (MIMO) system. Cram`er Rao lower bound (CRLB) expresses a lower bound on the variance of an unbiased estimator, but it does not work well for low SNR scenarios. We use DOA threshold-region approximation as an indicator and propose a greedy algorithm and a neural network-based algorithm. Finally, we propose a joint time difference of arrival (TDOA) and phase difference of arrival (PDOA) localization method. It is shown that the phase difference, which is also widely used in DOA estimation, can improve the performance of the well established TDOA technique. Although the joint TDOA/PDOA cost function has a lot of local minima, accurate estimates can be obtained effectively by choosing an appropriate initial estimation and using particle swarm optimization (PSO).

Date of Award	Aug 2021
Original language	English (US)
Awarding Institution	Computer, Electrical and Mathematical Sciences and Engineering
Supervisor	Tareq Al-Naffouri (Supervisor)