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The estimation error performance of the Iterative Compressive Sensing Direction of Arrival (icsDOA) algorithm for signals received over a multipath channel

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posted on 2022-09-26, 23:33 authored by Zachary LongeZachary Longe

For decades, direction of arrival (DOA) sensing has been used all across the world. New and more powerful direction sensing techniques are developed every year. In 2016 a new method of applying compressive sensing techniques to direction of arrival estimation called the icsDOA was created. This algorithm performs much more accurately than any other direction sensing technology used in the modern era. This algorithm is named the 'Iterative Compressive Sensing Direction of Arrival', or icsDOA algorithm. The icsDOA algorithm uses the compressive sensing recovery method as its basis, but implements it multiple times in an iterative manner. The algorithm converges to a directional indicator in two iterations and eliminates angular quantisation error. This method only requires a single snapshot of complex voltage outputs from an antenna array to determine a highly accurate directional indication vector. The performance of this algorithm has been demonstrated for signal transmission over an additive Gaussian noise channel. In many practical circumstances of DOA sensing, there is a multipath signal component that degrades the performance of the direction of arrival algorithm. This thesis establishes the achievable DOA estimation accuracy of the icsDOA Algorithm for signals received over a multipath channel.

History

Table of Contents

1 Introduction -- 2 State of the Art in Direction of Arrival Sensing -- 3 State of the Art in Compressive Sensing -- 4 The Multipath Channel -- 5 The icsDOA Algorithm -- 6 Conclusion and Future Work -- Appendix A -- Appendix B

Notes

A thesis submitted to Macquarie University for the degree of Master of Research

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Thesis (MRes), School of Engineering, Faculty of Science and Engineering, Macquarie University

Department, Centre or School

School of Engineering

Year of Award

2021

Principal Supervisor

Sam Reisenfeld

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

66 pages