Experimental analysis of time difference of arrival estimates based on inexactly reconstructed signals

TL;DR

This paper introduces a new method for accurately estimating time differences of arrival using compressed data, reducing the need for large data volumes.

Contribution

The paper presents an optimized inexact reconstruction-based compressed sensing method (EIRCS) for TDOA estimation with reduced computational complexity.

Findings

EIRCS achieves high-precision TDOA estimation with minimal error.

The method sustains accuracy across various compression ratios.

EIRCS reduces data acquisition and storage challenges.

Abstract

Abstract: Time Difference of Arrival (TDOA) estimation is a pivotal technique with extensive applications across various domains, including passive detection and indoor positioning. For signals characterized by unknown modulation types and originating from non-cooperative sources, achieving high-precision TDOA estimation traditionally necessitates a substantial volume of sampling data. Conventional approaches, such as cross-correlation, require high sampling rates and extended durations, posing significant challenges in terms of data acquisition, transmission, and storage. To surmount these obstacles, this paper delves into an enhanced inexact reconstruction-based compressed sensing method for TDOA estimation (EIRCS), presents an optimized algorithmic procedure that further reduces the computational complexity of the EIRCS method. Experimental results substantiate that the EIRCS method is an unbiased estimation technique. It accomplishes high-precision TDOA estimation concurrent with efficient data compression. These insights suggest that the EIRCS method can yield dependable TDOA estimates with minimal error, even at elevated compression ratios. Its capacity to sustain high accuracy across a range of compression ratios renders it particularly apt for applications demanding efficient data processing and reliable TDOA estimation.

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