Abstract

Aiming at improving the accuracy of pulse ranging measurement in underwater multi-path environment, this paper proposes a novel pulse ranging algorithm based on active virtual time reversal (AVTR). By using the focusing characteristics of AVTR, the received signal can be focused at the receiving end, which eliminates the negative influence of multiple pseudo-peaks. In order to extract the received signal, we propose an energy-based adaptive windowed method which preserves the signal focus peak while truncates the side peak component. Numerical simulations are provided and outfield experiments are conducted. The results demonstrate the effectiveness of the proposed method comparing with correlation-based method.

Highlights

  • High-precision ranging plays an important role in some information systems, such as sonar, radar, and communication system

  • We propose an active virtual time reversal based method in underwater multi-path environment for pulse ranging

  • Attracted by the focusing ability of Time reversal (TR), we utilize TR to improve the accuracy of time measurement and pulse ranging

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Summary

Introduction

High-precision ranging plays an important role in some information systems, such as sonar, radar, and communication system. Improving the accuracy of time measurement in multi-path environment is a key issue in regard to pulse ranging method. To solve this problem, many pulse ranging methods [10,11,12] have been proposed. In [15], a deconvolution algorithm is proposed to approximate the impulse response of multi-path channel These methods attempt to minimize the effect of multi-path as it has a negative contribution to the ranging results. We propose an active virtual time reversal based method in underwater multi-path environment for pulse ranging. (2) We propose an energy-based adaptive windowed method for further extracting the focusing term from the received signal after AVTR.

Problem Statement
Active Virtual Time Reversal Based Pulse Ranging
Active Virtual Time Reversal
Energy-Based Adaptive Windowed Algorithm
Simulation Analysis
Experimental Analysis
Method
Findings
Conclusions
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