Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micro-navigation

Ben Thomas, Alan J. Hunter, Samantha Dugelay

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Abstract

Accurate time delay estimation between signals is crucial for coherent imaging systems such as Synthetic Aperture Sonar (SAS) and Synthetic Aperture Radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery.

In this paper, an algorithm is introduced for detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit one- and two-dimensional models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase centre (RPC) micro-navigation. Phase wrap errors are then corrected by re-calculating the phase wrap number using the best-fitting model.

The approach is demonstrated using data collected by the 270-330 kHz SAS of the NATO Centre for Maritime Research and Experimentation (CMRE) Minehunting Unmanned underwater vehicle for Shallow water Covert Littoral Expeditions (MUSCLE). Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using one- and two- dimensional models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1D and 2D RANSAC methods out-perform the branch-cuts method, with improvements of 29-125% and 30-150% respectively compared to 16-134% for the branch-cuts method for this dataset.
Original languageEnglish
JournalIEEE Journal of Oceanic Engineering
DOIs
Publication statusPublished - 18 Feb 2020

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