Current Search: Synthetic apertures (x)
-
-
Title
-
Propagation medium impact on sonar coherent processing for high frequency synthetic aperture imaging.
-
Creator
-
Ciany, C. M., Walsh, G. M., Clark, A. M., Harbor Branch Oceanographic Institute
-
Date Issued
-
1994
-
PURL
-
http://purl.flvc.org/FCLA/DT/3183684
-
Subject Headings
-
Synthetic apertures, Underwater imaging systems
-
Format
-
Document (PDF)
-
-
Title
-
APPLICATION OF SIGNAL DECOMPOSITION TO IMPROVE TIME DELAY ESTIMATES FOR SYNTHETIC APERTURE SONAR MOTION COMPENSATION.
-
Creator
-
Gazagnaire, Julia, Beaujean, Pierre-Philippe, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
-
Abstract/Description
-
Synthetic Aperture Sonar (SAS) provides the best opportunity for side-looking sonar mounted on underwater platforms to achieve high-resolution images. However, SAS processing requires strict constraints on resolvable platform motion. The most common approach to estimate this motion is to use the Redundant Phase Center (RPC) technique. Here the ping interval is set, such that a portion of the sonar array overlaps as the sensor moves forward. The time delay between the pings received on these...
Show moreSynthetic Aperture Sonar (SAS) provides the best opportunity for side-looking sonar mounted on underwater platforms to achieve high-resolution images. However, SAS processing requires strict constraints on resolvable platform motion. The most common approach to estimate this motion is to use the Redundant Phase Center (RPC) technique. Here the ping interval is set, such that a portion of the sonar array overlaps as the sensor moves forward. The time delay between the pings received on these overlapping elements is estimated using cross-correlation. These time delays are then used to infer the pingto-ping vehicle motion. Given the stochastic nature of the operational environment, some level of decorrelation between these two signals is likely. In this research, two iterative signal decomposition methods well suited for nonlinear and non-stationary signals, are investigated for their potential to improve the Time Delay Estimation (TDE). The first of this type, the Empirical Mode Decomposition (EMD) was introduced by Huang in the seminal paper, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis and is the foundation for the algorithms used in this research. This method decomposes a signal into a finite sequence of simple components termed Intrinsic Mode Functions (IMFs). The Iterative Filter (IF) approach, developed by Lin, Wang and Zhou, builds on the EMD framework. The sonar signals considered in this research are complex baseband signals. Both the IF and EMD algorithms were designed to decompose real signals. However, the IF variant, the Multivariate Fast Iterative Filtering (MFIF) Algorithm, developed by Cicone, and the EMD variant, the Fast and Adaptive Multivariate Empirical Mode Decomposition (FAMVEMD) algorithm, developed by Thirumalaisamy and Ansell, preserve both the magnitude and phase in the decomposition and hence were chosen for this analysis.
Show less
-
Date Issued
-
2021
-
PURL
-
http://purl.flvc.org/fau/fd/FA00013795
-
Subject Headings
-
Sonar, Signal processing, Synthetic apertures
-
Format
-
Document (PDF)
