Small bottom object density analysis from side scan sonar data by a mathematical morphology detector

Grasso, Raffaele; Spina, Francesco

doi:10.1109/icif.2006.301753

“…Preprocessing of the received data includes baseband conversion, complex matched filtering and normalization for the bistatic range sum (BRS = R tx + R rx , estimated from the echo time of arrival at the receiver) profile attenuation. The attenuation profile has been estimated from the data by using a set of mathematical morphology filters as in [45] and [46]. Figure 13 shows the average power of the array elements after the normalization for the attenuation profile at scan time T 1 .…”

Section: Testing On Real Sonar Datamentioning

confidence: 99%

Single-snapshot DOA estimation by using Compressed Sensing

Fortunati

¹

,

Grasso

²

,

Gini

³

et al. 2014

EURASIP J. Adv. Signal Process.

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This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

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“…Preprocessing of the received data includes baseband conversion, complex matched filtering and normalization for the bistatic range sum (BRS = R tx + R rx , estimated from the echo time of arrival at the receiver) profile attenuation. The attenuation profile has been estimated from the data by using a set of mathematical morphology filters as in [45] and [46]. Figure 13 shows the average power of the array elements after the normalization for the attenuation profile at scan time T 1 .…”

Section: Testing On Real Sonar Datamentioning

confidence: 99%

Single snapshot DOA estimation using compressed sensing

Fortunati

¹

,

Grasso

²

,

Gini

³

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

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“…Figure 6 shows the building blocks of the system. A comprehensive introduction to mathematical morphology tools is provided in [3] while a detailed description of the morphological detection algorithm can be found in [5]. An introduction to the state of the art of ship detection algorithms for SAR data is provided in [6].…”

Section: Detection Algorithmmentioning

confidence: 99%

Performance Assessment of a Mathematical Morphology Ship Detection Algorithm for SAR Images through Comparison with AIS Data

Grasso

¹

,

Mirra

²

,

Baldacci³

et al. 2009

2009 Ninth International Conference on Intelligent Systems Design and Applications

Self Cite

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This paper describes a procedure to evaluate the performance of ship detection algorithms for Synthetic Aperture Radar (SAR) using real SAR images and Automatic Identification System (AIS) data as ground truth. Accurate AIS-SAR data association is achieved by correcting the AIS data for the SAR induced position errors by exploiting SAR acquisition parameters and vessel state information (speed and course) provided by AIS tracks. The methodology has been tested on a ship detection algorithm based on mathematical morphology which is described in this paper. The evaluation has been carried out on a RADARSAT-2 data set including images at different acquisition modes which was collected in the Mediterranean Sea. Estimates for the detection and the false alarm probability, and the contact position error are provided.

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Small bottom object density analysis from side scan sonar data by a mathematical morphology detector

Cited by 7 publications

References 3 publications

Single-snapshot DOA estimation by using Compressed Sensing

Single-snapshot DOA estimation by using Compressed Sensing

Single snapshot DOA estimation using compressed sensing

Performance Assessment of a Mathematical Morphology Ship Detection Algorithm for SAR Images through Comparison with AIS Data

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