:Added: Mar 17, 2015 11:39 am
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Image Processing - synthetic aperture radar analysis |
Sandia National Laboratories pioneered
the Phase Gradient Autofocus algorithm (PGA) which enables sharper imaging in Synthetic Aperture Radar (SAR). By deploying PGA, similar
algorithms and other related technologies from radar applications, and
image and signal processing capabilities, Sandia has the potential to
significantly advance the vanguard of sonar and underwater sensing,
especially in underwater vehicle applications. Advancements in sonar and
underwater sensing technology will broaden applications and will also
result in improved effectiveness in underwater vehicle technologies.
Sandia's capabilities include developing and applying existing algorithms in sonar and radar applications. Sandia has a large IP portfolio of algorithms in radar and sonar technologies that could be effectively deployed in the sonar and underwater sensing space. Prospective applications include port and shipping security, underwater or seafloor anomaly detection, and underwater salvage.
Sandia's capabilities include developing and applying existing algorithms in sonar and radar applications. Sandia has a large IP portfolio of algorithms in radar and sonar technologies that could be effectively deployed in the sonar and underwater sensing space. Prospective applications include port and shipping security, underwater or seafloor anomaly detection, and underwater salvage.
Background
- Synthetic Aperture Sonar - Kongsberg Maritime
- Synthetic Aperture Sonar - Kraken Sonar Systems Inc.
- T-SAS | Thales Group
- ATLAS ELEKTRONIK GROUP:Vision 600 SAS
- CHIPS Articles: Advances in Synthetic Aperture Sonar Transform Mine Countermeasures and Undersea Warfare
References
Methods for two-dimensional autofocus in high resolution radar systems
US 8009079 B2
Abstract Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include:
- two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH);
- two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods;
- a two-dimensional geometry filter algorithm;
- a two-dimensional prominent point processing algorithm;
- a one-dimensional phase estimate of higher order two dimensional phase errors; and,
- a fast SHARP parametric autofocus algorithm.
Wahl, D.E.; Eichel, P.H.; Ghiglia, D.C.; Jakowatz, C.V., Jr., "Phase gradient autofocus-a robust tool for high resolution SAR phase correction," Aerospace and Electronic Systems, IEEE Transactions on , vol.30, no.3, pp.827,835, Jul 1994
doi: 10.1109/7.303752
Abstract: The phase gradient autofocus (PGA) technique for phase error correction of spotlight mode synthetic aperture radar (SAR) imagery is examined carefully in the context of four fundamental signal processing steps that constitute the algorithm. We demonstrate that excellent results over a wide variety of scene content, and phase error function structure are obtained if and only if all of these steps are included in the processing. Finally, we show that the computational demands of the fun PGA algorithm do not represent a large fraction of the total image formation problem, when mid to large size images are involved
keywords: {computational complexity;error correction;focusing;image processing;parameter estimation;synthetic aperture radar;SAR;SAR phase correction;circular shifting;computational demands;phase error correction;phase error function structure;phase gradient autofocus;phase gradient estimation;signal processing;spotlight mode synthetic aperture radar;total image formation;windowing;Diffraction;Electronics packaging;Error correction;Focusing;Image resolution;Image restoration;Layout;Motion measurement;Robustness;Signal processing algorithms},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=303752&isnumber=7475
H. J. Callow, M. P. Hayes, and P. T. Gough, Stripmap Phase Gradient Autofocus a499457.pdf
Abstract — Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult.
This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries.
The SPGA method works by employing wavenumber domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wavenumber transform. Robust sway estimates are obtained by using redundancy over a number of target points.
We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wavenumber transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA’s benefits over traditional algorithms and the limitations of the SPGA algorithm.
The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement
Callow, H.J.; Hayes, M.P.; Gough, P.T., "Stripmap phase gradient autofocus," OCEANS 2003. Proceedings , vol.5, no., pp.2414,2421 Vol.5, 22-26 Sept. 2003
doi: 10.1109/OCEANS.2003.178291
Abstract: Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.
keywords: {image denoising;oceanographic techniques;sonar imaging;synthetic aperture sonar;2D phase errors;2D phase estimation techniques;PCA algorithm;PGA algorithm;SAS imagery;SPGA algorithm;aperture position errors;blur removal;phase curvature autofocus;phase error estimation;phase gradient autofocus;sonar autofocus techniques;spotlight SAR;stripmap PGA;stripmap SAS geometries;sway estimates;synthetic aperture radar;synthetic aperture sonar;target points;wave number domain;wavenumber transform;Electronics packaging;Geometry;Layout;Phase estimation;Principal component analysis;Radar imaging;Robustness;Synthetic aperture radar;Synthetic aperture sonar;Wideband},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1282922&isnumber=28618
Sanwen Zhu; Jianping Yue; Weitao Jiang, "SAS Autofocus Based on Phase Gradient Autofocus," Chaos-Fractals Theories and Applications (IWCFTA), 2011 Fourth International Workshop on , vol., no., pp.298,301, 19-22 Oct. 2011
doi: 10.1109/IWCFTA.2011.42
Abstract: Phase gradient autofocus (PGA) is widely used in spotlight SAR autofocus with strong robustness. However, being the different nature of synthetic aperture sonar (SAS) imagery, traditional PGA algorithm usually fails to yield satisfactory result on SAS image without modification. Three measures are taken to ensure that PGA can be applied in stripmap SAS autofocus. A SAS image is divided into sub-images which ensures that the phase error is invariant in each sub-image; The image shift in azimuth can be avoided by removing the mean value of the phase gradient and by using strong point targets in the image; The estimation accuracy can be improved by selecting range bins according to energy criteria combined with quality criteria. The proposed method is successfully applied on SAS image and excellent results are attained.
keywords: {sonar imaging;synthetic aperture sonar;SAS autofocus;SAS image;azimuth image shift;phase gradient autofocus;synthetic aperture sonar imagery;Apertures;Azimuth;Electronics packaging;Estimation;Imaging;Signal processing algorithms;Synthetic aperture sonar;Phase Gradient Autofocus;Phase error;stripmap synthetic aperture sonar},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6093541&isnumber=6093471
Jiang, R.; Zhu, D.; SHEN, M.; Zhu, Z., "Synthetic aperture radar autofocus based on projection approximation subspace tracking," Radar, Sonar & Navigation, IET , vol.6, no.6, pp.465,471, July 2012
doi: 10.1049/iet-rsn.2011.0312
Abstract: An eigenvector method for maximum-likelihood estimation (MLE) of phase error has better algorithmic performance than phase gradient autofocus (PGA), which is implemented by the simultaneous processing of multiple-pulse vectors of the range-compressed data. However, this method requires eigendecomposition of the sample covariance matrix, which is a computationally expensive task and also limits the real-time application. In order to overcome such difficulty, this study proposes a novel autofocus algorithm using the projection approximation subspace tracking (PAST) approach. With this methodology, the computational cost can be reduced effectively to the level of PGA via avoiding the procedures of covariance matrix estimation and eigendecomposition. Monte Carlo tests and real synthetic aperture radar (SAR) data validate that although undergoing performance loss compare with the original multiple-pulse MLE algorithm, the new approach outperforms the mostly used PGA.
keywords: {Monte Carlo methods;error correction;radar imaging;radar tracking;synthetic aperture radar;Monte Carlo tests;PAST;PGA;computational cost reduction;eigenvector method;phase error;projection approximation subspace tracking;synthetic aperture radar autofocus algorithm},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6232398&isnumber=6232391
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