Publications

Abstract not provided.

Direction of Arrival (DOA) measurements, as with a monopulse antenna, can be compared against Doppler measurements in a Synthetic Aperture Radar ( SAR ) image to determine an aircraft's forward velocity as well as its crab angle, to assist the aircraft's navigation as well as improving high - performance SAR image formation and spatial calibration.

We propose a new laboratory method for characterizing synthetic aperture radar (SAR) systems through the use of a synthetic scene generator. Flight tests are the only definitive way to characterize the system level performance of airborne synthetic aperture radar systems. However, due to the expense of flights tests it is beneficial to complete as much testing as possible in a laboratory environment before flight testing is performed. There are many existing tests that are employed to measure the performance of various subsystems in a SAR system, find defective hardware, and indicate design problems that need to be mitigated. However, certain issues can only be found on an integrated system, and laboratory testing at a system level is typically confined to characterizing the impulse response (IPR) of a single point target through the use of an optical delay line. While useful, delay line testing requires running a modified version of real-time image formation code as the delay line does not completely mimic a real target. Ideally, system level tests are performed on unmodified code. On modern SAR systems many algorithms are data driven (e.g., autofocus) and require a substantially more sophisticated data model for testing. We desire to create a complete system test by combining an arbitrary number of point targets and clutter patterns to mimic radar responses from a real scene. This capability enables complete testing of radar systems in a laboratory environment according to prescribed terrain/scene characteristics. This paper presents an overview of the system requirements for a synthetic scene generator. The analysis is limited to SAR systems utilizing chirp waveforms and stretch processing. Furthermore, we derive relationships between IF bandwidth, target position, and the phase history model. A technique to properly compensate for motion pulse to pulse is presented. Finally, our concept is demonstrated with simulation data. © 2014 SPIE.

We propose a new laboratory method for characterizing synthetic aperture radar (SAR) systems through the use of a synthetic scene generator. Flight tests are the only definitive way to characterize the system level performance of airborne synthetic aperture radar systems. However, due to the expense of flights tests it is beneficial to complete as much testing as possible in a laboratory environment before flight testing is performed. There are many existing tests that are employed to measure the performance of various subsystems in a SAR system, find defective hardware, and indicate design problems that need to be mitigated. However, certain issues can only be found on an integrated system, and laboratory testing at a system level is typically confined to characterizing the impulse response (IPR) of a single point target through the use of an optical delay line. While useful, delay line testing requires running a modified version of real-time image formation code as the delay line does not completely mimic a real target. Ideally, system level tests are performed on unmodified code. On modern SAR systems many algorithms are data driven (e.g., autofocus) and require a substantially more sophisticated data model for testing. We desire to create a complete system test by combining an arbitrary number of point targets and clutter patterns to mimic radar responses from a real scene. This capability enables complete testing of radar systems in a laboratory environment according to prescribed terrain/scene characteristics. This paper presents an overview of the system requirements for a synthetic scene generator. The analysis is limited to SAR systems utilizing chirp waveforms and stretch processing. Furthermore, we derive relationships between IF bandwidth, target position, and the phase history model. A technique to properly compensate for motion pulse to pulse is presented. Finally, our concept is demonstrated with simulation data. © 2014 SPIE.

High quality focused SAR imaging dictates that the relative phase error over an aperture must be kept below a fraction of a wavelength. On most deployed SAR systems the internal measurement systems ability to measure position uncertainty is not sufficient to achieve this required precision. This necessitates an additional post-processing step of data-driven phase error mitigation known as autofocus. We present results comparing the performance of a variety of autofocus techniques including image metric optimization based techniques and several variants of phase gradient autofocus (PGA). The degree of focusing is evaluated with an image focus metric, specific to SAR images, that is not biased toward any particular autofocus algorithm. This evaluation is performed on a variety of scene types using injected (known) phase errors. We show that PGA autofocus outperforms the image metric optimization techniques tested (based on minimizing image entropy) in low contrast SAR scenes. © 2013 SPIE.

High quality focused SAR imaging dictates that the relative phase error over an aperture must be kept below a fraction of a wavelength. On most deployed SAR systems the internal measurement systems ability to measure position uncertainty is not sufficient to achieve this required precision. This necessitates an additional post-processing step of data-driven phase error mitigation known as autofocus. We present results comparing the performance of a variety of autofocus techniques including image metric optimization based techniques and several variants of phase gradient autofocus (PGA). The degree of focusing is evaluated with an image focus metric, specific to SAR images, that is not biased toward any particular autofocus algorithm. This evaluation is performed on a variety of scene types using injected (known) phase errors. We show that PGA autofocus outperforms the image metric optimization techniques tested (based on minimizing image entropy) in low contrast SAR scenes. © 2013 SPIE.

Synthetic aperture radar systems that use the polar format algorithm are subject to a focused scene size limit inherent to the polar format algorithm. The classic focused scene size limit is determined from the dominant residual range phase error term. Given the many sources of phase error in a synthetic aperture radar, a system designer is interested in how much phase error results from the assumptions made with the polar format algorithm. Autofocus algorithms have limits to the amount and type of phase error that can be corrected. Current methods correct only one or a few terms of the residual phase error. A system designer needs to be able to evaluate the contribution of the residual or uncorrected phase error terms to determine the new focused scene size limit. This paper describes a method to estimate the complete residual phase error, not just one or a few of the dominant residual terms. This method is demonstrated with polar format image formation, but is equally applicable to other image formation algorithms. A benefit for the system designer is that additional correction terms can be added or deleted from the analysis as necessary to evaluate the resulting effect upon image quality. © 2013 SPIE.

Abstract not provided.