SAR image formation toolbox for MATLAB

Gorham, LeRoy A.; Moore, Linda J.

doi:10.1117/12.855375

Cited by 192 publications

(111 citation statements)

References 21 publications

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“…The SAR sensor travels along a flight path such that (x(τ ), y(τ ), z(τ )) represents the trajectory of the Transmitting (Tx)/Receiving (Rx) antenna phase center, where τ stands for the "slow-time" [2]. We consider a scene populated by generic stationary targets (i.e., their locations do not depend on τ ), residing at the Figure 1.…”

Section: Sar Signal Modelmentioning

confidence: 99%

“…As the antenna phase center moves along the flight path, it transmits pulses (chirp pulses, typically) in the direction of the scene at regularly spaced time instants τ n . Following the "start-stop" approximation, at a given τ n , the output of the receiver is a function of the "fast-time" t [1,2] denoted by s(t, τ n ) and known as "raw-data". Its frequency domain expression is…”

Section: Sar Signal Modelmentioning

confidence: 99%

“…The backprojection algorithm [1,2] is a frequently used technique in Synthetic Aperture Radar (SAR) image formation. Assuming that the data acquired by the SAR sensor are available over a uniform grid in the frequency domain, SAR backprojection consists of two-steps.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, for a fixed M and depending on the interpolation scheme, the dominant term can become P (M ) or O(M 2 log M ). Thus, the choice of the interpolator is critical to obtain a favourable tradeoff between accuracy and processing speed, since accurate but slow interpolators can make the approach for very large data sets even impractical [2]. In other words, proper interpolators can enable the algorithm to have the same asymptotic computational complexity of a standard FFT.…”

Section: Introductionmentioning

confidence: 99%

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Fast Gpu-Based Interpolation for Sar Backprojection

Capozzoli¹,

Curcio²,

Liseno³

2013

PIER

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Abstract-We introduce and discuss a parallel SAR backprojection algorithm using a Non-Uniform FFT (NUFFT) routine implemented on a GPU in CUDA language.The details of a convenient GPU implementation of the NUFFT-based SAR backprojection algorithm, amenable to further generalizations to a multi-GPU architecture, are also given.The performance of the approach is analyzed in terms of accuracy and computational speed by comparisons to a "standard", parallel version of the backprojection algorithm exploiting FFT + interpolation instead of the NUFFT. Different interpolators have been considered for the latter processing scheme. The NUFFT-based backprojection has proven significantly more accurate than all the compared approach, with a computing time of the same order. An analysis of the computational burden of all the different steps involved in both the considered approaches (i.e., standard and NUFFT backprojections) has been also reported.Experimental results against the Air Force Research Laboratory (AFRL) airborne data delivered under the "challenge problem for SAR-based Ground Moving Target Identification (GMTI) in urban environments" and collected over circular flight paths are also shown.

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Section: Sar Signal Modelmentioning

confidence: 99%

Section: Sar Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Gpu-Based Interpolation for Sar Backprojection

Capozzoli¹,

Curcio²,

Liseno³

2013

PIER

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“…Further details on SAR processing are available in [7]. Figure 1 provides an example of one such surface, with Doppler (in discrete bins, roughly corresponding to target radial velocity with respect to the platform) on the horizontal axis and range (in discrete bins) on the vertical axis.…”

Section: Technical Backgroundmentioning

confidence: 99%

Detection and tracking of prominent scatterers in SAR data

et al. 2012

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Tracking prominent scatterers provides a mechanism for scene-derived motion compensation of Synthetic Aperture Radar (SAR) data. Such a process is useful in environments where GPS is unavailable and a lack of precise sensor position data makes standard motion compensation difficult. Our approach to sensor positioning estimates range histories of multiple isolated scatterers with high accuracy, then performs a geometric inversion to locate the scatterers in three dimensions and estimate the platform's motion.For high-accuracy scatterer range tracking, we first detect prominent scatterers using a CFAR criterion automatic algorithm and then track them with a two-input Kalman Filter (KF) operation. These two steps provide accurate range estimates of multiple scatterers over a sequence of SAR pulses. The KF state space is range and range-rate. We derive data inputs to the KF algorithm from multiple SAR pulses, divided into Coherent Processing Intervals (CPI). Within each CPI, individual scatterer peak amplitudes and phases are available to the algorithm.Our approach to scene-derived motion compensation combines the high accuracy range history estimates with a novel three-dimensional geometric inversion. This geometric inversion uses the range histories to estimate both 3D scatterer location and 3D relative motions of the radar. We illustrate our KF-based approach to high-accuracy tracking and demonstrate its application to estimating scene scatterer locations on synthetic and real collected SAR data.

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An Autofocus Technique for Sub-aperture Processing of Video-SAR

Min

2015

Lecture Notes in Electrical Engineering

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SAR image formation toolbox for MATLAB

Cited by 192 publications

References 21 publications

Fast Gpu-Based Interpolation for Sar Backprojection

Fast Gpu-Based Interpolation for Sar Backprojection

Detection and tracking of prominent scatterers in SAR data

An Autofocus Technique for Sub-aperture Processing of Video-SAR

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