🗊Презентация Synthetic-Aperture Radar (SAR) Image Formation Processing

Synthetic-Aperture Radar (SAR) Image Formation Processing

Outline Raw SAR image characteristics Algorithm basics Range compression Range cell migration correction Azimuth compression Motion compensation Types of algorithms Range Doppler algorithm Chirp scaling algorithm Frequency-wavenumber algorithm (-k or f-k) Comparison of algorithms Processing errors, Computational load, Pros and cons Autofocus techniques

Airborne SAR real-time IFP block diagram

Basic SAR image formation processes

Basic SAR image formation processes

Basic SAR image formation processes

Basic SAR image formation processes

Basic SAR image formation processes

Optical image-formation processing

Demodulated baseband SAR signal [from Digital processing of synthetic aperture radar data, by Cumming and Wong, 2005] Time domain representation After removing the radar carrier cos(fo) from the received signal, the demodulated, complex, baseband signal from a single point target can be represented as

Demodulated baseband SAR signal includes R-4 and target RCS factors

SAR signal spectrum [from Digital processing of synthetic aperture radar data, by Cumming and Wong, 2005] Frequency-domain represention For reasons of efficiency, many SAR processing algorithms operate in the frequency domain. For the low-squint case, the two-dimensional frequency spectrum of the received SAR signal is

SAR signal spectrum Also f : range frequency, Hz, where –Fr /2  f  Fr /2 Fr : range sampling frequency, Hz f : azimuth (Doppler) frequency, Hz fc : absolute Doppler centroid frequency, Hz Wr(f ) : envelope of the radar data’s range spectrum Wa(f ) : envelope of the antenna’s beam pattern Doppler spectrum The relationship between azimuth time to frequency is where

SAR signal spectrum

Matched filter processing Given an understanding of the characteristics of the ideal SAR signal, an ideal matched-filter can be applied using correlation to produce a bandwidth limited impulse response. However this process has limitations as the characteristics of the ideal matched-filter varies with the target’s position in range and azimuth. So while such correlation processing is theoretically possible, it is not computationally efficient and is not appropriate when large-scale image-formation processing is required, e.g., from a spaceborne SAR system.

Range Doppler domain spectrum [from Digital processing of synthetic aperture radar data, by Cumming and Wong, 2005] Range Doppler-domain representation The range-Doppler domain is useful for range-Doppler image formation algorithms. The range-Doppler domain signal is

Range migration

Range-dependent range migration

Range-Doppler processing

Range-Doppler processing

Range-Doppler processing

Range-Doppler algorithm

Range-cell migration compensation Part of the migration compensation requires a re-sampling of the range-compressed pulse using an interpolation process.

Chirp scaling algorithm The range-Doppler algorithm was the first digital algorithm developed for civilian satellite SAR processing and is still the most widely used. However disadvantages (high computational load, limited accuracy secondary-range compression in high-squint and wide-aperture cases) prompted the development of the chirp-scaling algorithm to eliminate interpolation from the range-cell migration compensation step. As the name implies it uses a scaling principle whereby a frequency modulation is applied to a chirp-encoded signal to achieve a shift or scaling of the signal.

Chirp scaling algorithm

Chirp scaling algorithm

Chirp scaling algorithm

Chirp scaling algorithm

Chirp scaling algorithm

Range-cell migration compensation

Omega-K algorithm (WKA) The chirp-scaling algorithm assumes a specific form of the SAR signal in the range Doppler domain, which involves approximations that may become invalid for wide apertures or high squint angles. The Omega-K algorithm uses a special operation in the two-dimensional frequency domain to correct range dependent range-azimuth coupling and azimuth frequency dependence. The WKA uses a focusing step wherein a reference function is multiplied to provide focusing of a selected range. Targets at the reference range are correctly focused while targets at other ranges are partially focused. Stolt interpolation is used to focus the remainder of the targets.

Omega-K algorithm (WKA) Illustration of the range/azimuth cross coupling using the raw phase history from a point target. Range-cell migration introduces a phase change into the azimuth samples in addition to the normal phase encoding. The RCM cross coupling creates an additional azimuth phase term which affects the azimuth FM rate.

Omega-K algorithm (WKA)

Stolt interpolation

Stolt interpolation

Stolt interpolation

Comparison of IFP algorithms

Motion compensation Imperfect trajectories during SAR data collection will distort the data set resulting in degraded images unless these imperfections are removed. Removal of the effects of these imperfections is called motion compensation. Motion compensation requires precise knowledge of the antenna’s phase center over the entire aperture. For example vertical velocity will introduce an additional Doppler shift into the data that, if uncompensated, will corrupt along-track processing. Similarly a variable ground speed will result in non-periodic along-track sampling that, if uncompensated, will also corrupt along-track processing. Knowledge of the antenna’s attitude (roll, pitch, yaw angles) is also important as these factors may affect the illumination pattern as well as the position of the antenna’s phase center.

Motion compensation To provide position and attitude knowledge various instruments are used Gyroscopes (mechanical or ring-laser) Inertial navigation system (INS) Accelerometers GPS receiver

Motion compensation

Motion compensation In addition to position and attitude knowledge acquired from various external sensors and systems, the radar signal itself can provide information useful in motion compensation. The Doppler spectrum can be used to detect antenna pointing errors. The nadir echo can be used to detect vertical velocity (at least over level terrain).

Autofocus Just as non-ideal motion corrupts the SAR’s phase history, the received signal can also reveal the effects of these motion imperfections and subsequently cancel them. This process is called autofocus. Various autofocus algorithms are available Map drift Phase difference Inverse filtering Phase-gradient autofocus Prominent point processing Many of these techniques exploit the availability of a high-contrast point target in the scene.

Quadratic phase errors

High-frequency phase errors

Autofocus – inverse filtering

Autofocus – inverse filtering

Autofocus – phase gradient The phase gradient autofocus algorithm is unique in that it is not model based. It estimates higher order phase errors as it accurately estimates multicycle phase errors in SAR signal data representing images over a wide variety of scenes.

Autofocus – phase gradient