Optical SAR processor for space applications

In the last few years, a considerable effort has been expended on the development of a number of both active and passive guided-wave devices, such as laser diodes, electrooptic modulators, acoustooptic transducers, photodetectors and microlens arrays, for optical signal processing applications including optical computing 1-3. Many simple optical devices can be easily fabricated by using mature and reproducible technologies. The interest in optical processors involving simple devices is mainly due to a lower power consumption, reduced size, cost and weight and high throughput with respect to the corresponding electronic processors. A number of guided-wave optical circuits for communications, signal processing and optical computing have been demonstrated using the acoustooptic, magnetooptic and electrooptic effects 4. Moreover, fields of application of particular interest are synthetic aperture radar (SAR), remote sensing and beam forming of linear array antennas, which are all well suited for optics-based processing because of the parallel processing allowed by optical architectures. Therefore, guided-wave optical processors can be successfully applied to the optical control of microwaves, to the reconstruction of two-dimensional images using both spatial and time integration, to data classification and identification in satellite applications and for performing a number of signal processing and optical computing functions.

Synthetic Aperture Radar (SAR) satellite power system has the characteristics of high power, pulse power supply and fast response speed. Based on the combination of domestic and foreign SAR satellite power supply structures, a SAR satellite power supply system scheme with a composite bus architecture is proposed in this paper. By adding battery discharge regulators (BDR) between the platform bus and the SAR load bus as a grid-connected controller, the SAR load bus can supply power to the platform load, and the solar array and battery pack can be used efficiently while achieving dual bus output. Aiming at the composite bus, this paper also proposes an efficient high-power filter technology. Finally, the ground test results verify the correctness of the scheme, which can meet the different power supply requirements of the SAR satellite stable and pulse load equipment.

Synthetic Aperture Radar (SAR) satellites are complex systems that usually require long development periods and lot of investment. In 2013, a study of an innovative bi-static SAR concept based on a passive, receive-only Companion Satellite (CS) to a full SAR satellite was performed by ESA and the Industry to design a small, low-cost and fast-track implementation satellite to fly with SAOCOM-1 satellite that acts as an illuminator. SAOCOM is a constellation of two L-band SAR satellites (1A and 1B) under development in Argentina (CONAE). The CS was initially conceived to be launched together with the 1B satellite onboard a Falcon-9 rocket in 2019 and fly in formation to acquire the echoes from its SAR payload. Thus, in addition to the performance requirements, the launch conditions and the challenging implementation schedule drove the design of SAOCOM-CS, forcing the use of components with high technology readiness levels. These led to the CS payload design, including the L-band SAR antenna and the external synchronization antenna for a non-collaborative bistatic SAR mission.

Approximately 70% of South Korea’s land is mountainous, and as expressways and national highways have expanded across this terrain, extensive roadside slopes have been formed. Although various reinforcement and stabilization measures were applied during road construction, failures, such as collapses and settlements, continue to occur due to heavy rainfall, typhoons, and other extreme weather events. In recent years, South Korea has initiated research efforts to utilize imagery acquired from Synthetic Aperture Radar (SAR) satellites to detect long-term ground displacements along roadside slopes and support preparedness for potential geohazards. This study assessed the detectability of ground displacements on road slopes by considering the motion direction and incidence angle characteristics of SAR satellites over the Korean Peninsula. SAR satellites can acquire high-resolution images regardless of daylight or weather conditions. Furthermore, interferometric techniques (InSAR) enable the generation of digital elevation model (DEM) with sub-meter accuracy and ground displacement estimations with millimeter-level precision. However, as radar satellites observe the Earth at oblique angles relative to their flight direction and employ side-looking geometry perpendicular to the line of sight, geometric distortion may occur in areas with highly irregular terrain. In such conditions, shadowing effects—including layover and radar shadow can arise where radar signals are unable to reach. Steep road slopes are particularly susceptible to these limitations, and accurate observation may be impossible depending on the satellite’s motion direction and incidence angle. Therefore, before applying SAR data to slope-monitoring studies, it is essential to assess the feasibility of observations for the target slopes. In this study, the motion and observation characteristics of the Sentinel-1B satellite which continuously acquires imagery across the Korean Peninsula at approximately 12 day intervals were analyzed to evaluate the feasibility of observing road slopes nationwide. To evaluate the SAR observation feasibility for road slopes in South Korea, the evaluation was conducted in three stages: 1. Road-slope information, road networks, and other required datasets were compiled and converted into geospatial (Geographic Information System) data formats. 2. SAR imagery covering the Korean Peninsula was collected, and satellite motion direction and incidence angles were derived from image header files and metadata, then converted into raster datasets. 3. The relative spatial relationship between the road-slope data and satellite-observation data was analyzed, and based on these results, the observation feasibility of each road slope was evaluated considering the effects of the SAR satellite’s motion direction and incidence angle. As a result, the study evaluated the SAR observation feasibility for approximately 27,000 road slopes across South Korea. Considering the orbit and incidence-angle characteristics of the Sentinel-1B satellite, it was found that about 66% of all slopes were located within the satellite’s effective imaging range.AcknowledgementThis research is based upon work supported by Korea Institute of Civil Engineering & Building Technology(KICT), Project No.20250285-001

Synthetic Aperture Radar (SAR) systems deployed on Unmanned Aerial Vehicles (UAVs) are emerging as a highly promising solution for a wide range of future remote sensing and monitoring applications. The integration of SAR’s well-established capabilities for high-resolution imaging and change detection with the flexibility, maneuverability, and cost-effectiveness of modern UAV platforms unlocks significant potential for innovative sensing missions. However, the practical implementation of SAR on UAVs presents considerable challenges, primarily due to the inherent limitations in UAV flight stability and precise navigation, which degrade image quality. As a result, robust autofocusing techniques are essential to correct motion-induced phase errors and ensure coherent image reconstruction. While numerous autofocusing methods have been proposed in the literature, most are designed under assumptions that do not hold for low-flying UAV platforms. Specifically, conventional algorithms often overlook the strong dependence of the SAR imaging geometry on both azimuth and range positions of the targets, which becomes pronounced at low altitudes. In this study, we introduce a novel autofocusing approach termed Geometry-Aware Entropy Minimization (GAME). The proposed algorithm reconstructs three-dimensional positioning errors by explicitly modeling the spatially variant acquisition geometry along the UAV trajectory. This geometry-aware formulation enables accurate autofocus correction under the challenging conditions associated with low-altitude UAV SAR acquisitions. The effectiveness of the GAME algorithm is demonstrated using real X-band SAR data collected by an octacopter platform, confirming its applicability and performance in practical scenarios.

For a synthetic aperture radar (SAR) system, the platform attitude drift will affect the image qualities. During SAR system design computer simulation can be used to determine the attitude stability which can satisfy the users' demands. In the computer simulation, the first step is to create the mathematic model. General models of platform attitude drift are usually created in the time domain, such as the sinusoid one, and the model presented is done in the frequency domain. For each attitude angle (roll, yaw, pitch), the amplitude and phase values are first generated in the frequency domain. Because the attitude parameters are sampled with PRF (pulse repetition frequency) in SAR system, the maximum frequency is set to PRF/2. The amplitude may be changed with the frequency (for example, inversely proportional to the square of frequency), while the phase may be uniformly distributed between 0 to 2/spl pi/. Then the inverse Fourier transformation converts this data into the time domain. Because the phase center of the SAR's antenna is usually offset from the platform center, the attitude drift will change the slant range between the antenna's phase center and the target on the ground. These changes will affect the Doppler history in azimuth direction for the SAR. After these changes are simulated, the SAR raw data of a point target can be generated. With the general SAR procession, the simulated image is obtained. Using the analysis software, the authors can get the effects on the image qualities of the attitude drift. At last, the simulation steps and an example are also presented.

SAR image formation scheme implementation and endorsement sprouting from Level-0 data decoding

This paper analyzes the synchronization of a bistatic synthetic aperture radar (SAR) system based upon a geostationary transmitter and small receive-only SAR systems mounted on airplanes or unmanned aerial vehicles (UAVs), namely GEO Spaceborne-Airborne Bistatic SAR. This paper will analyze and present our solution of the space synchronization, time synchronization, frequency synchronization and phase synchronization for the GEO Spaceborne-Airborne bistatic SAR respectively. It will first introduce the GEO Spaceborne-Airborne bistatic SAR system and geometry model briefly. Then possible solutions of the system synchronization will be discussed. It will introduce the synchronization scheme based on the direct path between the transmitter and receiver. It uses synchronization modules to realize the time and frequency synchronization. A mathematical model considering the time and frequency synchronization error is given and a phase error analysis is performed. Range and azimuth compressions of a single target are implemented by simulations.

Synthetic Aperture Radar (SAR) satellite an active sensor offering unique high spatial resolution regardless of weather conditions can operate both day and night time with wide area coverage. Therefore, SAR satellite can be used for monitoring ship on sea surface. This study showed on an alternative method for ship detection of SAR data using Pi-SAR-L2 (L-band, JAXA-Airborne SAR) data. The ship detection method is this study was consisted of eight main stages. After the Pi-SAR data was registered and speckle was filtered, then the land was masked using SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data since most ship detectors produced false detections when it applied to land areas. A ship sample image was then selected (cropped). The next step was to detect some unique keypoints of ship sample image using Speeded Up Robust Features (SURF) detector. The maximum distance (‘MaxDist’) of keypoints was also calculated. The same detector was then applied to whole Pi-SAR imagery to detect all possible keypoints. Then, for each detected keypoint, we calculated distance to other keypoint (‘Dist’). If ‘Dist’ was smaller than ‘MaxDist’, then we marked these two (or more) keypoints as neighboring keypoints. If the number of neighbor keypoints was equal or greater than two, finally we marked these keypoints as ‘Detected Ship’ (draw rectangle and show its geographic position). Results showed that our method can detect successfully 32 ‘possible ships’ from Pi-SAR-L2 data acquired on the area of North Sulawesi, Indonesia (August 8, 2012).

The Synthetic Aperture Radar (SAR) system is a kind of modern high-resolution microwave imaging radar used in all-weather and all day long to provide remote sensing means and generate high resolution images of the land under illumination of radar beam. Unlike optical sensors, SAR algorithm needs a post-processing process on the data acquired to form the final image. In this article, we use the General Purpose Graphic Processing Units (GPGPU) to accelerate two of SAR algorithms, PGA (Phase Gradient Autofocus) and PDE (Partial Differential Equations), which are two computational intensive algorithms in the post-processing process for the system. Our work shows that the GPU architecture has different acceleration effects on the two algorithms. PGA can achieve an acceleration of 21.7% and PDE can get a speed up of 2.58\(\times \) on GPGPU. We analyse the reasons for the results and conclude that GPU is a promising platform to accelerate the SAR system.

<p>The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to its predecessor, MASC-3, it can automatically takeoff and land on small patches of land while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows for deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.</p><p>Before its first offshore mission, MASC-V underwent a system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UAS data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UAS for vertical profiling as well as to operate tens of kilometers away from ground personell over open water.</p>

<p>The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to it<span>s</span> predecessor, MASC-3, it can automatically takeoff and land on small patches<span> of land</span> while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows <span>for </span>deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.</p><p><span>Before its first offshore mission, MASC-V underwent a full system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UA</span><span>S</span><span> data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UA</span><span>S </span><span>for vertical profiling as well as</span><span> to operate tens of kilometers away from ground personell over open water.</span></p>

Surveillance and inspection of agricultural status of field is getting more and more important, especially on small scale. The use of modern synthetic aperture radar (SAR) systems is advised to produce a system independent of weather and dust as well as time of day. One challenge is the integration of a SAR system into small aerial vehicles to reach a large range of applications and users. This paper covers the experimental millimeter wave SAR setup using a Pod, which is high flexible and small in size to fit on small manned airplanes. The implementation shown can also be operated on Unmanned Aerial Vehicles (UAVs). Using a millimeter-wave (mmW) system working at 94 GHz ensures, that the used technique including the antennas can be implemented into small payload bays. Measurements of the used IMU compared to movement estimations using a high resolution experimental radar will be shown as well as radar images. (4 pages)

This chapter presents a completely new type of Synthetic Aperture Radar (SAR) system that radiates short carrierless pulses, namely the Impulse SAR (ImpSAR). A SAR system radiates chirped pulses from different equispaced positions along the azimuth, which is usually a straight line; a linear array is thus synthesized. Higher range resolution of ImpSAR requires shorter radiated pulses, implying lower average radiated power, while radiated pulse amplitude is dictated by the currently available hardware technology. In order to implement the polychromatic SAR imaging system, the bandwidth of the scattered pulse must be broken down into subbands, which are processed independently. Raw polychromatic SAR data is processed, yielding a number of microwave images, which are coincident to those obtainable with conventional SAR systems. Polychromatic SAR has the useful capability of generating multiple images simultaneously, which is of particular importance to the target detection and identification process.

This article presents comprehensive validation of specific sea state parameters (SSPs) and synthetic aperture radar (SAR)-derived wind speeds (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>). The article introduces a novel approach to retrieving roughness length (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>) based on wave steepness, following the retrieval of the short wavelengths necessary to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>. The SAR onboard the Sentinel-1 (S1) satellite that was used specifically in the interferometric wide swath mode (IW) data. The data were processed using the extended version of CWAVE (CWAVE_EX) algorithm for SSPs and CMOD5 for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>. CWAVE_EX was developed especially for coastal waters; the processing chain includes steps for SAR image denoising and eliminating image artifacts. SAR S-1 data inherently exhibit a substantial azimuthal cutoff length due to the data's high satellite altitude and SAR IW resolution. That complicates the retrieving of short wavelengths prevalent in coastal zones and needed to retrieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>. The article focuses on the coastal seas of the USA, benefiting from the presence of an extensive network of ocean buoys for validation purposes. The complete SAR S1 A/B archive from 2014 to 2022 was first processed to retrieve SSPs and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>. The validation for significant wave height (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula>), second moment wave period (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula>), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula> was performed using in-situ measurements with about 6000 collocations. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula> were compared against the corresponding parameters from hindcast spectral numerical model data with about 380 000 collocations. The comparisons between the retrieved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula> against the in-situ observations and hindcast wave model data yielded a root mean square error (RMSE) of 0.46–0.50 m and 0.9–1.1 s. The RMSE of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula> against in-situ observation was about 2 m/s with a bias of 0.78 m/s. The estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula> values from satellite-driven wave parameters were highly correlated with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula> estimated from the in-situ observations, with an RMSE of 0.04 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> m and a bias of −0.01 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> m. The article highlights the possibility of using SAR remote sensing data for global mapping of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>, including coastal effects of local variability in sea state and wind field gustiness.