An Efficient Solution to the Factorized Geometrical Autofocus Problem

This paper describes a new search strategy within the scope of factorized geometrical autofocus (FGA) and synthetic-aperture-radar processing. The FGA algorithm is a fast factorized back-projection formulation with six adjustable geometry parameters. By tuning the flight track step by step and maximizing focus quality by means of an object function, a sharp image is formed. We propose an efficient two-stage approach for the geometrical variation. The first stage is a low-order (few parameters) parallel search procedure involving small image areas. The second stage then combines the local hypotheses into one global autofocus solution, without the use of images. This method has been applied successfully on ultrawideband CARABAS II data. Errors due to a constant acceleration are superposed on the measured track prior to processing, giving a 6-D autofocus problem. Image results, including resolution, peak-to-sidelobe ratio and magnitude values for point-like targets, finally confirm the validity of the strategy. The results also verify the prediction that there are several satisfying autofocus solutions for the same radar data

Factorized Geometrical Autofocus for Synthetic Aperture Radar Processing

This paper describes a factorized geometrical autofocus (FGA) algorithm, specifically suitable for ultrawideband synthetic aperture radar. The strategy is integrated in a fast factorized back-projection chain and relies on varying track parameters step by step to obtain a sharp image; focus measures are provided by an object function (intensity correlation). The FGA algorithm has been successfully applied on synthetic and real (Coherent All RAdio BAnd System II) data sets, i.e., with false track parameters introduced prior to processing, to set up constrained problems involving one geometrical quantity. Resolution (3 dB in azimuth and slant range) and peak-to-sidelobe ratio measurements in FGA images are comparable with reference results (within a few percent and tenths of a decibel), demonstrating the capacity to compensate for residual space variant range cell migration. The FGA algorithm is finally also benchmarked (visually) against the phase gradient algorithm to emphasize the advantage of a geometrical autofocus approach

Factorized Geometrical Autofocus for Synthetic Aperture Radar Processing

Synthetic Aperture Radar (SAR) imagery is a very useful resource for the civilian remote sensing community and for the military. This however presumes that images are focused. There are several possible sources for defocusing effects. For airborne SAR, motion measurement errors is the main cause. A defocused image may be compensated by way of autofocus, estimating and correcting erroneous phase components. Standard autofocus strategies are implemented as a separate stage after the image formation (stand-alone autofocus), neglecting the geometrical aspect. In addition, phase errors are usually assumed to be space invariant and confined to one dimension. The call for relaxed requirements on inertial measurement systems contradicts these criteria, as it may introduce space variant phase errors in two dimensions, i.e. residual space variant Range Cell Migration (RCM). This has motivated the development of a new autofocus approach. The technique, termed the Factorized Geometrical Autofocus (FGA) algorithm, is in principle a Fast Factorized Back-Projection (FFBP) realization with a number of adjustable (geometry) parameters for each factorization step. By altering the aperture in the time domain, it is possible to correct an arbitrary, inaccurate geometry. This in turn indicates that the FGA algorithm has the capacity to compensate for residual space variant RCM. In appended papers the performance of the algorithm is demonstrated for geometrically constrained autofocus problems. Results for simulated and real (Coherent All RAdio BAnd System II (CARABAS II)) Ultra WideBand (UWB) data sets are presented. Resolution and Peak to SideLobe Ratio (PSLR) values for (point/point-like) targets in FGA and reference images are similar within a few percents and tenths of a dB. As an example: the resolution of a trihedral reflector in a reference image and in an FGA image respectively, was measured to approximately 3.36 m/3.44 m in azimuth, and to 2.38 m/2.40 m in slant range; the PSLR was in addition measured to about 6.8 dB/6.6 dB. The advantage of a geometrical autofocus approach is clarified further by comparing the FGA algorithm to a standard strategy, in this case the Phase Gradient Algorithm (PGA)

Autofocus and analysis of geometrical errors within the framework of fast factorized back-projection

This paper describes a Fast Factorized Back-Projection (FFBP) formulation that includes a fully integrated autofocus algorithm, i.e. the Factorized Geometrical Autofocus (FGA) algorithm. The base-two factorization is executed in a horizontal plane, using a Merging (M) and a Range History Preserving (RHP) transform. Six parameters are adopted for each sub-aperture pair, i.e. to establish the geometry stage-by-stage via triangles in 3-dimensional space. If the parameters are derived from navigation data, the algorithm is used as a conventional processing chain. If the parameters on the other hand are varied from a certain factorization step and forward, the algorithm is used as a joint image formation and autofocus strategy. By regulating the geometry at multiple resolution levels, challenging defocusing effects, e.g. residual space-variant Range Cell Migration (RCM), can be corrected. The new formulation also serves another important purpose, i.e. as a parameter characterization scheme. By using the FGA algorithm and its inverse, relations between two arbitrary geometries can be studied, in consequence, this makes it feasible to analyze how errors in navigation data, and topography, affect image focus. The versatility of the factorization procedure is demonstrated successfully on simulated Synthetic Aperture Radar (SAR) data. This is achieved by introducing different GPS/IMU errors and Focus Target Plane (FTP) deviations prior to processing. The characterization scheme is then employed to evaluate the sensitivity, to determine at what step the autofocus function should be activated, and to decide the number of necessary parameters at each step. Resulting FGA images are also compared to a reference image (processed without errors and autofocus) and to a defocused image (processed without autofocus), i.e. to validate the novel approach further

Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

A Generalized Phase Gradient Autofocus Algorithm

The phase gradient autofocus (PGA) algorithm has seen widespread use and success within the synthetic aperture radar (SAR) imaging community. However, its use and success has largely been limited to collection geometries where either the polar format algorithm (PFA) or range migration algorithm is suitable for SAR image formation. In this work, a generalized phase gradient autofocus (GPGA) algorithm is developed which is applicable with both the PFA and backprojection algorithm (BPA), thereby directly supporting a wide range of collection geometries and SAR imaging modalities. The GPGA algorithm preserves the four crucial signal processing steps comprising the PGA algorithm, while alleviating the constraint of using a single scatterer per range cut for phase error estimation which exists with the PGA algorithm. Moreover, the GPGA algorithm, whether using the PFA or BPA, yields an approximate maxi- mum marginal likelihood estimate (MMLE) of phase errors having marginalized over unknown complex-valued reflectivities of selected scatterers. Also, in this work a new approximate MMLE, termed the max-semidefinite relaxation (Max-SDR) phase estimator, is proposed for use with the GPGA algorithm. The Max-SDR phase estimator provides a phase error estimate with a worst-case approximation bound compared to the solution set of MMLEs (i.e., solution set to the non-deterministic polynomial- time hard (NP-hard) GPGA phase estimation problem). Moreover, in this work a specialized interior-point method is presented for more efficiently performing Max- SDR phase estimation by exploiting low-rank structure typically associated with the GPGA phase estimation problem. Lastly, simulation and experimental results produced by applying the GPGA algorithm with the PFA and BPA are presented

A review of synthetic-aperture radar image formation algorithms and implementations: a computational perspective

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for in stance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations. We also compare such implementations in terms of power consumption, execution time, and image quality for the different algorithms used.info:eu-repo/semantics/publishedVersio

Research progress on geosynchronous synthetic aperture radar

Based on its ability to obtain two-dimensional (2D) high-resolution images in all-time and all-weather conditions, spaceborne synthetic aperture radar (SAR) has become an important remote sensing technique and the study of such systems has entered a period of vigorous development. Advanced imaging modes such as radar interferometry, tomography, and multi-static imaging, have been demonstrated. However, current in-orbit spaceborne SARs, which all operate in low Earth orbits, have relatively long revisit times ranging from several days to dozens of days, restricting their temporal sampling rate. Geosynchronous SAR (GEO SAR) is an active research area because it provides significant new capability, especially its much-improved temporal sampling. This paper reviews the research progress of GEO SAR technologies in detail. Two typical orbit schemes are presented, followed by the corresponding key issues, including system design, echo focusing, main disturbance factors, repeat-track interferometry, etc, inherent to these schemes. Both analysis and solution research of the above key issues are described. GEO SAR concepts involving multiple platforms are described, including the GEO SAR constellation, GEO-LEO/airborne/unmanned aerial vehicle bistatic SAR, and formation flying GEO SAR (FF-GEO SAR). Due to the high potential of FF-GEO SAR for three-dimensional (3D) deformation retrieval and coherence-based SAR tomography (TomoSAR), we have recently carried out some research related to FF-GEO SAR. This research, which is also discussed in this paper, includes developing a formation design method and an improved TomoSAR processing algorithm. It is found that GEO SAR will continue to be an active topic in the aspect of data processing and multi-platform concept in the near future

Contribution to ground-based and UAV SAR systems for Earth observation

Mankind's way of life is the main driver of a planetary-scale change that is marked by the growing of human population's demand of energy, food, goods, services and information. As a result, it have emerged new ecological, economical, social and geopolitical concerns. In this scenario, SAR remote sensing is a potential tool that provides unique information about the Earth's properties and processes that can be used to solve societal challenges of local and global dimension. SARs, which are coherent systems that are able to provide high resolution images with weather independence, represent a suitable alternative for EO with diverse applications. Some examples of SAR application areas are topography (DEM generation with interferometry), agriculture (crop classification or soil moisture), or geology (monitoring surface deformation). In this framework, the encompassing objective of the present doctoral work has been part of the implementation and the subsequent evaluation of capabilities of two X-band SAR sensors. On the one hand, the RISKSAR-X radar designed to be operated at ground to monitor small-scale areas of observation and, on the other, the ARBRES-X sensor designed to be integrated into small UAVs. Despite its inherently dissimilar conception, the concurrence of both sensors has been evidenced along this manuscript. By taking advantage of the similarities between them, it has been possible to analogously assess both sensors to obtain conclusions. In this context, the common link has been the development of the polarimetric OtF operation mode of the RISKSAR-X, allowing this sensor to be operated equivalently to the ARBRES-X. Regarding the RISKSAR-X SAR sensor, several hardware contributions have been developed during part of this Ph.D. with the aim of improving the system performance. By endowing the system with the capability to operate in the fully polarimetric OtF acquisition mode, the relative long scanning time has been reduced. It is of great interest since the measured scatterers that present a short term variable reflectivity during the scanning time, such as moving vegetation, may degrade the extracted parameters from the retrieved data and the SAR image reconstruction. During this doctoral activity, it has been studied the image blurring, the decorrelation and the coherence degradation introduced by this effect. Furthermore, a new term in the differential interferometric coherence that takes into account the image blurring has been introduced. Concerning the ARBRES-X SAR system, one of the main objectives pursued during this Ph.D. has been the integration of the sensor into a small UAV MP overcoming restrictions of weight, size and aerodynamics of the platform. The use of this type of platforms is expected to open up new possibilities in airborne SAR remote sensing, since it offers much more versatility than the commonly used fixed wings UAVs. Different innovative flight strategies with this type of platforms have been assessed and some preliminary results have been obtained with the use of the ARBRES-X SAR system. During the course of the present doctoral work, much effort has been devoted to achieve the first experimental repeat-pass interfereometric results obtained with the UAV MP together with the ARBRES-X. Moreover, the sensor has been endowed with fully polarimetric capabilities by applying the improvements developed to the RISKSAR-X radar, which is another example of the duality between both systems. Finally, a vertical and a semicircular aperture have been successfully performed obtaining SLC images of the scenario, which envisages the capability of the UAV MP to perform tomographic images and complete circular apertures in the future. In conclusion, the UAV MP is a promising platform that opens new potentials for several applications, such as repeat-pass interferometry or differential tomography imaging with the realization of almost arbitrary trajectories.El mode de viure de la humanitat és el principal motor d'un canvi a escala planetària que està marcat per la creixent demanda d'energia, d'aliment, de béns, de serveis i d'informació de les poblacions humanes. Com a resultat, han sorgit noves inquietuds ecològiques, econòmiques, socials i geopolítiques. En aquest escenari, la detecció remota SAR és una eina potencial que proporciona informació única sobre les propietats i processos de la Terra que es pot utilitzar per resoldre reptes socials de dimensió local i global. Els SARs, que són sistemes coherents que poden proporcionar imatges d'alta resolució amb independència del temps, representen una alternativa adequada per a l'observació de la Terra. Alguns exemples d'àrees d'aplicació SAR són la topografia (generació de DEM amb interferometria), l'agricultura (classificació de cultius o humitat del sòl) o la geologia (monitoratge de deformació superficial). En aquest context, l'objectiu general del present doctorat ha estat part de la implementació i posterior avaluació de les capacitats de dos sensors SAR de banda X. D'una banda, el radar RISKSAR-X dissenyat per funcionar a terra i monitoritzar àrees d'observació a petita escala i, d'altra, el sensor ARBRES-X dissenyat per ser integrat en petits UAVs. Malgrat la seva concepció inherentment diferent, la concurrència d'ambdós sensors s'ha evidenciat al llarg d'aquest manuscrit. Aprofitant les similituds entre ells, s'han pogut avaluar de forma anàloga els dos sensors per obtenir conclusions. En aquest sentit, el vincle comú ha estat el desenvolupament del mode de funcionament polimètric OtF del RISKSAR-X, permetent que aquest sensor operi de forma equivalent a l'ARBRES-X. Pel que fa al sensor RISKSAR-X, s'han desenvolupat diverses contribucions hardware durant part d'aquest doctorat amb l'objectiu de millorar el rendiment del sistema. En dotar el sistema de la possibilitat d'operar en el mode d'adquisició totalment polarimètric OtF, s'ha reduït el relatiu llarg temps d'escaneig. Això és de gran interès ja que els blancs mesurats que presenten una reflectivitat variable a curt termini, com ara la vegetació en moviment, poden degradar els paràmetres extrets de les dades recuperades i la reconstrucció d'imatges SAR. Durant aquesta activitat doctoral s'ha estudiat el desenfocat de la imatge, la decorrelació i la degradació de la coherència introduïts per aquest efecte. A més, s'ha introduït un nou terme en la coherència interferomètrica diferencial que té en compte el desenfocat de la imatge. Pel que fa al sistema ARBRES-X, un dels principals objectius perseguits durant aquest doctorat ha estat la integració del sensor en un petit UAV MP superant les restriccions de pes, grandària i aerodinàmica de la plataforma. S'espera que l'ús d'aquest tipus de plataformes obri noves possibilitats en la detecció remota SAR aerotransportada, ja que ofereix molta més versatilitat que els UAV d'ales fixes habituals. S'han avaluat diferents estratègies de vol innovadores amb aquest tipus de plataformes i s'han obtingut resultats preliminars amb l'ús del sistema ARBRES-X. Durant el transcurs del present treball, s'ha dedicat molt esforç a assolir els primers resultats experimentals d'interferometria de múltiple passada obtinguts amb l'UAV MP conjuntament amb l'ARBRES-X. A més, el sensor ha estat dotat de capacitats totalment polarimètriques aplicant les millores desenvolupades al radar RISKSAR-X, el qual constitueix un altre exemple de la dualitat entre ambdós sistemes. Finalment, s'han realitzat amb èxit una apertura vertical i semicircular obtenint imatges SLC de l'escenari, el qual permet preveure la capacitat de l'UAV MP per a realitzar imatges tomogràfiques i apertures circulars completes en el futur. En conclusió, l'UAV MP és una plataforma prometedora que obre nous potencials per a diverses aplicacions, com ara la interferometria de múltiple passada o la tomografia diferencial amb la realització de trajectòries gairebé arbitràries.Postprint (published version