An Autofocus Algorithm for Estimating Residual Trajectory Deviations in Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

Sidelobe reduction is a very primary task for synthetic aperture radar (SAR) images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA) filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA) for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.

Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar, which can work in any weather conditions and is often used for target positioning. Airborne SAR has the advantages of flexible scanning geometry and high spatial resolution compared with Spaceborne SAR, but the quality of SAR image and positioning accuracy is affected by air turbulence and mechanical vibration. In practical applications, it is vital to improve Airborne SAR’s geometric positioning accuracy without ground control points or other reference image information. When positioning a single SAR image with long-range, the Earth surface model is often used as an additional equation to solve the Range-Doppler (RD) model. However, the Earth surface equation is difficult to solve the target height accurately. This study proposed an Airborne SAR target positioning methodology based on a local linear approximation of the Earth surface equation. The proposed local Linear RD (L-RD) model establishes a linear constraint of the local elevation, and the iterative algorithm converges rapidly. To further improve the efficiency of geolocation, we propose a method to directly obtain the initial geolocation used for the iteration based on the geometric relationship. An iterative framework is also proposed to enhance positioning accuracy when DEM is available. The experimental results show that the proposed method can significantly improve the positioning accuracy and reliability of Airborne SAR positioning without control points compared to other methods.

The wavenumber domain algorithm is an ideal solution for high-resolution and highly squinted (HRHS) synthetic aperture radar (SAR) imaging in the case of an ideal straight trajectory. However, for airborne HRHS SAR imaging, the Stolt mapping leads to nonsystematic range cell migration (NsRCM) and secondary range compression (SRC) for the HRHS SAR, which causes the HRHS SAR image to defocus severely. To obtain a well-focused HRHS SAR image, a new autofocusing algorithm for HRHS SAR imagery based on an equivalent broadside model is proposed in this article. After coarse motion compensation, linear range walk correction and azimuth resampling are employed to transform the HRHS SAR data into the equivalent broadside SAR data, which has been proved to greatly reduce NsRCM and SRC. After that, the motion error prior structure in the 2-D wavenumber domain is revealed. According to the structure, a novel 2-D wavenumber domain autofocusing algorithm is proposed by the relationship between the 1-D azimuth phase error and the 2-D wavenumber domain phase error correction. Finally, the well-focused HRHS SAR imagery is obtained. Experiments based on simulated and acquired data are carried out to verify the necessity and effectiveness of the proposed algorithm for HRHS SAR imaging.

This paper proposes using a backpropagation (BP) neural network for the classification of ship targets in airborne synthetic aperture radar (SAR) imagery. The ship targets consisted of 2 destroyers, 2 cruisers, 2 aircraft carriers, a frigate and a supply ship. A SAR image simulator was employed to generate a training set, a validation set, and a test set for the BP classifier. The features required for classification were extracted from the SAR imagery using three different methods. The first method used a reduced resolution version of the whole SAR image as input to the BP classifier using simple averaging. The other two methods used the SAR image range profile either before or after a local-statistics noise filtering algorithm for speckle reduction. Performance on an extensive test set demonstrated the performance and computational advantages of applying the neural classification approach to targets in airborne SAR imagery. Improvements due to the use of multi-resolution features were also observed.

In unmanned aerial vehicle (UAV) synthetic aperture radar (SAR), step-frequency mode is usually used due to the limitation of size and weight. Due to the small size and weight, the trajectory deviation is so serious that the motion compensation becomes very complex. In order to estimate and compensate the motion error in UAV SAR, a two-step motion compensation approach is proposed in this paper. Firstly, the global positioning system (GPS) and inertial navigation system (INS) data is used for the coarse compensation to eliminate the major motion error. Secondly, fine phase error compensation based on raw SAR data is processed to circumvent the residual phase error. The fine phase error is estimated by weighted phase gradient autofocus (WPGA) in each sub-pulse of step-frequency mode. Then the weight least square (WLS) method is used to obtain the final focusing phase from the estimated phase of sub-pulse. Real data experiments show that the proposed method is suitable for UAV SAR motion compensation.

In this article, we propose an entire processing chain that addresses the geometrical defocusing problems caused by a large orbital arc, a high squint, and topography variations in ultra-high-resolution (UHR) synthetic aperture radar (SAR) operations. Slant-range histories are formulated using fifth-order polynomials to accurately express radar signals with a UHR spaceborne and squint SAR geometry. SAR focusing is first conducted with a constant height assumption. Thereafter, residual topography errors are compensated. Several beneficial contributions are included in the proposed processing chain. First, to achieve constant-height SAR focusing, we improve the typical least-square decomposition-based Stolt interpolations in terms of the image quality and computation time. Second, we clarify how illuminated areas are mapped in a two-dimensional imaging domain after polynomial-based focusing, which is completely different from the well-known relationship between the closest range and zero-Doppler time. Third, a generalized blockwise postfocusing method is devised to correct the range and azimuth defocusing effects induced by height variations. These contributions are verified using numerical simulations. Thus, we can conclude that the proposed method can provide focused SAR images for a height-variant geometry in UHR spaceborne and squint operations.

The SAR (synthetic aperture radar) system simulation is a useful tool for the design of an SAR system. It can predict the system performance before the SAR is realized. We present the design concept of a general purpose SAR simulation package, which can deal with both the airborne and spaceborne SARs, and also the multi-polarization, multi-frequency and interferometric SARs. This software package must be modular so that the different models can be selected for each subsystem. It consists of three parts. The first one is design entry for the model creation and representation. The second part is a simulation controller which selects the different models of the SAR subsystems, and generates the SAR raw data. The third part is the data processing and analysis tools which consists of a general SAR processor, waveform analysis tools and image analysis tools.

Airborne synthetic aperture radar (SAR) system is an essential tool for modern remote sensing applications. The aircraft is easily affected by the atmospheric turbulence, leading to deviations from the ideal track. To enable high-resolution imagery, a navigation system is usually mounted on the aircraft. Due to the limitation of the navigation system's accuracy, motion errors estimated from the SAR raw data are needed. In this paper, a novel motion compensation algorithm, which is based on the instrumental variables (IV) method, is proposed. We call this IV-based algorithm the IVA algorithm. In this algorithm, double-derivative motion errors are estimated without modeling the random disturbances to be a zero-mean Gaussian distribution and to be created from mutually independent noise, which makes it more robust and accurate in focusing SAR images. Before the motion error estimation, a Savitzky–Golay filter is performed to reduce the phase estimation errors, in which the phase is obtained by the phase gradient autofocus algorithm. Finally, the estimated motion errors are used to compensate the received signal with the range-dependent model. The IVA algorithm is validated by using real airborne SAR data, and experimental results show that the proposed algorithm achieve an excellent performance in airborne SAR systems.

With the bandwidth restriction in airborne synthetic aperture radar (SAR)-based human-in-the-loop applications, the acquired SAR images should be compressed with loss to overcome the conflict of image quantity and the response time. In this paper we described a framework of SAR image compression. The SAR image is decomposed into two components, namely structural and textural components. The target region mask is used to retain the important target information by allocating more bits during compression, while less bits is allocated for the background region. The obtained result showed that the compressed image using the proposed algorithm has better visual effect under the same bit rate compared with JPEG2000 algorithm.

This paper addresses theoretical aspects of forming images from an airborne synthetic aperture radar (SAR) of targets buried below the Earth's surface. Soil is generally a lossy, dispersive medium, with wide ranging variability in these attributes depending on soil type, moisture content, and a host of other physical properties. Focussing a SAR subsurface image presents new dimensions of complexity relative to its surface-image counterpart, even when the soil's properties are known. This paper treats the soil as a lossy, dispersive half space, and presents a practical model for the radar echo-delay time to point scatterers within it. This model is then used to illustrate effects of refraction, dispersion, and attenuation on a SAR's phase histories, and the resulting image. Various data collection geometries and processing strategies are examined for both two-dimensional and three-dimensional SAR images. The conclusions from this work are that (1) focussing a SAR image must generally take into account both refraction and dispersion, (2) resolving targets at different depths in lossy soils requires perhaps unprecedented sidelobe attenuation, that for some soils may only be achievable with specialized window functions, (3) the impulse response of the soil itself places a practical limit on the usable bandwidth of the radar, and (4) dynamic ranges and sensitivities will need to be orders of magnitude greater than typical surface-imaging SARs, leading to significant impact on SAR parameters, for example compressing the usable range of pulse repetition frequencies (PRFs). >

With the bandwidth restriction in airborne Synthetic Aperture Radar (SAR)-based human-in-the-loop applications, the acquired SAR images should be compressed with loss to overcome the conflict of image quantity and the response time. In this letter a framework of SAR image compression is described. The SAR image is decomposed into two components, namely structural and textural components. The target region mask is used to retain the important target information by allocating more bits during compression, while less bits is allocated for the background region. The obtained results show that the compressed image using the proposed algorithm has better visual effect under the same bit rate compared with JPEG2000 algorithm.

Oceanology is playing an important role in economy and social development. Ocean surveillance is of great importance for protecting maritime interests, marine pollution monitoring and shipping management. Microwave sensing of the ocean using synthetic aperture radar (SAR) is effective and of great interest because of the high spatial resolution, high precision information extraction capability and the almost instantaneous observation potential of large ocean areas. Airborne SAR can implement instantaneous detection to specific area, which is very valuable for emphasis marine area and emergency surveillance. In this paper, an airborne ocean surveillance SAR system is introduced and its application experiment results are demonstrated. Experiment results show the great potential of SAR in ocean applications.

Nonlinear frequency modulation (NLFM) waveforms have been investigated in recent years. The main advantages of the waveform include fine resolution, good signal-to-noise ratio, and good interference mitigation. However, for synthetic aperture radar (SAR), there are hitherto no real applications of NLFM. It is known that Doppler intolerance is the drawback of NLFM waveform. Therefore, in this paper, the two-dimensional NLFM SAR signal model with Doppler shift is formulated and the influences of Doppler shift on airborne SAR imaging are analysed in detail. Furthermore, the first experimental result of airborne NLFM SAR is demonstrated.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.