Synthetic aperture radar (SAR) was first invented in the early 1950s as the remote surveillance instruments to produce high resolution 2D images of the illuminated scene with weather-independent, day-or-night performance. Compared to the Real Aperture Radar (RAR), SAR is synthesising a large virtual aperture by moving a small antenna along the platform path. Typical SAR imaging systems are designed with the basic assumption of a static scene, and moving targets are widely known to induce displacements and defocusing in the formed images. While the capabilities of detection, states estimation and imaging for moving targets with SAR are highly desired in both civilian and military applications, the Ground Moving Target Indication (GMTI) techniques can be integrated into SAR systems to realise these challenging missions. The state-of-the- art SAR-based GMTI is often associated with multi-channel systems to improve the detection capabilities compared to the single-channel ones. Motivated by the fact that the SAR imaging is essentially solving an optimisation problem, we investigate the practicality to reformulate the GMTI process into the optimisation form. Furthermore, the moving target sparsities and underlying similarities between the conventional GMTI processing and sparse reconstruction algorithms drive us to consider the compressed sensing theory in SAR/GMTI applications. This thesis aims to establish an end-to-end SAR/GMTI processing framework regularised by target sparsities based on multi-channel SAR models. We have explained the mathematical model of the SAR system and its key properties in details. The common GMTI mechanism and basics of the compressed sensing theory are also introduced in this thesis. The practical implementation of the proposed framework is provided in this work. The developed model is capable of realising various SAR/GMTI tasks including SAR image formation, moving target detection, target state estimation and moving target imaging. We also consider two essential components, i.e. the data pre-processing and elevation map, in this work. The effectiveness of the proposed framework is demonstrated through both simulations and real data. Given that our focus in this thesis is on the development of a complete sparsity-aided SAR/GMTI framework, the contributions of this thesis can be summarised as follows. First, the effects of SAR channel balancing techniques and elevation information in SAR/GMTI applications are analysed in details. We have adapted these essential components to the developed framework for data pre-processing, system specification estimation and better SAR/GMTI accuracies. Although the purpose is on enhancing the proposed sparsity-based SAR/GMTI framework, the exploitation of the DEM in other SAR/GMTI algorithms may be of independent interest. Secondly, we have designed a novel sparsity-aided framework which integrates the SAR/GMTI missions, i.e. SAR imaging, moving target and background decomposition, and target state estimation, into optimisation problems. A practical implementation of the proposed framework with a two stage process and theoretically/experimentally proven algorithms are proposed in this work. The key novelty on utilising optimisations and target sparsities is explained in details. Finally, a practical algorithm for moving target imaging and state estimation is developed to accurately estimate the full target parameters and form target images with relocation and refocusing capabilities. Compared to the previous processing steps for practical applications, the designed algorithm consistently relies on the exploitation of target sparsities which forms the final processing stage of the whole pipeline. All the developed components contribute coherently to establish a complete sparsity driven SAR/GMTI processing framework.

Conformal arrays possess certain desirable characteristics for deployment on unmanned aerial vehicles and other payload-limited platforms: aerodynamic design, minimal payload weight, increased field of view, and ease of integration with diverse sensor functions. However, the conformal arrays nonplanar geometry causes high adaptive losses in conventional space-time adaptive processing (STAP) algorithms. In this thesis, we develop a conformal array signal model and apply it to evaluate the performance of conventional STAP algorithms on simulated ground clutter data. We find that array-induced clutter nonstationarity leads to high adaptive losses, which greatly burden detection performance. To improve adaptive performance, we investigate the application of existing equivalent-linear-array transformations and develop novel deterministic and adaptive angle-Doppler compensation techniques, which align nonstationary clutter returns. Through the application of these techniques, we are able to nearly fully mitigate the nonstationary behavior yielding performance similar to that of a conventional planar array. Finally, we investigate the impact of array errors on the performance of conformal arrays, and propose several array calibration techniques as ameliorating solutions.

No description available.

In airborne radar systems with Ground Moving Target Indication (GMTI) mode, it is desired to detect the presence of targets in the interference consisting of noise, ground clutter, and jamming signals. These interference components usually mask the target return signal, such that the detection requires suppression of the interference signals. Space-time adaptive processing is a widely used interference suppression technique which uses temporal and spatial information to eliminate the effects of clutter and jamming and enables the detection of moving targets with small radial velocity. However, adaptive estimation of the interference requires high computation capacity as well as large secondary sample data support. The available secondary range cells may be fewer than required due to non-homogeneity problems and computational capacity of the radar system may not be sufficient for the computations required. In order to reduce the computational load and the required number of secondary data for estimation, parametric methods use a priori information on the structure of the clutter covariance matrix. Space Time Auto-regressive (STAR) filtering, which is a parametric adaptive method, and full parametric model-based approaches for interference suppression are proposed as alternatives to STAP in the literature. In this work, space time auto-regressive filtering and model-based GMTI approaches are investigated. Performance of these approaches are evaluated by both simulated and flight test data and compared with the performance of sample matrix inversion space time adaptive processing.

Synthetic aperture radar (SAR) is an imaging technique based on the radio reflectivity of the target being imaged. SAR instruments offer many advantages over optical imaging due to the ability to form coherent images in inclement weather, at night, and through ground cover. High resolution is achieved in azimuth through a synthesized aperture much larger than the physical antenna of the imaging device. Consequently, proper focusing requires accurate information about the relative motion between the antenna phase center and the scene. Any unknown target velocity, acceleration, rotation, or vibration will introduce errors in the image. This work addresses a novel method of focusing a moving target in a SAR image through the estimation of various motion parameters. The target azimuth position is determined through monopulse radar, at which point range velocity and acceleration are estimated across a series of overlapping sub-apertures. Cross-range velocity is then estimated through a search to optimize an image quality metric such as entropy or contrast. A final focused image is then generated based on this velocity vector. Methods of extending this work for a single phase center system are considered. This technique is demonstrated with real radar data from an experimental system, and the performance of this technique is compared both subjectively and with a variety of image metrics to the MITRE keystone technique. Finally, extensions to this current line of research are considered. / text

Synthetic aperture radar (SAR) was originally designed as an airborne ground-imaging radar technology. But it has long been desired to also be able to use SAR imaging systems to detect, locate, and track moving ground targets, a process called Ground Moving Target Indication (GMTI). Unfortunately, due to the nature of how SAR works, it is inherently poorly suited to the task of GMTI. SAR only focuses targets and image features that remain stationary during the data collection. A moving ground target therefore does not focus in a conventional SAR image, which complicates the process of performing GMTI with SAR systems. This thesis investigates the feasibility of performing GMTI with single-channel, unsquinted, broadside stripmap SAR despite this inherent limitation. This study focuses solely on the idealized case of direct energy returns from point targets on flat ground, where they and the airborne radar platform all move rectilinearly with constant speed. First, the various aspects of how SAR works, the signal processing used to collect the SAR data, and the backprojection image formation algorithm are explained. The effects of target motion are described and illustrated in actual and simulated SAR images. It is shown how the backprojection (BPJ) algorithm, typically used to image a stationary landscape scene, can also focus on moving targets when the target motion is known a priori. A SAR BPJ ambiguity function is also derived and presented. Next, the time-changing geometry between the airborne radar and a ground target is mathematically analyzed, and it is shown that the slant range between the radar and any ground target, moving or stationary, is a hyperbolic function of time. It is then shown that this hyperbolic range history causes the single-channel SAR GMTI problem to be underdetermined. Finally, a method is then presented for resolving the underdetermined nature of the problem. This is done by constraining a target's GMTI solution using contextual information in the SAR image. Using constraining information, a theoretical way is presented to perform limited GMTI with a single-channel SAR system by using a modified form of the BPJ imaging algorithm, and practical considerations are addressed that complicate the process. Instead of focusing on stationary pixels, this GMTI method uses the BPJ ambiguity function to search for moving targets on a straight path, such as a road, by performing matched filtering on a collection of moving pixels in a position-velocity image space. Nevertheless, it is concluded that for moving point targets, general GMTI with no path constraints is infeasible in practice with a single-channel SAR.

Synthetic aperture radar (SAR) systems are versatile, high-resolution radar imagers useful for providing detailed intelligence, surveillance, and reconnaissance, especially when atmospheric conditions are non-ideal for optical imagers. However, moving targets in SAR images are smeared. Along-track interferometry is a commonly-used method for extracting the motion parameters of moving targets but requires a dual-aperture SAR system, which may be power- size- or cost-prohibitive. This thesis presents a method of estimating target motion parameters in single-channel SAR data given geometric target motion constraints. I test this method on both simulated and actual SAR data. This estimation method includes an initial estimate, computation of the SAR ambiguity function, and application of the target motion constraints to form a focused image of the moving target. The constraints are imposed by assuming that target motion is restricted to a road. Finally, I measure its performance by investigating the error introduced in the motion estimates using both simulated and actual data.

Ground Moving Target Indication (GMTI) operation relies on clutter suppression techniques for the detection of slow moving ground targets in the presence of strong radar returns from the ground. Space Time Adaptive Processing (STAP) techniques provide a means to achieve this goal by adaptively forming the clutter suppression filter, whose parameters are obtained using an estimated covariance matrix of the clutter data. Therefore, the performance of the GMTI operation is directly aected by the performance of the estimation process mentioned above. Knowledge based techniques are applicable in applications such as the parametric estimation of the clutter covariance matrix and the estimation of the clutter covariance matrix in a nonhomogeneous clutter environment. In this study, a knowledge based approach which makes use of both a priori and instantaneous data is proposed for the mentioned estimation process. The proposed approach makes use of Shuttle Radar Topography Mission (SRTM) data as well as instantaneous platform ownship data in order to determine distributed homogeneous regions present in the region of interest / and afterwards employs Doppler Beam Sharpening (DBS) maps along with the colored loading technique for the blending process of the a priori data and the instantaneous data corresponding to the obtained homogeneous regions. A nonhomogeneity detector (NHD) is also implemented for the elimination of discrete clutter and target-like signals which may contaminate the STAP training data. Simulation results are presented for both the knowledge aided and the traditional cases. Finally, the performance of the STAP algorithm will be evaluated and compared for both cases. Results indicate that by using the developed processing approach, detection of previously undetectable targets become possible, and the overall number of false alarms is reduced.

Space-Time Adaptive Processing (STAP) is an effective method in Ground Moving Target Indicator (GMTI) operation of airborne radars. Clutter suppression is the key to successful MTI operation. Airborne radars are different than the ground based ones in regard to clutter due to the displacement of the platform during operation. When STAP methods are to be investigated, one needs to have accurate signal models while evaluating performance. In this thesis, a comprehensive received signal model is developed first for an airborne antenna array. The impacts of the aircraft motion and irregularities in it, aircraft displacement during reception, intrinsic clutter motion and radar parameters have been accounted in the model and incorporated into a simulator environment. To verify the correctness of the signal simulator, the classical DPCA approach and optimum STAP methods are inspected.

No description available.