The detection of weak, ground-moving targets can be improved through effective utilization of additional target signal energy collected over an extended dwell time. The signal model used in conventional radar processing limits integration of signal energy over an extended dwell. Two solutions that consider the complexity of the extended-dwell signal model and effectively combine signal energy collected over a long dwell are presented. The first solution is a single-channel algorithm that provides an estimate of the optimal detector to maximize output signal-to-interference-plus-noise ratio for the extended dwell time signal. Rather than searching for the optimal detector in an intractably large filter bank that contains all combinations of phase components, the single-channel algorithm projects dictionary entries against the data to estimate the signal’s linear and nonlinear phase components sequentially with small, phase-specific dictionaries in a multistage process. When used as the detector, the signal model formed from the estimated phase components yields near optimal performance for a wide range of target parameters for dwell times up to four seconds. In comparison, conventional radar processing methods are limited to an integration time of approximately 100 milliseconds. The second solution is a multichannel, multistage algorithm based on element-space pre-Doppler space-time-adaptive processing with two modifications that make it suitable for detection of weak targets whose energy is collected over an extended dwell time. The multichannel solution detects targets with lower radial velocities at significantly lower signal-to-noise ratios (SNRs) than conventional radar processing methods. The decrease in required input SNR for the multichannel solution as compared to conventional methods nearly doubles the detection range for a typical target of interest. Future related research includes extension of these concepts to other radar applications and investigation of algorithm performance for the multiple-target scenario.

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 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.