Adaptive Illumination Patterns for Radar Applications

The fundamental goal of Fully Adaptive Radar (FAR) involves full exploitation of the joint, synergistic adaptivity of the radar\u27s transmitter and receiver. Little work has been done to exploit the joint space time Degrees-of-Freedom (DOF) available via an Active Electronically Steered Array (AESA) during the radar\u27s transmit illumination cycle. This research introduces Adaptive Illumination Patterns (AIP) as a means for exploiting this previously untapped transmit DOF. This research investigates ways to mitigate clutter interference effects by adapting the illumination pattern on transmit. Two types of illumination pattern adaptivity were explored, termed Space Time Illumination Patterns (STIP) and Scene Adaptive Illumination Patterns (SAIP). Using clairvoyant knowledge, STIP demonstrates the ability to remove sidelobe clutter at user specified Doppler frequencies, resulting in optimum receiver performance using a non-adaptive receive processor. Using available database knowledge, SAIP demonstrated the ability to reduce training data heterogeneity in dense target environments, thereby greatly improving the minimum discernable velocity achieved through STAP processing

Parametric Estimation Techniques for Space-Time Adaptive Processing with Applications for Airborne Bistatic Radar Systems

This thesis considers parametric scenario based methods for Space-Time Adaptive Processing (STAP) in airborne bistatic radar systems. STAP is a multidimensional filtering technique used to mitigate the influence of interference and noise in a target detector. To be able to perform the mitigation, an accurate estimate is required of the associated space-time covariance matrix to the interference and noise distribution. In an airborne bistatic radar system geometry-induced effects due to the bistatic configuration introduces variations in the angle-Doppler domain over the range dimension. As a consequence of this, clutter observations of such systems may not follow the same distribution over the range dimension. This phenomena may affect the estimator of the space-time covariance matrix.\ua0In this thesis, we study a parametric scenario based approach to alleviate the geometry-induced effects. Thus, the considered framework is based on so called radar scenarios. A radar scenario is a description of the current state of the bistatic configuration, and is thus dependent on a few parameters connected to the two radar platforms which comprise the configuration. The scenario description can via a parametric model be used to represent the geometry-induced effects present in the system. In the first topic of this thesis, an investigation is conducted of the effects on scenario parameter residuals on the performance of a detector. Moreover, two methods are presented which estimate unknown scenario parameters from secondary observations. In the first estimation method, a maximum likelihood estimate is calculated for the scenario parameters using the most recent set of secondary data. In the second estimation method, a density is formed by combination of the likelihood associated with the most recent set of radar observations with a prior density obtained by propagation of previously considered scenario parameter estimates through a dynamical model of the scenario platforms motion over time. From the formed density a maximum a posteriori estimate of the scenario parameters can be derived. Thus, in the second estimation method, the radar scenario is tracked over time. Consequently, in the first topic of the thesis, the sensitivity between scenario parameters and detector performance is evaluated in various aspects, and two methods are investigated to estimate unknown scenario parameters from different radar scenarios.\ua0In the second part of the thesis, the scenario description is used to estimate a space-time covariance matrix and to derive a generalized likelihood ratio test for the airborne bistatic radar configuration. Consequently, for the covariance matrix estimate, the scenario description is used to derive a transformation matrix framework which aims to limit the non-stationary behavior of the secondary data observed by a bistatic radar system. Using the scenario based transformation framework, a set of non-stationary secondary data can be transformed to become more stationarily distributed after the transformation. A transformed set of secondary data can then be used in a conventional estimator to estimate the space-time covariance matrix. Furthermore, as the scenario description provides a representation of the geometry-induced effects in a bistatic configuration, the scenario description can be used to incorporate these effects into the design of a detector. Thus, a generalized likelihood ratio test is derived for an airborne bistatic radar configuration. Moreover, the presented detector is adaptive towards the strength of both the clutter interference and the thermal noise

Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed that performs well in most environments. In this study, a novel subspace form of the PDOF (SPDOF) was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed to the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target clutter ratio (TCR) case

Small business innovation research: Program solicitation

This, the seventh annual SBIR solicitation by NASA, describes the program, identifies eligibility requirements, outlines the required proposal format and content, states proposal preparation and submission requirements, describes the proposal evaluation and award selection process, and provides other information to assist those interested in participating in NASA's SBIR program. It also identifies the Technical Topics and Subtopics in which SBIR Phase 1 proposals are solicited in 1989. These Topics and Subtopics cover a broad range of current NASA interests, but do not necessarily include all areas in which NASA plans or currently conducts research. High-risk high pay-off innovations are desired

Fusion-based impairment modelling for an intelligent radar sensor architecture

An intelligent radar sensor concept has been developed using a modelling approach for prediction of sensor performance, based on application of sensor and environment models. Land clutter significantly impacts on the operation of radar sensors operating at low-grazing angles. The clutter modelling technique developed in this thesis for the prediction of land clutter forms the clutter model for the intelligent radar sensor. Fusion of remote sensing data is integral to the clutter modelling approach and is addressed by considering fusion of radar remote sensing data, and mitigation of speckle noise and data transmission impairments. The advantages of the intelligent sensor approach for predicting radar performance are demonstrated for several applications using measured data. The problem of predicting site-specific land radar performance is an important task which is complicated by the peculiarities and characteristics of the radar sensor, electromagnetic wave propagation, and the environment in which the radar is deployed. Airborne remote sensing data can provide information about the environment and terrain, which can be used to more accurately predict land radar performance. This thesis investigates how fusion of remote sensing data can be used in conjunction with a sensor modelling approach to enable site-specific prediction of land radar performance. The application of a radar sensor model and a priori information about the environment, gives rise to the notion of an intelligent radar sensor which can adapt to dynamically changing environments through intelligent processing of this a priori knowledge. This thesis advances the field of intelligent radar sensor design, through an approach based on fusion of a priori knowledge provided by remote sensing data, and application of a modelling approach to enable prediction of radar sensor performance. Original contributions are made in the areas of intelligent radar sensor development, improved estimation of land surface clutter intensity for site-specific low-grazing angle radar, and fusion and mitigation of sensor and data transmission impairments in radar remote sensing data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Sparsity driven ground moving target indication in synthetic aperture radar

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

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 206, May 1980

This bibliography lists 169 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980