Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

Contributions in inverse synthetic aperture radar imaging

Ph.DDOCTOR OF PHILOSOPH

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Implementation of tracking algorithms for multistatic systems

Due to the increased prevalence of ubiquitous communication technologies and the reduced cost of electronic components, there is an increasing interest in developing networked radar systems. Such networked radar systems offer potential benefits in robustness as well as improvements in performance for detection, tracking and classification. As a branch of applied computer sciences sensor data fusion addresses the ability to process this vast quantity of information, generated by multiple sources, in an effective way. The purpose of this thesis is to validate the tracking algorithms implemented, to determine whether they are capable of identifying and tracking two closely spaced targets, to determine the capability of the system to track a target that moves with fast maneuvers as well as the ability to handle a potential simultaneous attack from both the air and the sea. We present a method for multiple target tracking using multiple sensors both for passive and active sensors. Firstly, regarding active radar, we describe an algorithm for combining range-Doppler data from multiple sensors to perform multi-target tracking. In particular we considered the problem of very poor azimuth resolution. In this case more than two sensors are needed to triangulate target tracks and techniques like multilateration are needed to overcome the problem. Then two tracking algorithms for bistatic DVB-T passive radar based on the Extended Kalman Filter (for single target tracking) and on the Kalman filter (for multiple target tracking), exploiting measurement of bistatic range and bistatic velocity of a target are described. Also the direction of arrival of the target is estimated through beamforming and then used in the tracking model. The algorithms have been tested and validated by using real data

Investigation of non-cooperative target recognition of small and slow moving air targets in modern air defence surveillance radar

This thesis covers research in the field of non-cooperative target recognition given the limitations of modern air defence surveillance radars. The potential presence of low observable manned or unmanned targets within the vast surveillance volume demand highly sensitive systems. This may again introduce unwanted detections of single birds of comparable radar cross section, previously avoided by use of wide clutter rejection filters and sensitivity time control. The demand for methods effectively separating between birds and slow moving manmade targets is evident. The research questions addressed are connected to identification of characteristic features of birds and manmade targets of comparable size. Ultimately the goal has been to find methods that can utilize such features to effectively distinguish between the classes. In contrast to the vast majority of non-cooperative target recognition publications, this thesis includes non-rigid targets covering a range of dielectric properties and targets falling in the resonant and Rayleigh scattering regions. These factors combined with insufficient spatial resolution for classification require alternative approaches such as utilization of periodic RCS modulation, micro-Doppler- and polarimetric signatures. Signatures of birds and UAVs are investigated through electromagnetic prediction and radar measurements. A flexible and fully polarimetric radar capable of simultaneous operation in both L- and S-band is developed for collection of relevant signatures. Inspired by the use of polarimetric radar for classification of precipitation covered in the weather radar literature, focus has been on using similar methods to recognize signatures of rotors, propellers and bird wings. Novel micro-Doppler signatures combining polarimetric information from this sensor is found to hold information about the orientation of such target parts. This information combined with several other features is evaluated for classification. The benefit from involving polarimetric measurements is especially investigated, and is found to be highly valuable when information provided by other methods is limited

Battle damage assessment using inverse synthetic aperture radar (ISAR)

An imaging radar, like ISAR, offers a combatant the capability to perform long range surveillance with high quality imagery for positive target identification. Extending this attractive feature to the battle damage assessment problem (BDA) gives the operator instant viewing of the target's behavior when it is hit. As a consequence, immediate and decisive action can be quickly taken (if required). However, the conventional Fourier processing adopted by most ISAR systems does not provide adequate time resolution to capture the target's dynamic responses during the hit. As a result, the radar image becomes distorted. To improve the time resolution, time-frequency transform (TFT) methods of ISAR imaging have been proposed. Unlike traditional Fourier-based processing, TFT's allows variable time resolution of the entire event that falls within the ISAR coherent integration period to be extracted as part of the imaging process. We have shown in this thesis that the use of linear Short Time-Frequency Transforms allows the translational response of the aircraft caused by a blast force to be clearly extracted. The TFT extracted images not only tell us how the aircraft responds to a blast effect but also provides additional information about the cause of image distortion in the traditional ISAR display.http://archive.org/details/battledamagesses109451223Approved for public release; distribution is unlimited

NASA thesaurus. Volume 2: Access vocabulary

The access vocabulary, which is essentially a permuted index, provides access to any word or number in authorized postable and nonpostable terms. Additional entries include postable and nonpostable terms, other word entries and pseudo-multiword terms that are permutations of words that contain words within words. The access vocabulary contains almost 42,000 entries that give increased access to the hierarchies in Volume 1 - Hierarchical Listing

NASA thesaurus. Volume 2: Access vocabulary

The Access Vocabulary, which is essentially a permuted index, provides access to any word or number in authorized postable and nonpostable terms. Additional entries include postable and nonpostable terms, other word entries, and pseudo-multiword terms that are permutations of words that contain words within words. The Access Vocabulary contains 40,738 entries that give increased access to the hierarchies in Volume 1 - Hierarchical Listing

Ground clutter mitigation for slow-time MIMO radar using independent component analysis

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets