REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms

The Atmospheric Imaging Radar for High Resolution Observations of Severe Weather

Mobile weather radars often utilize rapid scan strategies when collecting obser- vations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center at the University of Oklahoma is engaged in the design, construction and testing of a mobile imaging weather radar system called the Atmospheric Imaging Radar (AIR).Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamform- ing techniques. Adaptive algorithms allow for the improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during three severe weather events in Oklahoma, including an isolated cell event with high surface winds, a squall line, and a non-tornadic cyclone. Sev- eral digital beamforming techniques were tested and analyzed, producing unique, simultaneous multi-beam measurements using the AIR.The author made specific contributions to the field of radar meteorology in several areas. Overseeing the design and construction of the AIR was a signif- icant effort and involved the coordination of many smaller teams. Interacting with the members of each group and ensuring the success of the project was a primary focus throughout the venture. Meteorological imaging radars of the past have typically focused on boundary layer or upper atmospheric phenomena. The AIR's primary focus is to collect precipitation data from severe weather. Ap- plying well defined beamforming techniques, ranging from Fourier to adaptive algorithms like robust Capon and Amplitude and Phase Estimation (APES), to precipitation phenomena was a unique effort and has served to advance the use of adaptive array processing in radar meteorology. Exploration of irregular antenna spacing and drawing from the analogies between temporal and spatial process- ing led to the development of a technique that reduced the impact of grating lobes by unwrapping angular ambiguities. Ultimately, the author leaves having created a versatile platform capable of producing some of the highest resolution weather data available in the research community today, with opportunities to significantly advance the understanding of rapidly evolving weather phenomena and severe storms

DOA Signal Identification Based on Amplitude and Phase Estimation for Subarray MIMO Radar Applications

The overlapped equal subarray transmit radar, which is also known as the Subarray Multiple-Input Multiple-Output radar, utilizes the key advantages simultaneously of both types of multi-antenna radar, i.e. the phased array and MIMO radars, so that it is able to detect multiple targets even though it has a radar cross section (RCS) of a weak or small target. In this paper, it is proposed to develop a parameter estimation approach called amplitude and phase estimation (APES). This approach provides improved resolution to the estimation of the amplitude and direction of arrival (DoA) of the target reflection signal on the radar compared to the existing conventional estimation methods such as least squares (LS). The formulation of the APES method on this radar is based on the tested parameters such as DoA and RCS and continuously being evaluated. The results show that the performance of the APES method of this radar can detect targets very precisely when the number of subarrays (M) is greater than the number of detection targets (P), precisely M > P. For the results of DoA and RCS accuracy from the APES method, this radar is more accurate than the LS when testing the angular resolution between the two targets, an angle resolution of 2° is obtained for the APES method which is superior to the LS with an angle resolution of 5.8°. In these conditions, the APES method is able to accurately distinguish between two targets while the LS method is only able to detect one target

Broadband, ultra-sparse array processing for low complexity multibeam sonar imaging

Imaging sonar systems have become increasingly popular in numerous applications associated with underwater imaging. Though multibeam sonar systems have been used in a variety of applications, the cost of these systems limits their use. The reason for the high costs has been identified to the use of large number of hydrophone array elements and hence large number of associated analogue channels and analogue-to-digital converters (ADC) that are required in high resolution imaging. In this thesis, an imaging sonar system has been developed with as few as four array elements to minimise cost. The inter-element spacing between any two array elements was chosen to be much greater than half the wavelength. In order to avoid phase ambiguity associated with wide array element spacing, the time difference of arrival is determined. Hence, for this purpose a wideband chirp signal was used. The return signals were divided into range cells to determine the target range. The time difference of arrival was obtained by correlating the range cells. Using the time difference of arrival, the direction of arrival (DOA) angle was calculated. The image of the target being illuminated was formed using the calculated range and the DOA values. The image pixel intensity at any pixel position was determined from the correlation result between the range cells. A simulation model was built to test the theory developed. Simulations were performed for various inter-element spacing and for four different target profiles types. Two objective metrics (signal to noise (SNR) ratio and peak signal to noise (PSNR) ratio) and a subjective metric (Structural Similarity (SSIM) index) were used to determine the performance of the algorithm and image quality. Image formed from the simulations using two hydrophone elements showed the presence of artefacts in the form of correlation sidelobes. The SNR metric showed a low gain of -5dB on comparison against a test image. PSNR and SSIM ratio showed a constant image quality over all the array spacing. The number of array elements was increased and linear operation like averaging was applied. The results showed no improvement in the gain and image quality. ii To overcome the problem of correlation sidelobes, a non-linear combining process has been proposed. Using the non-linear combining process it was found that the SNR showed an average gain of 10 dB on simulated data over the images formed without it. The PSNR and SSIM also showed a small increase in the image quality. The computational complexity of the proposed non-linear combining process was calculated by determining the number of multiplications and additions. The time taken to perform these operations on a SHARC ADSP 21261 chip was calculated theoretically. The calculations showed the feasibility of using this algorithm on a digital signal processing (DSP) hardware. An experimental prototype was built and performance was tested during sea trials. The data obtained was processed using a computer. The experimental results verified that the processing algorithm was effective in a practical system.EThOS - Electronic Theses Online ServiceUniversities UK : Newcastle UniversityGBUnited Kingdo

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Array Signal Processing for Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a kind of imaging radar that can produce high resolution images of targets and terrain on the ground. At present, most of SAR processing algorithms are based on matched filtering. This method is easy to implement and can produce stable results. However, It also has some limitations. This approach must obey the Nyquist sampling theorem and the resolution depends on bandwidth of pulses. This means that the matched filter approach must be based on a large amount of raw data but the performance is limited. With the development of radar imaging, it is difficult for the matched filtering approach to meet the requirement of high resolution SAR images. In this thesis, a new processing method based on the least squares (LS) beamforming is utilized in the processing of SAR raw data. The model of SAR simulates a virtual linear array. The processing of SAR data can also be seen as a process of beamforming. The 1- D azimuth direction echo data is processed using the beamforming method. Simulation results based on the least squares design method are compared with the matched filtering method and the conventional beamforming method with different windows

Architectures and Algorithms for the Signal Processing of Advanced MIMO Radar Systems

This thesis focuses on the research, development and implementation of novel concepts, architectures, demonstrator systems and algorithms for the signal processing of advanced Multiple Input Multiple Output (MIMO) radar systems. The key concept is to address compact system, which have high resolutions and are able to perform a fast radar signal processing, three-dimensional (3D), and four-dimensional (4D) beamforming for radar image generation and target estimation. The idea is to obtain a complete sensing of range, Azimuth and elevation (additionally Doppler as the fourth dimension) from the targets in the radar captures. The radar technology investigated, aims at addressing sev- eral civil and military applications, such as surveillance and detection of targets, both air and ground based, and situational awareness, both in cars and in flying platforms, from helicopters, to Unmanned Aerial Vehicles (UAV) and air-taxis. Several major topics have been targeted. The development of complete systems and innovative FPGA, ARM and software based digital architectures for 3D imaging MIMO radars, which operate in both Time Division Multiplexing (TDM) and Frequency Divi- sion Multiplexing (FDM) modes, with Frequency Modulated Continuous Wave (FMCW) and Orthogonal Frequency Division Multiplexing (OFDM) signals, respectively. The de- velopment of real-time radar signal processing, beamforming and Direction-Of-Arrival (DOA) algorithms for target detection, with particular focus on FFT based, hardware implementable techniques. The study and implementation of advanced system concepts, parametrisation and simulation of next generation real-time digital radars (e.g. OFDM based). The design and development of novel constant envelope orthogonal waveforms for real-time 3D OFDM MIMO radar systems. The MIMO architectures presented in this thesis are a collection of system concepts, de- sign and simulations, as well as complete radar demonstrators systems, with indoor and outdoor measurements. Several of the results shown, come in the form of radar images which have been captured in field-test, in different scenarios, which aid in showing the proper functionality of the systems. The research activities for this thesis, have been carried out on the premises of Air- bus, based in Munich (Germany), as part of a Ph.D. candidate joint program between Airbus and the Polytechnic Department of Engineering and Architecture (Dipartimento Politecnico di Ingegneria e Architettura), of the University of Udine, based in Udine (Italy).Questa tesi si concentra sulla ricerca, lo sviluppo e l\u2019implementazione di nuovi concetti, architetture, sistemi dimostrativi e algoritmi per l\u2019elaborazione dei segnali in sistemi radar avanzati, basati su tecnologia Multiple Input Multiple Output (MIMO). Il con- cetto chiave `e quello di ottenere sistemi compatti, dalle elevate risoluzioni e in grado di eseguire un\u2019elaborazione del segnale radar veloce, un beam-forming tri-dimensionale (3D) e quadri-dimensionale (4D) per la generazione di immagini radar e la stima delle informazioni dei bersagli, detti target. L\u2019idea `e di ottenere una stima completa, che includa la distanza, l\u2019Azimuth e l\u2019elevazione (addizionalmente Doppler come quarta di- mensione) dai target nelle acquisizioni radar. La tecnologia radar indagata ha lo scopo di affrontare diverse applicazioni civili e militari, come la sorveglianza e la rilevazione di targets, sia a livello aereo che a terra, e la consapevolezza situazionale, sia nelle auto che nelle piattaforme di volo, dagli elicotteri, ai Unmanned Aerial Vehicels (UAV) e taxi volanti (air-taxis). Le tematiche affrontante sono molte. Lo sviluppo di sistemi completi e di architetture digitali innovative, basate su tecnologia FPGA, ARM e software, per radar 3D MIMO, che operano in modalit`a Multiplexing Time Division Multiplexing (TDM) e Multiplexing Frequency Diversion (FDM), con segnali di tipo FMCW (Frequency Modulated Contin- uous Wave) e Orthogonal Frequency Division Multiplexing (OFDM), rispettivamente. Lo sviluppo di tecniche di elaborazione del segnale radar in tempo reale, algoritmi di beam-forming e di stima della direzione di arrivo, Direction-Of-Arrival (DOA), dei seg- nali radar, per il rilevamento dei target, con particolare attenzione a processi basati su trasformate di Fourier (FFT). Lo studio e l\u2019implementazione di concetti di sistema avan- zati, parametrizzazione e simulazione di radar digitali di prossima generazione, capaci di operare in tempo reale (ad esempio basati su architetture OFDM). Progettazione e sviluppo di nuove forme d\u2019onda ortogonali ad inviluppo costante per sistemi radar 3D di tipo OFDM MIMO, operanti in tempo reale. Le attivit`a di ricerca di questa tesi sono state svolte presso la compagnia Airbus, con sede a Monaco di Baviera (Germania), nell\u2019ambito di un programma di dottorato, svoltosi in maniera congiunta tra Airbus ed il Dipartimento Politecnico di Ingegneria e Architettura dell\u2019Universit`a di Udine, con sede a Udine

Optimisation des formes d'ondes d'un radar d'aide à la conduite automobile, robustes vis-à-vis d'environnements électromagnétiques dégradés

Several driver assistance radars are developed for security and comfort requirements. Their goal is among others to detect the presence of obstacles for collision avoidance. The current demand in terms of automotive radar sensors experience a significant growth and the technologies being employed must ensure good performances especially in an environment degraded by interfering signals of other users. In this thesis, we are interested in developing a radar system which is effective in all situations especially in a multi-user context. For this purpose, we propose novel radar waveforms based on the combination of frequency hopping Costas codes and other pulse compression techniques, using modified Costas signals. The design approach allows to synthesize a significant number of waveforms, thanks to the high diversity introduced. Afterwards, we have exploited two estimation of target parameters approaches. The first one, quite classic, is based on Doppler processing in a coherent pulse train. The second one, recent in the automotive field , is based on the Compressed sensing techniques. An adaptation of these algorithms to proposed signals is discussed in noisy and multi-target environments. All these works contribute in one hand to explore novel radar waveforms, complement to those currently used in automotive radars and in another hand to propose an innovative processing at the receiver level, suited to radar applications in general and automotive ones in particular.Divers radars sont développés pour des besoins d’aide à la conduite automobile de sécurité mais aussi de confort. Ils ont pour but de détecter la présence d’obstacles routiers afin d’éviter d’éventuelles collisions. La demande actuelle en termes de capteurs radars pour l’automobile connaît une croissance importante et les technologies employées doivent garantir de bonnes performances dans un environnement dégradé par les signaux interférents des autres utilisateurs. Dans cette thèse, nous nous intéressons au développement d’un système radar performant en tout lieu et en particulier dans un contexte multi-utilisateurs. A ce propos, nous proposons de nouvelles formes d’ondes qui se basent sur la combinaison des codes fréquentiels de Costas et d’autres techniques de compression d’impulsion en exploitant les signaux de Costas modifiés. La conception adoptée permet, grâce à la diversité introduite, de synthétiser un nombre important de formes d’ondes. Nous avons, ensuite, exploité deux approches d’estimation des paramètres des cibles. La première, plutôt classique, se base sur le traitement Doppler dans un train d’impulsions cohérent. La deuxième, récente dans le domaine automobile, se base sur la technique dite de « Compressed Sensing ». Une adaptation de ces algorithmes pour les signaux proposés a été discutée dans des environnements bruités et multi-cibles. L’ensemble de ces travaux contribue à explorer de nouvelles formes d’ondes, autres que celles utilisées dans les radars actuels et à proposer un traitement innovant en réception, adapté aux radars en général et à l’automobile en particulier

Multiple radar environment emission deinterleaving and PRI prediction

The aim of this study was to research TOA based tracking and deinterleaving algorithms suited to radar emitters in an EW environment for application on the CSIR 5th generation DRFM platform. The research problem statement stipulated that the only defining characteristic of the different emitters be the time of arrival (TOA) of their pulses. The pulse repetition interval (PRI) schemes considered in the study was constant, jittered, staggered and dwell and switch. The different TOA based deinterleaving algorithms investigated were sequence search (SS), TOA difference histogram, CDIF, SDIF, CDIF with SS (CDIF SS), SDIF with SS (SDIF SS) and interleaved pulse train spectrum estimation. The interleaved pulse train spectrum estimation algorithm results could not be replicated and were not included in simulations. The TOA based tracking algorithms that were also investigated were Delta-t histogram, Kalman filter, alpha-beta filter and alpha-beta-gamma filter. The alpha-beta-gamma filter became unstable during simulations and hence their results have also been excluded. The algorithms were simulated in MATLAB against EW environments with varied TOA measurement noise, number of emitters, PRI schemes and interference pulses (missing and spurious). General conclusions drawn from the deinterleaving simulations were the success of the algorithms decrease with the increase of emitters in the EW environment, interference pulses increased the success of some algorithms and the success of algorithms increased with TMNR (time measurement to noise ratio). General conclusions drawn from the tracking simulations were track loss of the algorithms decrease with increase in TMNR, tracking error decreases with increase in TMNR and interference pulses affected the initial estimates used to initialise the filters. The performance of the deinterleaving (CDIF & CDIF SS) and tracking ( Delta-t histogram & alpha-beta filter) algorithms were compared on the DRFM platform. On the DRFM platform, the CDIF algorithm deinterleaved in fewer pulses but had more false detections as compared to the CDIF SS algorithm. The alpha-beta filter performed better with lower TMNR than the Delta-t histogram, on the DRFM platform. The CDIF SS algorithm and alpha-beta filter were chosen, based on their performance on the DRFM, to be implemented on a DRFM based system that would deinterleave and then track emitters in an EW environment. The system was successfully implemented and met all requirements that were placed on it. Possible improvements to the system and the future improvements to the research are also discussed