An extension of the linear regression model for improved vessel trajectory prediction utilising a priori AIS Information

Thesis (MSc)--Stellenbosch University, 2022.ENGLISH ABSTRACT: As maritime activities increase globally, there is a greater dependency on technology in monitoring, control and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders, and satellite- and terrestrial receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this thesis, we propose a novel method for predicting future locations of vessels by making use of historic AIS data. The proposed method extends a Linear Regression Model (LRM), utilising historic AIS movement data in the form of a priori generated spatial maps of the course over ground (LRMAC). The LRMAC has low complexity and is programmatically easy to implement, and attains accurate prediction results. We first compare the LRM with a Discrete Kalman Filter (DKF) on linear trajectories. We then extend the LRM to form the LRMAC. The LRMAC is compared to another method in literature called the Single Point Neighbour Search (SPNS). For the use case of predicting Cargo and Tanker vessel trajectories, with a prediction horizon of up to six hours, the LRMAC has an improved execution time and performance compared to the SPNS.AFRIKAANSE OPSOMMING: As gevolg van die toename in maritieme aktiwiteite wˆereldwyd, het die afhanklikheid van tegnologie in die monitering, beheer en toesig van vaartuigaktiwiteite ook toegeneem. Een van die mees prominente stelsels vir die monitering van vaartuigaktiwiteit is die Outomatiese Identifikasiestelsel (OIS). ’n Toename in vaartuie wat toegerus is met OIS-transponders, en die toename in satelliet- en terrestri¨ele ontvangers, het gelei tot ’n aansienlike groei in OIS-boodskappe wat wˆereldwyd ontvang is. Dit het weer gelei tot die toename in dataryke ruimte-temporele bronne, wat verband hou met vaartuigaktiwiteite. Dit gee ontleders die vermo¨e om gevorderde vaartuig-bewegingsanalise uit te voer, waarvan ’n toepaslike voorbeeld, die verbetering van vaartuig-liggingvoorspelling is. In hierdie tesis stel ons ’n nuwe strategie voor om toekomstige liggings van vaartuie te voorspel, wat gebruik maak van historiese OIS-data. Die voorgestelde metode brei ’n Lineˆere Regressie Model (LRM) uit, deur gebruik te maak van historiese bewegingsdata en ruimte kaarte van a priori koers oor grond inligting (LRMAK). Die LRMAK het ’n lae kompleksiteit en is programmaties eenvoudig om te implementeer, met relatiewe akkurate voorspelling resultate. Ons vergelyk eers die LRM met ’n Diskrete Kalman Filter (DKF) op lineˆere trajekte. Dan brei ons die LRM uit om die LRMAK te vorm. Die LRMAK word vergelyk met ’n ander metode in literatuur wat die Enkel-punt Buur soektog (EPBS) genoem word. In die geval van trajek-voorspelling vir vrag- en tenkwa-vaartuie, het die LRMAK ’n verbeterde uitvoeringstyd en is vergelykbaar met ’n ander algoritme in literatuur, die EPBS, tot en met ’n voorspellingstydperk van ses-ure.Master

Arrayed synthetic aperture radar

In this thesis, the use of array processing techniques applied to Single Input Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated. In Single Input Single Output (SISO) SAR systems there is a high resolution, wide swath contradiction, whereby it is not possible to increase both cross-range resolution and the imaged swath width simultaneously. To overcome this, a novel beamformer for SAR systems in the cross-range direction is proposed. In particular, this beamformer is a superresolution beamformer capable of forming wide nulls using subspace based approaches. SIMO SAR systems also give rise to additional sets of received data, which includes geometrical information about the SAR and target environment, and can be used for enhanced target parameter estimation. In particular, this thesis looks at round trip delay, joint azimuth and elevation angle, and relative target power estimation. For round trip delay estimation, the use of the traditional matched filter with subspace partitioning is proposed. Then by using a joint 2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival (DOA) estimation can be achieved. Both the use of range lines of raw SAR data and the use of a Region of Interest (ROI) of a SAR image are investigated. However in terms of imaging, MUSIC is not well-suited for SAR, due to its target response not corresponding to the target's true power return. Therefore a joint DOA and target power estimation algorithm is proposed to overcome this limitation. These algorithms provide the framework for the development of three processing techniques. These allow sidelobe suppression in the slant range direction, along with the reconstruction of undersampled data and region enhancement using MUSIC with power preservation.Open Acces

훈련 자료 자동 추출 알고리즘과 기계 학습을 통한 SAR 영상 기반의 선박 탐지

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2021. 2. 김덕진.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vessel’s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.전천후 지구 관측 위성인 SAR를 통한 선박 탐지는 해양 자원의 확보와 해상 안전 보장에 매우 중요한 역할을 한다. 기계 학습 기법의 도입으로 인해 선박을 비롯한 사물 탐지의 정확도 및 효율성이 향상되었으나, 이와 관련된 다수의 연구는 탐지 알고리즘의 개량에 집중되었다. 그러나, 탐지 정확도의 근본적인 향상은 정밀하게 취득된 대량의 훈련자료 없이는 불가능하기에, 본 연구에서는 선박의 실시간 위치, 속도 정보인 AIS 자료를 이용하여 인공 지능 기반의 선박 탐지 알고리즘에 사용될 훈련자료를 자동적으로 취득하는 알고리즘을 제안하였다. 이를 위해 이산적인 AIS 자료를 SAR 영상의 취득시각에 맞추어 정확하게 보간하고, AIS 센서 자체가 가지는 오차를 최소화하였다. 또한, 이동하는 산란체의 시선 속도로 인해 발생하는 도플러 편이 효과를 보정하기 위해 SAR 위성의 상태 벡터를 이용하여 위성과 산란체 사이의 거리를 정밀하게 계산하였다. 이렇게 계산된 AIS 센서의 영상 내의 위치로부터 선박 내 AIS 센서의 배치를 고려하여 선박 탐지 알고리즘의 훈련자료 형식에 맞추어 훈련자료를 취득하고, 어선에 대한 위치, 속도 정보인 VPASS 자료 역시 유사한 방법으로 가공하여 훈련자료를 취득하였다. AIS 자료로부터 취득한 훈련자료는 기존 방법대로 수동 취득한 훈련자료와 함께 인공 지능 기반 사물 탐지 알고리즘을 통해 정확도를 평가하였다. 그 결과, 제시된 알고리즘으로 취득한 훈련 자료는 수동 취득한 훈련 자료 대비 더 높은 탐지 정확도를 보였으며, 이는 기존의 사물 탐지 알고리즘의 평가 지표인 정밀도, 재현율과 F1 score를 통해 진행되었다. 본 연구에서 제안한 훈련자료 자동 취득 기법으로 얻은 선박에 대한 훈련자료는 특히 기존의 선박 탐지 기법으로는 분별이 어려웠던 항만에 인접한 선박과 산란체 주변의 신호에 대한 정확한 분별 결과를 보였다. 본 연구에서는 이와 함께, 선박 탐지 결과와 해당 지역에 대한 AIS 및 VPASS 자료를 이용하여 선박의 미식별성을 판정할 수 있는 가능성 또한 제시하였다.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - 국문 요약문 - 128 - Bibliography - 130 -Maste

Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks

Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept

An experimental synthetic aperture SONAR

Aperture synthesis is a mature technique that has been used with success in a number of remote sensing fields. Sonars can also potentially benefit from the technique, though to date the limitations of slow acoustic propagation and difficulty in maintaining a stable platform has hindered investigation. This thesis investigates aperture synthesis for high resolution underwater imaging. A prototype sonar is designed and fabricated for the study. The performance of the sonar is assessed in both tank and sea trials and the results presented in this thesis

Time domain based image generation for synthetic aperture radar on field programmable gate arrays

Aerial images are important in different scenarios including surface cartography, surveillance, disaster control, height map generation, etc. Synthetic Aperture Radar (SAR) is one way to generate these images even through clouds and in the absence of daylight. For a wide and easy usage of this technology, SAR systems should be small, mounted to Unmanned Aerial Vehicles (UAVs) and process images in real-time. Since UAVs are small and lightweight, more robust (but also more complex) time-domain algorithms are required for good image quality in case of heavy turbulence. Typically the SAR data set size does not allow for ground transmission and processing, while the UAV size does not allow for huge systems and high power consumption to process the data. A small and energy-efficient signal processing system is therefore required. To fill the gap between existing systems that are capable of either high-speed processing or low power consumption, the focus of this thesis is the analysis, design, and implementation of such a system. A survey shows that most architectures either have to high power budgets or too few processing capabilities to match real-time requirements for time-domain-based processing. Therefore, a Field Programmable Gate Array (FPGA) based system is designed, as it allows for high performance and low-power consumption. The Global Backprojection (GBP) is implemented, as it is the standard time-domain-based algorithm which allows for highest image quality at arbitrary trajectories at the complexity of O(N3). To satisfy real-time requirements under all circumstances, the accelerated Fast Factorized Backprojection (FFBP) algorithm with a complexity of O(N2logN) is implemented as well, to allow for a trade-off between image quality and processing time. Additionally, algorithm and design are enhanced to correct the failing assumptions for Frequency Modulated Continuous Wave (FMCW) Radio Detection And Ranging (Radar) data at high velocities. Such sensors offer high-resolution data at considerably low transmit power which is especially interesting for UAVs. A full analysis of all algorithms is carried out, to design a highly utilized architecture for maximum throughput. The process covers the analysis of mathematical steps and approximations for hardware speedup, the analysis of code dependencies for instruction parallelism and the analysis of streaming capabilities, including memory access and caching strategies, as well as parallelization considerations and pipeline analysis. Each architecture is described in all details with its surrounding control structure. As proof of concepts, the architectures are mapped on a Virtex 6 FPGA and results on resource utilization, runtime and image quality are presented and discussed. A special framework allows to scale and port the design to other FPGAs easily and to enable for maximum resource utilization and speedup. The result is streaming architectures that are capable of massive parallelization with a minimum in system stalls. It is shown that real-time processing on FPGAs with strict power budgets in time-domain is possible with the GBP (mid-sized images) and the FFBP (any image size with a trade-off in quality), allowing for a UAV scenario

Impact of Radio Frequency Interference and Real-Time Spectral Kurtosis Mitigation

We catalog the ubiquity of Radio Frequency Interference (RFI) plaguing every modern radio telescope and investigate several ways to mitigate it in order to create better science-ready data products for astronomers. There are a myriad of possible RFI sources, including satellite uplinks and downlinks, cellular communications, air traffic radar, and natural sources such as lightning. Real-time RFI mitigation strategies must take these RFI characteristics into account, as the interfering signals can look significantly different at very high time and frequency resolutions. We examine Spectral Kurtosis (SK) as a real-time statistical RFI detection method, and compare its flagging efficacy against simulated RFI witha wide range of signal characteristics. We found to be weak against signals with a 50% effective duty cycle, as well as low signal-to-noise ratio sidelobe spillover from strong and frequency-wide RFI. Coarsening the SK time resolution improved flagging, as did using multi-scale SK, which averages adjacent time-frequency pixels with small rolling windows to circumvent the weakness to 50% duty cycle signals. Multiscale SK raised flagging above 90% for almost all cases, and as long as the amount of channels included in the multi-scale window wasn’t wider than the RFI signal, there was no significant increase in false positive rate. Simulated realistic incoherent astronomical signals were not detected by SK at all, as expected. To simulate real-time SK RFI detection in a real data set, raw, unaveraged data was taken with the Robert C. Byrd Green Bank Telescope (GBT). The observation targets included one pulsar, two neutral hydrogen (HI) galaxies, the Milky Way HI emission, and a hydroxyl megamaser. These targets are all easily observable on short timescales but are also nearby several sources of RFI. Flagged data was replaced with representative Gaussian noise using the statistics of adjacent time-frequency pixels. We run different variations of SK detection on copies of the raw datasets and compared to the original, to see how well the RFI was removed and if the science data product was affected in any way. The spectral line targets are all completely ignored by SK , while the pulsar results decreased in quality due to the noise replacement averaging over the time variable structure, unless care was taken to flag data on timescales shorter than the pulse length. In these cases, single pulse signal-to-noise ratio was marginally improved

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

Earth resources: A continuing bibliography, issue 46

This bibliography lists 467 reports, articles and other documents introdcued into the NASA scientific and technical information system between April 1 and June 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental cultural resources geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis