Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

In evolving radar systems, detection is regarded as a fundamental stage in their receiving end. Consequently, detection performance enhancement of a CFAR variant represents the basic requirement of these systems, since the CFAR strategy plays a key role in automatic detection process. Most existing CFAR variants need to estimate the background level before constructing the detection threshold. In a multi-target state, the existence of spurious targets could cause inaccurate estimation of background level. The occurrence of this effect will result in severely degrading the performance of the CFAR algorithm. Lots of research in the CFAR design have been achieved. However, the gap in the previous works is that there is no CFAR technique that can operate in all or most environmental varieties. To overcome this challenge, the linear fusion (LF) architecture, which can operate with the most environmental and target situations, has been presented

AN ARTIFICIAL INTELLIGENCE APPROACH TO THE PROCESSING OF RADAR RETURN SIGNALS FOR TARGET DETECTION

Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification

Statistical assessment on Non-cooperative Target Recognition using the Neyman-Pearson statistical test

Electromagnetic simulations of a X-target were performed in order to obtain its Radar Cross Section (RCS) for several positions and frequencies. The software used is the CST MWS©. A 1 : 5 scale model of the proposed aircraft was created in CATIA© V5 R19 and imported directly into the CST MWS© environment. Simulations on the X-band were made with a variable mesh size due to a considerable wavelength variation. It is intended to evaluate the Neyman-Pearson (NP) simple hypothesis test performance by analyzing its Receiver Operating Characteristics (ROCs) for two different radar detection scenarios - a Radar Absorbent Material (RAM) coated model, and a Perfect Electric Conductor (PEC) model for recognition purposes. In parallel the radar range equation is used to estimate the maximum range detection for the simulated RAM coated cases to compare their shielding effectiveness (SE) and its consequent impact on recognition. The AN/APG-68(V)9’s airborne radar specifications were used to compute these ranges and to simulate an airborne hostile interception for a Non-Cooperative Target Recognition (NCTR) environment. Statistical results showed weak recognition performances using the Neyman-Pearson (NP) statistical test. Nevertheless, good RCS reductions for most of the simulated positions were obtained reflecting in a 50:9% maximum range detection gain for the PAniCo RAM coating, abiding with experimental results taken from the reviewed literature. The best SE was verified for the PAniCo and CFC-Fe RAMs.Simulações electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS) para várias posições e frequências. O software utilizado é o CST MWS©. O modelo proposto à escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho CST MWS©. Foram efectuadas simulações na banda X com uma malha de tamanho variável devido à considerável variação do comprimento de onda. Pretende-se avaliar estatisticamente o teste de decisão simples de Neyman-Pearson (NP), analisando as Características de Operação do Receptor (ROCs) para dois cenários de detecção distintos - um modelo revestido com material absorvente (RAM), e outro sendo um condutor perfeito (PEC) para fins de detecção. Em paralelo, a equação de alcance para radares foi usada para estimar o alcance máximo de detecção para ambos os casos de modo a comparar a eficiência de blindagem electromagnética (SE) entre os diferentes revestimentos. As especificações do radar AN/APG-68(V)9 do F-16 foram usadas para calcular os alcances para cada material, simulando uma intercepção hostil num ambiente de reconhecimento de alvos não-cooperativos (NCTR). Os resultados mostram performances de detecção fracas usando o teste de decisão simples de Neyman-Pearson como detector e uma boa redução de RCS para todas as posições na gama de frequências selecionada. Um ganho de alcance de detecção máximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com os resultados experimentais da bibliografia estudada. Já a melhor SE foi verificada para o RAM CFC-Fe e PAniCo

A Self-organizing Hybrid Sensor System With Distributed Data Fusion For Intruder Tracking And Surveillance

A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated

Wideband Spectrum Sensing for Dynamic Spectrum Sharing

The proliferation of wireless devices grows exponentially, demanding more and more data communication capacity over wireless links. Radio spectrum is a scarce resource, and traditional wireless networks deployed by Mobile Network Operators (MNO) are based on an exclusive spectrum band allocation. However, underutilization of some licensed bands in time and geographic domains has been reported, especially in rural areas or areas away from high population density zones. This coexistence of increasingly high data communication needs and spectrum underutilization is an incomprehensible scenario. A more rational and efficient use of the spectrum is the possibility of Licensed Users (known as Primary Users – PU) to lease the spectrum, when not in use, to Unlicensed Users (known as Secondary Users – SU), or allowing the SU to opportunistically use the spectrum after sensing and verifying that the PU is idle. In this latter case, the SU must stop transmitting when the PU becomes active. This thesis addresses the spectrum sensing task, which is essential to provide dynamic spectrum sharing between PUs and SUs. We show that the Spectral Correlation Function (SCF) and the Spectral Coherence Function (SCoF) can provide a robust signal detection algorithm by exploiting the cyclostationary characteristics of the data communication signal. We enhance the most used algorithm to compute de SCF - the FAM (FFT Accumulation Method) algorithm – to efficiently compute the SCF in a local/zoomed region of the support ( ; ) plane (frequency/cycle frequency plane). This will provide the quick identification of spectral bands in use by PUs or free, in a wideband sampling scenario. Further, the characterization of the probability density of the estimates of the SCF and SCoF when only noise is present, using the FAM algorithm, will allow the definition of an adaptive threshold to develop a blind (with respect to the noise statistics) Constant False Alarm Rate (CFAR) detector (using the SCoF) and also a CFAR and a Constant Detection Rate (CDR) detector when that characterization is used to obtain an estimate of the background noise variance (using the SCF).A proliferação de dispositivos sem fios cresce de forma exponencial, exigindo cada vez mais capacidade de comunicação de dados através de ligações sem fios. O espectro radioelétrico é um recurso escasso, e as redes sem fios tradicionais implantadas pelos Operadores de Redes Móveis baseiam-se numa atribuição exclusiva de bandas do espectro. No entanto, tem sido relatada a subutilização de algumas bandas licenciadas quer ao longo do tempo, quer na sua localização geográfica, especialmente em áreas rurais, e em áreas longe de zonas de elevada densidade populacional. A coexistência da necessidade cada vez maior de comunicação de dados, e a subutilização do espectro é um cenário incompreensível. Uma utilização mais racional e eficiente do espectro pressupõe a possibilidade dos Utilizadores Licenciados (conhecidos como Utilizadores Primários – Primary Users - PU) alugarem o espectro, quando este não está a ser utilizado, a Utilizadores Não Licenciados (conhecidos como Utilizadores Secundários – Secondary Users - SU), ou permitir ao SU utilizar oportunisticamente o espectro após a deteção e verificação de que o PU está inativo. Neste último caso, o SU deverá parar de transmitir quando o PU ficar ativo. Nesta tese é abordada a tarefa de deteção espectral, que é essencial para proporcionar a partilha dinâmica do espectro entre PUs e SUs. Mostra-se que a Função de Correlação Espectral (Spectral Correlation Function - SCF) e a Função de Coerência Espectral (Spectral Coherence Function - SCoF) permitem o desenvolvimento de um algoritmo robusto de deteção de sinal, explorando as características ciclo-estacionárias dos sinais de comunicação de dados. Propõe-se uma melhoria ao algoritmo mais utilizado para cálculo da SCF – o método FAM (FFT Accumulation Method) - para permitir o cálculo mais eficiente da SCF numa região local/ampliada do plano de suporte / (plano de frequência/frequência de ciclo). Esta melhoria permite a identificação rápida de bandas espectrais em uso por PUs ou livres, num cenário de amostragem de banda larga. Adicionalmente, é feita a caracterização da densidade de probabilidade das estimativas da SCF e SCoF quando apenas o ruído está presente, o que permite a definição de um limiar adaptativo, para desenvolver um detetor de Taxa de Falso Alarme Constante (Constant False Alarm Rate – CFAR) sem conhecimento do ruído de fundo (usando a SCoF) e também um detetor CFAR e Taxa de Deteção Constante (Constant Detection Rate – CDR), quando se utiliza aquela caracterização para obter uma estimativa da variância do ruído de fundo (usando a SCF)

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Continuous Monitoring of Environmental Disturbances by Cumulative Sums of Dense SAR Satellite Timeseries

Climate change together with growing socio-economic pressures are leading to a significant increase in alterations to natural ecosystems. The alteration of natural cycles and dynamics through the direct destruction or continuous degradation, are threatening the conservation of these natural spaces on a global scale. Satellite remote sensing is a suitable solution for large-scale monitoring and evaluation of natural landscapes under threat, as it provides a consistent source of information for both historical and updated environmental studies. However, most current remote sensing-based environmental monitoring tools still present certain limitations which hinder access to continuous and real-time information. The design and development of new methods and approaches to environmental remote sensing is required to mitigate the current environmental degradation trends. This thesis analyses the current challenges associated with environmental monitoring to focus on the development of new change detection methods applied to the study of environmental disturbances in highly dynamic natural ecosystems. By exploiting the frequent monitoring capabilities of Synthetic Aperture Radar (SAR) dense timeseries, this research introduces new approaches based on Cumulative Sum (CuSum) strategies for continuous and near-real-time investigation. These approaches have been applied to monitor permanent and cyclical disturbances in highly threatened forest and wetland ecosystems. The main scientific contribution of this thesis is the introduction of three novel SAR-based change detection approaches capable of exploiting dense satellite imagery time series for continuous and near-real-time monitoring. The outcome of this research provides environmental managers with a fully operational alternative tool capable of rapid and continuous monitoring of environmental dynamics

A Priori Knowledge-Based Post-Doppler STAP for Traffic Monitoring with Airborne Radar

Die Verkehrsüberwachung gewinnt aufgrund des weltweiten Anstiegs der Verkehrsteilnehmer immer mehr an Bedeutung. Sicherer und effizierter Straßenverkehr erfordert detaillierte Verkehrsinformationen. Häufig sind diese lediglich stationär, räumlich stark begrenzt und meist nur auf Hauptverkehrsstraßen verfügbar. In dieser Hinsicht ist ein Ausfall des Telekommunikationsnetzes, beispielsweise im Falle einer Katastrophe, und der damit einhergehende Informationsverlust als kritisch einzustufen. Flugzeuggetragene Radarsysteme mit synthetischer Apertur (eng. Synthetic Aperture Radar - SAR) können für dieses Szenario eine Lösung darstellen, da sie großflächig hochauflösende Bilder generieren können, unabhängig von Tageslicht und Witterungsbedingungen. Sie ermöglichen aufgrund dieser Charakteristik die Detektion von Bewegtzielen am Boden (eng. ground moving target indication – GMTI). Moderne GMTI-Algorithmen und -Systeme, die prinzipiell für die Verkehrsüberwachung verwendbar sind, wurden in der Literatur bereits diskutiert. Allerdings ist die Robustheit dieser Systeme oft mit hohen Kosten, hoher Hardwarekomplexität und hohem Rechenaufwand verbunden. Diese Dissertation stellt einen neuartigen GMTI-Prozessor vor, der auf dem Radar-Mehrkanalverfahren post-Doppler space-time adaptive processing (PD STAP) basiert. Durch die Überlagerung einer Straßenkarte mit einem digitalen Höhenmodell ist es mithilfe des PD STAP möglich, Falschdetektionen zu erkennen und auszuschließen sowie die detektierten Fahrzeuge ihren korrekten Straßenpositionen zu zuordnen. Die präzisen Schätzungen von Position, Geschwindigkeit und Bewegungsrichtung der Fahrzeuge können mit vergleichsweise geringerer Hardware-Komplexität zu niedrigeren Kosten durchgeführt werden. Ferner wird im Rahmen dieser Arbeit ein effizienter Datenkalibrierungsalgorithmus erläutert, der das Ungleichgewicht zwischen den Empfangskanälen sowie die Variation des Dopplerschwerpunkts über Entfernung und Azimut korrigiert und so das Messergebnis verbessert. Darüber hinaus werden neue und automatisierte Strategien zur Erhebung von Trainingsdaten vorgestellt, die für die Schätzung der Clutter-Kovarianzmatrix wegen ihres direkten Einflusses auf die Clutter-Unterdrückung und Zieldetektion essentiell für PD STAP sind. Der neuartige PD STAP Prozessor verfügt über drei verschiedene Betriebsarten, die für militärische und zivile Anwendungen geeignet sind, darunter ein schneller Verarbeitungsalgorithmus der das Potential für eine zukünftige Echtzeit-Verkehrsüberwachung hat. Alle Betriebsarten wurden erfolgreich mit Radar-Mehrkanaldaten des flugzeuggetragenen F-SAR-Radarsensors des DLR getestet