Adaptive waveform design for cognitive radar

Advances in technology, especially in sensing, robotics, wireless communications, hardware capabilities and the constant need to confront not only the existing but also new and advanced threats are pushing for the need of advanced radar techniques. In this context, Cognitive Radar (CR) is visualized as the next generation multifunctional, smart and adaptive radar that extends its capabilities and responsibilities far beyond the traditional radar. CR incorporates knowledge gained by the interaction with the environment into its operation therefore forming a closed-loop system aiming to enhance the system performance. A very important element of the CR operation is the ability to adaptively design the transmitted waveforms based on the radar objective and the changes in the environment. In this thesis, we present the different aspects involved in the Cognitive Radar concept with deeper focus on the adaptive waveform design of the system aiming to improve the tracking performance. A method of adaptive waveform design within the sensor management problem ensuring that the total transmitted power is reduced compared to the transmission of a fixed waveform is proposed and finally a promising direction towards the multi-sensor resource allocation and waveform design is presented

Space Remote Sensing and Detecting Systems of Oceangoing Ships

This paper introduces the implementation of space remote sensing and detecting systems of oceangoing ships as an alternative to the Radio – Automatic Identification System (R-AIS), Satellite – Automatic Identification System (S-AIS), Long Range Identification and Tracking (LRIT), and other current vessel tracking systems. In this paper will be not included  a new project known as a Global Ship Tracking (GST) as an autonomous and discrete satellite network designed by the Space Science Centre (SSC) for research and postgraduate studies in Satellite Communication, Navigation and Surveillance (CNS) at Durban University of Technology (DUT). The ship detection from satellite remote sensing imagery system is a crucial application for maritime safety and security, which includes among others ship tracking, detecting and traffic surveillance, oil spill detection service, and discharge control, sea pollution monitoring, sea ice monitoring service, and protection against illegal fisheries activities. The establishment of a modern sea surface and ships monitoring system needs enhancement of the Satellite Synthetic Aperture Radar (SSAR) that is here discussed as a modern observation infrastructure integrated with Ships Surveillance and Detecting via SSAR TerraSAR-X Spacecraft, Ships Surveillance and Detecting via SSAR Radarsat Spacecraft and Vessels Detecting System (VDS) via SSAR

Mathematical optimization techniques for cognitive radar networks

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise

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

New challenges in wireless and free space optical communications

AbstractThis manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced

A comparison of processing approaches for distributed radar sensing

Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich

Multi-angle, Frequency and Polarization Radar Measurement of Ice Sheets

Radio echo sounding of polar ice sheets provides important information on the ice bed topography and internal layers. These data have been used by scientists to create 3D maps of polar ice sheets for climate modeling as well as to reconstruct the climate history that dates back to hundreds of thousands of years. In this paper, we present the design, and development of three surface-based multi-channel radars in the VHF and UHF bands. We provide results from radar data multi-frequency and polarization radar data collected over the Greenland ice sheet. All the three radars shared the same digital waveform generator and digitizer, and were installed in and operated on a tracked vehicle. The radars are operated with 3 different antenna arrays designed for operation over 170-230 MHz, 180-340 MHz and 600-900 MHz. The results we sounded more than 2.7 km thick ice with radars operating at frequencies as high as 850 MHz with more than 40 dB signal-to-noise ratio