Multibeam radar system based on waveform diversity for RF seeker applications

Existing radiofrequency (RF) seekers use mechanically steerable antennas. In order to improve the robustness and performance of the missile seeker, current research is investigating the replacement of mechanical 2D antennas with active electronically controlled 3D antenna arrays capable of steering much faster and more accurately than existing solutions. 3D antenna arrays provide increased radar coverage, as a result of the conformal shape and flexible beam steering in all directions. Therefore, additional degrees of freedom can be exploited to develop a multifunctional seeker, a very sophisticated sensor that can perform multiple simultaneous tasks and meet spectral allocation requirements. This thesis presents a novel radar configuration, named multibeam radar (MBR), to generate multiple beams in transmission by means of waveform diversity. MBR systems based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver with digital signal processing. The advantage is that MBR transmit differently designed waveforms in arbitrary directions so that waveforms can be selected to provide multiple radar functions and better manage the available resources. An analytical model of an MBR is derived to analyse the relationship between individual channels and their performance in terms of isolation and phase steering effects. Combinations of linear frequency modulated (LFM) waveforms are investigated and the analytical expressions of the isolation between adjacent channels are presented for rectangular and Gaussian amplitude modulated LFM signals with different bandwidths, slopes and frequency offsets. The theoretical results have been tested experimentally to corroborate the isolation properties of the proposed waveforms. In addition, the practical feasibility of the MBR concept has been proved with a radar test bed with two orthogonal channels simultaneously detecting a moving target

OFDM Waveform Optimisation for Joint Communications and Sensing

Radar systems are radios to sense objects in their surrounding environment. These operate at a defined set of frequency ranges. Communication systems are used to transfer information between two points. In the present day, proliferation of mobile devices and the advancement of technology have led to communication systems being ubiquitous. This has made these systems to operate at the frequency bands already used by the radar systems. Thus, the communication signal interferes a radar receiver and vice versa, degrading performance of both systems. Different methods have been proposed to combat this phenomenon. One of the novel topics in this is the RF convergence, where a given bandwidth is used jointly by both systems. A differentiation criterion must be adopted between the two systems so that a receiver is able to separately extract radar and communication signals. The hardware convergence due to the emergence of software-defined radios also motivated a single system be used for both radar and communication. A joint waveform is adopted for both radar and communication systems, as the transmit signal. As orthogonal frequency-division multiplexing (OFDM) waveform is the most prominent in mobile communications, it is selected as the joint waveform. Considering practical cellular communication systems adopting OFDM, there often exist unused subcarriers within OFDM symbols. These can be filled up with arbitrary data to improve the performance of the radar system. This is the approach used, where the filling up is performed through an optimisation algorithm. The filled subcarriers are termed as radar subcarriers while the rest as communication subcarriers, throughout the thesis. The optimisation problem minimises the Cramer--Rao lower bounds of the delay and Doppler estimates made by the radar system subject to a set of constraints. It also outputs the indices of the radar and communication subcarriers within an OFDM symbol, which minimise the lower bounds. The first constraint allocates power between radar and communication subcarriers depending on their subcarrier ratio in an OFDM symbol. The second constraint ensures the peak-to-average power ratio (PAPR) of the joint waveform has an acceptable level of PAPR. The results show that the optimised waveform provides significant improvement in the Cramer--Rao lower bounds compared with the unoptimised waveform. In compensation for this, the power allocated to the communication subcarriers needs to be reduced. Thus, improving the performances of the radar and communication systems are a trade-off. It is also observed that for the minimum lower bounds, radar subcarriers need to be placed at the two edges of an OFDM symbol. Optimisation is also seen to improve the estimation performance of a maximum likelihood estimator, concluding that optimising the subcarriers to minimise a theoretical bound enables to achieve improvement for practical systems

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems

Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Analysis and Life Cycle Assessment of Printed Antennas for Sustainable Wireless Systems

Siirretty Doriast

Aeronautical engineering: A continuing bibliography with indexes (supplement 255)

This bibliography lists 529 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics