Recently, researchers have considered the application of multiple-input multiple-output (MIMO) techniques developed for wireless communication systems to the radar scenario. In MIMO systems, multiple antennas are employed at both transmitter and receiver to increase the data rate and reduce the effect of rapid changes in the radio channel with time. In the context of radar systems, mono-static or bi-static MIMO radars could be used to reduce the impact of scintillation effects, by illuminating the target from multiple transmit antennas but with the same total transmitter power budget. MIMO radars could also reduce the search time to find targets by transmitting multiple waveforms simultaneously, which allows more efficient searching of transmit angle. Further, MIMO processing increases the effective degrees of freedom in the radar system and may thus increase tolerance to echoes from the ground in radar systems and from the sea floor in sonar systems as well as deliberate man/made sources of interference. Since the emergence of MIMO radar concept international activity has focused both on the underlying theory, confirming the significant potential gains in detection and resolution performance that might be achieved, and on developing signal processing algorithms to facilitate these gains. What we propose here is to exploit the work we have already done in (i) methodologies for calculating detection performance in realistic MIMO radar or sonar scenarios; (ii) adaptive detection techniques for radar array-based signal processing that do not require secondary training data. We address the open research questions whose solution will facilitate industrial exploitation of the MIMO radar concept. In particular these are: (i) the design of correlation controlled constant amplitude MIMO waveforms; (ii) the development of adaptive receiver algorithms capable of working in environments of unknown clutter statistics and within the constraints of limited bandwidth communication channels between individual TR/RX pairs. A further novel aspect of the work will be the application of and assessment of MIMO concepts in sonar environments. What we propose is a rigorous generic approach to the understanding and application of MIMO detection. The results will be tested and validated in radar and sonar applications using detailed computer modelling techniques for both target and the medium. In the sonar case they will also be tested with measured data.

One of the remaining unsolved problems in control design is the development of simple and practical controllers for realtime control, based on a sound theoretical basis, for systems with severe nonlinearities and constraints. Although all dynamic systems are nonlinear, classical approaches to analysis and design are almost universally based on linear time invariant approximations to the dynamic characteristics. However, classical approaches are no longer adequate becauseof the increasing performance needs of modern industry, which require plants to be operated in regions where there are constraints and strong non-linear behaviour. Combined with the fact that most existing non-linear control techniques used by industry are empirically based and, as a result, difficult to tune and analyse, there is a real need for a scientifically more rigorous framework for practical non-linear multivariable control.This is a cooperative project in which the research work is divided between the Industrial Control Centre, University of Strathclyde and the Department of Aerospace Engineering, University of Glasgow. It builds upon the new nonlineargeneralized minimum variance (NGMV) control design ideas developed at University of Strathclyde. The project will attack the problem of synthesising nonlinear controller design using two complementary philosophies: the traditionalapproach of a purely theoretical foundation tested through realistic case studies and the alternative, practical view of tailoring advanced control research to address specific engineering problems. Motivation for this second approach comesfrom the fact that bespoke nonlinear controllers already exist in many engineering systems and this practical experience should be harnessed in the search for complete theory. The project involves five very significant and difficult scientificchallenges that should make the proposed method suitable for the most challenging real-time applications. These theoretical challenges include:1. Development of a Nonlinear Predictive Control facility which is much faster than existing solutions for machinery controls and faster processes.2. Include new Constraint Handling features in both the predictive and non-predictive versions.3. Introduce measures to guarantee some minimum Robustness Margins.4. Develop real time Labview based implementation with pre-specified low order Restricted Structure implementation.5. Introduce a Learning and adaptation feature for applications where plant models change slowly.The last feature may seem particularly ambitious but the NGMV family of control structures has a useful property that its structure is very suitable for learning or adaptive system. That is, any nonlinear plant subsystems affect the controllerstructure and solution in a very simple way.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

One of the remaining unsolved problems in control design is the development of simple and practical controllers for real-time control, based on a sound theoretical basis, for systems with severe nonlinearities and constraints. Although all dynamic systems are nonlinear, classical approaches to analysis and design are almost universally based on linear time-invariant approximations to the dynamic characteristics. However, classical approaches are no longer adequate because of the increasing performance needs of modern industry, which require plants to be operated in regions where there are constraints and strong non-linear behaviour. Combined with the fact that most existing non-linear control techniques used by industry are empirically based and, as a result, difficult to tune and analyse, there is a real need for a scientifically more rigorous framework for practical non-linear multivariable control. This is a cooperative project in which the research work is divided between the Industrial Control Centre, University of Strathclyde and the Department of Aerospace Engineering, University of Glasgow. It builds upon the new nonlinear generalized minimum variance (NGMV) control design ideas developed at University of Strathclyde. The project will attack the problem of synthesising nonlinear controller design using two complementary philosophies: the traditional approach of a purely theoretical foundation tested through realistic case studies and the alternative, practical view of tailoring advanced control research to address specific engineering problems. Motivation for this second approach comes from the fact that bespoke nonlinear controllers already exist in many engineering systems and this practical experience should be harnessed in the search for complete theory. The project involves five very significant and difficult scientific challenges that should make the proposed method suitable for the most challenging real-time applications. These theoretical challenges include:1. Development of a Nonlinear Predictive Control facility which is much faster than existing solutions for machinery controls and faster processes.2. Include new Constraint Handling features in both the predictive and non-predictive versions.3. Introduce measures to guarantee some minimum Robustness Margins.4. Develop real time Labview based implementation with pre-specified low order Restricted Structure implementation.5. Introduce a Learning and adaptation feature for applications where plant models change slowly. The last feature may seem particularly ambitious but the NGMV family of control structures has a useful property that its structure is very suitable for learning or adaptive system. That is, any nonlinear plant subsystems affect the controller structure and solution in a very simple way.