Browsing by Author "Hughes, Evan J."
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Item Open Access Adaptive spatio-temporal CFAR and multiple-hypothesis tracking system(2009-03-12T16:36:30Z) Hughes, Evan J.; Lewis, M.This paper describes a self organising spatio-temporal radar CFAR system that uses multiple intelligent software agents to detect and adapt the processing to features in the environment. By combining both temporal and spatial data gathering sufficient samples can be collected to allow both the first and second order moments of the clutter distribution to be approximated for each cell. By gathering higher order statistics to a useful accuracy, more stable thresholds may be produced.Item Open Access Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches(Cranfield University, 2011-10) Butans, Jevgenijs; Tiwari, Ashutosh; Hughes, Evan J.The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem.Item Open Access Advanced detection and tracking in medium PRF radar(2009-03-12T15:51:08Z) Hughes, Evan J.; Lewis, M.This paper describes an improved method of target tracking particularly applicable to littoral environments where a wide range of clutter characteristics are present. A light weight multiple hypothesis tracker based on multiple intelligent software agents is presented.Item Open Access Analysis of performance of automatic target recognition systems(2012-08-22) Marino, G.; Hughes, Evan J.An Automatic Target Recognition (ATR) system is a sensor which is usually able to recognize targets or objects based on gathered data. The application of automatic target recognition technology is a critical element of robotic warfare. ATR systems are used in unmanned aerial vehicles and cruise missiles. There are many systems which are able to collect data (e.g. radar sensor, electro-optic sensor, infra-red devices) which are commonly used to collect information and detect, recognise and classify potential targets. Despite significant effort during the last decades, some problems in ATR systems have not been solved yet. This Ph.D. tried to understand the variation of the information content into an ATR system and how to measure as well as how to preserve information when it passes through the processing chain because they have not been investigated properly yet. Moreover the investigation focused also on the definition of class-separability in ATR system and on the definition of the degree of separability. As a consequence, experiments have been performed for understanding how to assess the degree of class-separability and how the choice of the parameters of an ATR system can affect the final classifier performance (i.e. selecting the most reliable as well as the most information ii iii preserving ones). As results of the investigations of this thesis, some important results have been obtained: Definition of the class-separability and of the degree of classseparability (i.e. the requirements that a metric for class-separability has to satisfy); definition of a new metric for assessing the degree of classseparability; definition of the most important parameters which affect the classifier performance or reduce/increase the degree of class-separability (i.e. Signal to Clutter Ratio, Clutter models, effects of despeckling processing). Particularly the definition of metrics for assessing the presence of artefacts introduced by denoising algorithms, the ability of denoising algorithms in preserving geometrical features of potential targets, the suitability of current mathematical models at each stage of processing chain (especially for clutter models in radar systems) and the measurement of variation of information content through the processing chain are some of them most important issues which have been investigated.Item Open Access Antenna performance optimisation using evolutionary algorithms(2010-11-08) Ansell, D. W.; Hughes, Evan J.This thesis investigates the novel idea of using evolutionary algorithms to optimise control and design aspects of active array antenna systems. Active arrays differ from most mechanically scanned antennas in that they offer the ability to control the shape of their radiation pattern. As active arrays consist of a multiplicity of transmit and receive modules (TRMs), the task of optimally controlling them in order to generate a desired radiation pattern becomes difficult. The control problem is especially true of conformal (non-planar) array antennas that require additional phase control to achieve good radiation pattern performance. This thesis describes a number of significant advances in the optimisation of array antenna performance. Firstly a genetic algorithm (GA) is shown to be effective at optimising both planar and conformal antenna performance. A number of examples are used to illustrate and promote the basic optimisation concept. Secondly, in this thesis the techniques are advanced to apply multiobjective evolutionary optimisation algorithms to array performance optimisation. It is shown that Evolutionary Algorithms allow users to simultaneously optimise many aspects of array performance without the need to fine-tune a large number of weights. The multiple-objective analysis methods shown demonstrate the advantages to be gained by holding knowledge of the Pareto optimal solution set. Thirdly, this thesis examines the problems of optimising the design of large (many element) array antennas. Larger arrays are often divided into smaller sub-arrays for manufacturing reasons and to promote formation of difference beam patterns for monopulse operation. In the past, the partitioning has largely been left to trial-and-error or simple randomisation techniques. This thesis describes a new and novel approach for optimally subdividing both planar and conformal array antennas as well as improving gain patterns in a single optimisation process. This approach contains a new method of partitioning array antennas, inspired from a biological process and is also presented and optimised using evolutionary algorithms. Additionally, the technique can be applied to any size or shape of array antenna, with the processing load dependent on the number of subarrays, rather than the number of elements. Finally, the success of these new techniques is demonstrated by presenting a range of performance optimised examples of planar and conformal array antenna installations including examples of optimally evolved subarray partitions.Item Open Access The dependence of radar target detectability on array weighting function(2009-07-14T14:40:00Z) Alabaster, Clive M.; Hughes, Evan J.This paper describes simulation work to assess the detectability of targets by an airborne fire control radar (FCR) operating in a medium pulse repetition frequency (PRF) mode in the presence of strong ground clutter as a function of transmitting and receiving array weighting functions.Item Open Access The Design of medium PRF radar schedules for optimum detectability in diverse clutter scenes(2009-04-22T14:28:48Z) Alabaster, Clive M.; Hughes, Evan J.Airborne fire control radars using medium pulse repetition frequency (PRF) waveforms are required to maintain good target detectability in various clutter scenes. This paper describes work to optimise PRF values of 3 of 8 medium PRF schedules in varying clutter scenes in order to achieve optimal target detectability. To this end, the detectability map is introduced as a means of illustrating and quantifying target detectability over the full Range/Doppler space of interest. The paper concludes that optimised PRF sets achieve similar detectability performances irrespective of the clutter scene.Item Open Access Evaluation of a Second order adaptive CFAR in the littoral environment(2009-03-12T15:29:44Z) Hughes, Evan J.; Lewis, M.This paper examines seven common CFAR techniques and describes a spatially adaptive CFAR that captures the best performance in respect of PD and Pfa in the presence of long tail and target corrupted clutter.Item Open Access Examination of the effect of array weighting function on radar target detectability(2010-07-01) Alabaster, Clive M.; Hughes, Evan J.This paper describes a methodology to assess the detectability of targets by anairborne fire control radar operating in a medium PRF mode in the presence of strongground clutter as a function of the transmitting and receiving antenna array weightingfunctions and proportion of failed array elements. It describes the radar, antenna andclutter modelling processes and the method by which target detectability is quantified.The detectability of targets in clutter is described using a detectability map, whichprovides a useful means of comparing target detectability as clutter conditions change.It concludes that the best target detectability is to be achieved using those weightingfunctions on transmit and receive which result in the lowest average sidelobe levelsbut that the margins between the more highly tapered weighting functions were small.Furthermore, it concludes that target detectability degrades as the proportion of failedelements increases. A failure of 5% of the elements gave modest, though meaningful,degradations in target detectability and would therefore form a suitable upper limit.Item Open Access Improved detection and ambiguity resolution of low observable targets in MPRF radar(2009-03-12T15:44:19Z) Hughes, Evan J.; Lewis, M.This paper presents the results of an investigation into the use of a Multiple Intelligent Software Agent technique as a means of eliminating ghost targets in a MPRF radar system especially when many real targets are present.Item Open Access Improved detection and ambiguity resolution of multiple targets in MPRF Radar(2009-07-14T15:15:26Z) Hughes, Evan J.; Lewis, M.This paper presents the results of an investigation into the mechanisms that generate ghost targets when many real targets are present on the same azimuth in a MPRF radar system. A new PRI selection strategy is proposed as a method to eliminate these ghost targets. The use of evolutionary algorithms as a means of optimising the PRI selection is discussed.Item Open Access Improved target detection and tracking in littoral environments using a self-organising spatio-temporal CFAR(2009-07-14T14:58:07Z) Hughes, Evan J.; Lewis, M.This paper describes a self organising spatio-temporal radar CFAR system that uses multiple intelligent software agents to detect and adapt the processing to features in the environment. By combining both temporal and spatial data gathering sufficient samples can be collected to allow both the first and second order moments of the clutter distribution to be approximated for each cell. By gathering higher order statistics to a useful accuracy, more stable thresholds may be producedItem Open Access An Intelligent agent based Track-Before-Detect system applied to a range and velocity ambiguous radar(2009-03-12T15:25:20Z) Hughes, Evan J.; Lewis, M.The problems associated with detecting low observable targets using Track-before-Detect systems based on Hough transform or Dynamic Programming techniques are reviewed. An alternative self-adaptive spatio-temporal CFAR system and a multiple hypothesis tracker based on Multiple Intelligent Software Agents and its adaptation to range and velocity ambiguous radar is described.Item Open Access Machine learning based decision support for a class of many-objective optimisation problems(Cranfield University, 2013-11) Duro, Joao A.; Saxena, Dhish; Tiwari, Ashutosh; Hughes, Evan J.There is a growing recognition for the multiple criteria decision making (MCDM) based multi-objective evolutionary algorithms (MOEAs) for tackling many-objective optimisation problems (MaOPs). In that, the aim is to utilise the decision makers’ (DMs’) preferences to guide the search towards a few solutions, as against the whole Pareto-optimal front (POF). This thesis is based on the premise that the practical utility of the MCDM based MOEAs may be impaired due to the lack of–objectivity (a rational basis); repeatability (identical preferences for identical options); consistency (alike preferences across multiple interaction stages); and coherence (alike preferences by multiple DMs) in the DMs’ preferences. To counter these limitations, this thesis aimed at developing offline and online decision support by capturing the preference-structure of the objective functions inherent in the problem model itself. This aim has been realised through the following objectives: • Identification of a criterion for the decision support: in that, preservation of the correlation– structure of the MOEA solutions is found to be a robust criterion in the case of MaOPs. • Development of a machine learning based offline objective reduction framework: it com¬prises of linear and nonlinear objective reduction algorithms, which facilitate the decision support through revelation of: (i) the redundant objectives (if any), (ii) preference-ranking of the essential objectives, (iii) the smallest objective sets corresponding to pre-specified errors, and (iv) the objective sets of pre-specified sizes that correspond to minimum error. • Development of an online objective reduction framework: it addresses a major pitfall associated with the offline framework, that–an essential objective if erroneously eliminated as redundant, has no scope of being reconsidered in the subsequent analysis. This pitfall is countered through a probabilistic retention of all the objectives, and this serves as a self-correcting mechanism that enhances the overall accuracy. • Timing the decision support: it is acknowledged that the revelations by the decision support may vary depending on when the offline or online framework is applied during an MOEA run. This uncertainty on the timing of the decision support is countered through the proposition of an entropy based dissimilarity measure. The efficacy of the proposed frameworks is investigated against a broad range of test problems (scaled up to 50 objectives) and some real-world MaOPs; and, the accuracy of the corresponding decision support is compared against that of an alternative approach based on preserving the dominance relations. The results illustrate that the proposed frameworks and the corresponding decision support bear significant utility for those MaOPs, where not all the objectives are essential, or equally important for describing the true POF. The considered real-world problems also bear evidence of the fact that MaOPs with redundant and disparately important objectives may commonly exist in practice.Item Open Access Medium PRF Schedules for Airborne Fire Control Radar(2009-04-22T14:23:36Z) Alabaster, Clive M.; Hughes, Evan J.; Parry, S. M.; Wiley, D. A.; Matthew, J. H.; Davies, P. G.Many modern radar systems use medium pulse repetition frequency (PRF) waveforms to measure target range and velocity in the presence of clutter. Medium PRF waveforms offer excellent clutter rejection characteristics which render them an attractive proposition for airborne fire control radar plus a variety of other military radar applications. This paper describes work to optimise the selection of precise values of PRF for a variety of medium PRF schedules and to rate the quality of the solutions found.Item Open Access On-line evolutionary algorithm guidance for multiple missiles against multiple targets(Elsevier, 2004-06-18) Hughes, Evan J.; White, Brian A.This paper details the application of a Cooperative Coevolution On-Line Evolutionary Algorithm (CCOLEA) to the guidance of a swarm of multiple missiles, against multiple targets. The CCOLEA trades the spatial distribution of missiles at impact, against the cost of re-aiming the missiles' seekers onto their final targets. A parallel approach is used where each missile optimises its own performance, based on limited information from the other missiles. The decision making processis thus distributed between the missiles giving distributed coordination.Item Open Access Radar cross section modelling using genetic algorithms(2009-03-12T16:12:26Z) Hughes, Evan J.; White, Prof. B. A.; Leyland, M.In the design of new, more sophisticated missile systems, simulations need to be realistic and fast. Realistic target models are just as important as realistic models of the missile, but have often been overlooked in the past. Existing methods for creating realistic target models require considerable computational resources. This thesis addresses the problem of using limited resources to create realistic target models for simulating engagements with radar guided homing missiles. A multiple genetic algorithm approach is presented for converting inverse synthetic aperture radar images of targets into scatterer models. The models produced are high fidelity and fast to process. Results are given that demonstrate the generation of a model from real data using a desktop computer. Realistic models are used to investigate the effects of target fidelity on the missile performance. The results of the investigation allow the model complexity to be traded against the fidelity of the representation to optimise simulation speed. Finally, a realistic target model is used in a feasibility study to investigate the potential use of glint for target manoeuvre detection. Target glint is considered as noise in conventional missile systems and filtered to reduce its effects on the tracking performance- The use of glint for target manoeuvre detection would provide a cheap and novel alternative to the optical techniques currently being developed. The feasibility study has shown that target manoeuvre detection using glint may be as fast as optical techniques and very reliable.