Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) [electronic resource] / David Blacknell, Hugh Griffiths (eds.).
- Published:
- Stevenage : IET, 2013.
- Physical Description:
- 1 online resource (296 pages)
- Additional Creators:
- Blacknell, David and Griffiths, Hugh
Access Online
- Series:
- Contents:
- Machine generated contents note: 1.Introduction -- 1.1.Motivation -- 1.2.Definitions and acronyms -- 1.3.Scope of book -- 2.Automatic target recognition of ground targets -- 2.1.Introduction -- 2.2.SAR phenomenology -- 2.3.The ATR processing chain -- 2.3.1.Pre-screening -- 2.3.2.Template-matching -- 2.3.3.Feature-based classification -- 2.4.Use of contextual information in target detection -- 2.4.1.Motivation -- 2.4.2.Statistical formulation -- 2.4.3.Simulated results -- 2.5.Databases and modelling -- 2.5.1.Database construction -- 2.5.2.Case study: model-based ATR using MOCEM -- 2.6.Performance assessment -- 2.6.1.Receiver operating characteristic (ROC) curves -- 2.6.2.Confusion matrices -- 2.6.3.Operational assessment -- 2.7.Conclusions -- Acknowledgements -- References -- 3.Automatic recognition of air targets -- 3.1.Introduction -- 3.2.Fundamentals of the target recognition process -- 3.2.1.Introduction -- 3.2.2.Target features -- 3.2.3.Aircraft recognition techniques and waveform design -- 3.2.4.Target signature measurement -- 3.2.5.Radar range equation for radar target recognition -- 3.2.6.Main classification functions -- 3.2.7.Database -- 3.2.8.Classifier -- 3.2.9.Assembly of database -- 3.2.10.Classifier performance -- 3.2.11.Conclusions -- 3.3.Jet engine recognition -- 3.3.1.Introduction -- 3.3.2.Jet engine mechanics -- 3.3.3.Interaction of radar signal with engine blades -- 3.3.4.Jet engine modulation spectrum: engine rotational rate -- 3.3.5.Jet engine modulation spectrum: rotor stage spectrum -- 3.3.6.Jet engine modulation spectrum: mixing products from rotor stages -- 3.3.7.Determination of blade count -- 3.3.8.JEM waveform -- 3.3.9.System requirements -- 3.3.10.Conclusions -- 3.4.Helicopter recognition -- 3.4.1.Introduction -- 3.4.2.Main rotor blade flash -- 3.4.3.Detection of blade flash -- 3.4.4.Waveform and system requirements for blade flash detection -- 3.4.5.Blade flash detection -- 3.4.6.Helicopter classification using blade flash -- 3.4.7.Main rotor hub spectrum -- 3.4.8.Rear rotor blades -- 3.4.9.Radar range equation for helicopter recognition -- 3.4.10.Helicopter recognition summary -- 3.5.Range-Doppler imaging -- 3.5.1.Introduction -- 3.5.2.Helicopter signature -- 3.5.3.Jet airliner signature -- 3.5.4.Business jet signature -- 3.5.5.Propeller aircraft signature -- 3.5.6.Waveforms and system requirements for supporting RDI -- 3.5.7.Conclusions -- 3.6.Aircraft target recognition conclusions -- Acknowledgements -- References -- 4.Radar ATR of maritime targets -- 4.1.Introduction -- 4.2.The use of high range resolution (HRR) profiles for ATR -- 4.3.The derivation of ATR features from HRR profiles -- 4.3.1.Length estimate -- 4.3.2.Position specific matrices (PSMs) -- 4.3.2.1.Determination of length -- 4.3.2.2.Alignment -- 4.3.2.3.Quantisation -- 4.3.2.4.Creation of reference PSMs -- 4.3.2.5.Compare the quantised test profile to the reference PSMs -- 4.3.2.6.Determine a figure of merit -- 4.3.2.7.Classification -- 4.3.3.Other examples of ATR features -- 4.3.4.Choosing sets of uncorrelated features -- 4.4.Ship ATR under the influence of multipath -- 4.4.1.What is multipath? -- 4.4.2.The problem of defining testing and training vectors -- 4.5.Results -- 4.5.1.Length estimate -- 4.5.1.1.Results for La and Lb based on measurements of ship HRR profiles -- 4.5.1.2.Simulation of ship HRR profiles -- 4.5.2.PSM results -- 4.5.3.Results based on geometrical, statistical and structural features -- 4.5.3.1.Measurements -- 4.5.3.2.Classification based on simulated ships -- 4.6.The mitigation of multipath effects on ship ATR -- 4.6.1.Using several antennas -- 4.6.2.Using several frequencies -- 4.6.3.Combining two antennas and two frequencies -- 4.6.4.Classification improvement via multi-frequency and/or multi-antenna approach -- 4.7.Summary -- References -- 5.Effects of image quality on target recognition -- 5.1.Introduction -- 5.2.Improving ATR performance via PGA image quality enhancement -- 5.3.Improving ATR performance using high resolution, PWF-processed full-polarisation SAR data -- 5.4.Improving ATR performance via high-definition image processing -- 5.5.Reconstruction of interrupted SAR imagery -- 5.6.Summary and conclusions -- References -- 6.Comparing classifier effectiveness -- 6.1.Introduction -- 6.2.NCTI studies -- 6.3.Measurements -- 6.3.1.TIRA system -- 6.3.2.Targets -- 6.4.Idea of classification -- 6.4.1.Appropriate features -- 6.4.2.HRR and 2D ISAR -- 6.4.3.2D ISAR template correlation classifier -- 6.4.4.Selection of radar parameters -- 6.5.Classification scheme -- 6.5.1.Pre-processing unit -- 6.5.2.Feature extraction/reduction -- 6.5.3.Choosing a classifier -- 6.5.4.Test of classifiers -- 6.6.Feature extraction -- 6.6.1.Classification results using different feature sets -- 6.7.Conclusion -- References -- 7.Biologically inspired and multi-perspective target recognition -- 7.1.Introduction -- 7.2.Biologically inspired NCTR -- 7.2.1.Waveform design -- 7.2.2.Nectar-feeding bats and bat-pollinated plants -- 7.2.3.Classification of flowers -- 7.2.3.1.Data collection -- 7.2.3.2.Data pre-processing and results -- 7.2.4.Classification of insects -- 7.3.Acoustic micro-Doppler -- 7.3.1.Description of the acoustic radar -- 7.3.2.Experimentation -- 7.3.3.Classification performance results -- 7.4.Multi-aspect NCTR -- 7.4.1.Data preparation -- 7.4.2.Feature extraction -- 7.4.3.Multi-perspective classifiers -- 7.4.4.Multi-perspective classification performance -- 7.5.Summary -- References -- 8.Radar applications of compressive sensing -- 8.1.Introduction -- 8.2.Principles of compressive sensing -- 8.2.1.Sparse and compressible signals -- 8.2.2.Restricted isometric property and coherence -- 8.2.3.Signal reconstruction -- 8.2.3.1.Minimum l2 norm reconstruction -- 8.2.3.2.Minimum l0 norm reconstruction -- 8.2.3.3.Minimum l1 norm reconstruction -- 8.2.3.4.Example of l1 norm versus l2 norm reconstruction -- 8.3.Reconstruction algorithms -- 8.3.1.Convex optimisation -- 8.3.1.1.Basis pursuit -- 8.3.1.2.Basis pursuit de-noising -- 8.3.1.3.Least absolute shrinkage and selection operator -- 8.3.2.Greedy constructive algorithms -- 8.3.2.1.Matching pursuit -- 8.3.2.2.Orthogonal matching pursuit -- 8.3.2.3.Stage-wise orthogonal matching pursuit -- 8.3.3.Iterative thresholding algorithms -- 8.3.3.1.Iterative hard thresholding -- 8.3.3.2.Iterative shrinkage and thresholding -- 8.4.Jet engine modulation -- 8.4.1.Introduction -- 8.4.2.Jet engine model -- 8.4.3.Simulation results of JEM compressive sensing -- 8.5.Inverse synthetic aperture radar -- 8.5.1.Introduction -- 8.5.2.Simulation model -- 8.6.Conclusions -- Acknowledgements -- References -- 9.Advances in SAR change detection -- 9.1.Introduction -- 9.2.An analysis of the CCD algorithm -- 9.3.Results using the `universal image quality index' -- 9.4.Performance comparison of change detection algorithms -- 9.4.1.Visual comparisons of the MLE and CCD algorithms -- 9.4.2.Coherent change detection performance with shadow regions masked -- 9.5.Summary and conclusions -- References -- 10.Future challenges -- 10.1.Introduction -- 10.2.Future challenges -- 10.2.1.Target variability and practical databases -- 10.2.2.Complex clutter environments -- 10.2.3.Use of contextual information -- 10.2.4.Performance assessment and prediction -- 10.2.5.Deception and countermeasures -- References.
- Summary:
- The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to the ground, air and maritime domains; the impact of image quality on the overall target recognition performance; the performance of different approaches to the classifier algorithm; the improvement in performance to be gained when a target can be viewed from more than one perspective; the impact of compressive sensing; advances in change detection; and challenges and directions for future research. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) explores both the fundamentals of classification techniques applied to data from a variety of radar modes and selected advanced techniques at the forefront of research, and is essential reading for academic, industrial and military radar researchers, students and engineers worldwide.
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- ISBN:
- 9781849196864
- Note:
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
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