TAILIEUCHUNG - Digital Signal Processing Handbook P13

Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing. | Hero A. Signal Detection and Classification Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton CRC Press LLC 1999 1999 by CRC Press LLC 13 Signal Detection and Classification Alfred Hero University of Michigan Introduction Signal Detection The ROC Curve Detector Design Strategies Likelihood Ratio Test Signal Classification The Linear Multivariate Gaussian Model Temporal Signals in Gaussian Noise Signal Detection Known Gains Signal Detection Unknown Gains Signal Detection Random Gains Signal Detection Single Signal Spatio-Temporal Signals Detection Known Gains and Known Spatial Covariance Detection Unknown Gains and Unknown Spatial Covariance Signal Classification Classifying Individual Signals Classifying Presence of Multiple Signals References Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of noise alone or signal masked by noise. Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing 1 2 . However signal processing engineers must also contend with the exceedingly large size of signal processing datasets the absence of reliable and tractible signal models the associated requirement of fast algorithms and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were .

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