Abstract
Signal detection is the area of study that deals with the processing of informationbearing signals for the purpose of extracting information from them. Specific applications in which detection techniques are useful include radar, seismology, radio astronomy, sonar, speech and image processing, automatic control, medical signal processing, and optical communications. In general, detection applications involve making inferences from observations that are distorted or corrupted in some unknown manner. Moreover, the information that one wishes to extract from such observations is a fortiori unknown to the observer. Thus it is very useful to cast detection problems in a probabilistic framework in which unknown behavior is assumed to be random. In this light, detection theory fits properly within the province of statistical inference, and this is the interpretation to be used throughout this treatment.
Keywords
- Decision Rule
- False Alarm Rate
- Maximum Likelihood Estimator
- Digital Signal Processing
- Constant False Alarm Rate
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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© 1997 Springer Science+Business Media New York
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Nechval, N.A. (1997). Adaptive CFAR Tests for Detection of a Signal in Noise and Deflection Criterion. In: Wysocki, T., Razavi, H., Honary, B. (eds) Digital Signal Processing for Communication Systems. The Springer International Series in Engineering and Computer Science, vol 403. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6119-4_20
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DOI: https://doi.org/10.1007/978-1-4615-6119-4_20
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