Publication | Closed Access
Performance Prediction for Energy Detection of Unknown Signals
49
Citations
7
References
2008
Year
EngineeringMachine LearningMeasurementDetection TechniqueEnergy MonitoringFalse DetectionStatistical Signal ProcessingSliding WindowData ScienceEnergy DetectorPattern RecognitionNoiseSignal DetectionPerformance PredictionSensor Signal ProcessingComputer EngineeringComputer ScienceSignal ProcessingSensors
This paper analyzes the energy detector that is commonly used to detect the presence of unknown information-bearing signals. The algorithm simply compares the energy (or power) in a sliding window to a threshold. The analysis allows for arbitrary spectra of information-bearing signal and noise processes. It yields two equations that relate five variables/parameters: the probability of false detection, the probability of missing a detection, window length, detection threshold, and signal-to-noise ratio (SNR). The probability density function of the detection variable is shown to be approximately Gamma distributed. All of the theoretical expressions and approximations are substantiated with simulation results.
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