Detection in a Non-Gaussian Environment: Weak and Fading Narrowband Signals
Procedures for the detection of both weak and narrowband signals in non–Gaussian noise environments are discussed. For the weak signal case, nonlinear processors based on the Middleton Class A noise model and the mixture representation are developed. Significant processing gains are achievable with some rather simple procedures. A variant of the mixture model leads to a nonstationary detector, called the “switched detector.” Experiments with this detector on ambient arctic and shrimp noises show processing gains of 1.4 to 4.1 dB, respectively.
Signals with a moderate signal-to-noise ratio in non-Gaussian noise are modeled as fading narrowband signals. A new processor is developed which combines a robust estimator (for the fading signal) with a robust detection procedure. The robust estimator-detector prserves the structure of the quadrature (envelope) matched filter and is shown to be asymptotically optimal for a wide range of decision rules and several common target models encountered in sonar and radar. Various degrees of robustness are achieved, depending on the assumed availability for noise reference samples.
KeywordsFalse Alarm Impulsive Noise Constant False Alarm Rate Linear Detector Processing Gain
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