Noncoherent Adaptive Detection Technique

摘要

An adaptive detection technique suitable for both stationary and nonstationarynoise environments based upon a generalized likelihood ratio test (GLRT) formulation is presented. The detector, which is statistically equivalent to a special form of the Wilk's lambda test, noncoherently combines the information contained In a pulse train of arbitrary length for decision making purposes. The probability density function of the test under the noise only hypothesis is shown to be central X2. Under the signal plus noise hypothesis, an exact statistical characterization Of the test cannot be obtained and, therefore, a Chernoff bound is derived. Results in terms of the probability of detection versus signal-to-noise ratio (SNR) obtained from Monte Carlo simulations, the Chernoff bound, and the optimal matched filter case are examined. The performance of the noncoherent detector Is shown to be a function of the covariance matrix estimate and the number of data samples. noncoherent adaptive detection, GLRT, signal-to-noise.