A new processor for detecting a radar target in correlated, non-Gaussian noise is obtained. When this processor and a matched filter are excited with this noise, performance is improved over that of the matched filter alone. The processor is obtained by developing an approximate, bivariate, probability density for the noise and constructing a Neyman-Pearson test and then using an approximation ...

Non-coherent receivers have become promising candidates for low complexity and low power ultra wide band (UWB) systems.Wediscuss the performance of energy detection and differential detection in additive white Gaussian noise, Rayleigh fading, and multipath environments. It is shown that non-coherent receivers can easily collect the multipath energy by means of equal gain combing. The loss (comp...

The problem of detecting a signal embedded in additive Gaussian noise is considered based on statistical decision theory and Neyman-Pearson criterion. The study is carried out for novel test statistics (TSs) estimated that use third-order moment functions. Performances of signal detection for traditional energy second-order TS calculated as the peak of the matched filter output, the second-orde...

Objective(s): In order to evaluate the effect of post-reconstruction Gaussian filtering on image quality and myocardial blood flow (MBF) measurement by dynamic N-13 ammonia positron emission tomography (PET), we compared various reconstruction and filtering methods with image characteristics. Methods: Dynamic PET images of three patients with coronary artery disease (male-female ratio of 2:1; a...

An optimal test does not exist for the problem of detecting a known target with unknown location in additive Gaussian noise. A common solution uses a generalised likelihood ratio testing (GLRT) formalism, where a maximum likelihood estimate of the unknown location parameter is used in a likelihood ratio test. The performance of this test is commonly assessed by comparing it to the ideal matched...