Adaptive Signal Detection and Parameter Estimation in Unknown Colored Gaussian Noise

Abstract

This paper considers the general signal detection and parameter estimation problem in the presence of colored Gaussian noise disturbance. By modeling the disturbance with an autoregressive process, we present three signal detectors with different unknown parameters under the general framework of binary hypothesis testing. The closed form of parameter estimates and the asymptotic distributions of these three tests are also given. Given two examples of frequency modulated signal detection problem and time series moving object detection problem, the simulation results demonstrate the effectiveness of three presented detectors.

The algorithm for the detection of information signals

“…In [4], a generalized power detector is considered, in which signal samples are summed to the power greater than 2. In [5], an operation of varied detection algorithms in "colored" noise is considered. In [2], the algorithms for signal detection with the specified false alarm probability and the need to estimate the noise level are introduced, along with the signal processing algorithm based on order statistics.…”