Finally, simpler parameter estimation algorithms that only require linear searches are proposed as alternatives to the computationally complex optimal schemes. Cramer-Rao lower bounds are developed for chirp parameter estimation if the multiplicative and additive noise is Gaussian. For the most part the material developed here can be.

The probability density functions for quantization noise, continuous wave interference, atmospheric noise, and impulse noise are presented and discussed in detail. A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate. This is the detection of signals in addi tive noise which is not required to have Gaussian probability den sity functions in its statistical description. Non-Gaussian noise is the subject of this study.

A least-squares method for parameter estimation in colored noise is proposed and asymptotic bounds are derived on the estimation error. This book contains a unified treatment of a class of problems of signal detection theory. Finally, simpler parameter estimation algorithms that only require linear searches are proposed as alternatives to the computationally complex optimal schemes.ĪB - Detection of chirps in (non-Gaussian) additive and multiplicative noise is explored via a novel cyclostationary approach. 3-level quantizer is used as a nondynamic stochastic resonance method to enhance SNR of weak signal. Non-Gaussian noise is modeled by Gaussian mixture distribution. Cramer-Rao lower bounds are developed for chirp parameter estimation if the multiplicative and additive noise is Gaussian. The detection of weak transient signal buried in non-Gaussian noise is investigated. A least-squares method for parameter estimation in colored noise is proposed and asymptotic bounds are derived on the estimation error. N2 - Detection of chirps in (non-Gaussian) additive and multiplicative noise is explored via a novel cyclostationary approach. Let f and F be the Student t density and distribution functions with > 0 degrees of freedom, respectively. For example, skew versions of the Student t distributions. A possible alternative to the Gaussian distribution consists of using skew distributions. T1 - Detection and estimation of chirp signals in non-Gaussian noise Yes, it is possible and it is used quite a bit nowadays.

Non gaussian noise