Title :
Maximum likelihood trend estimation in exponential noise
Author_Institution :
Ericsson Radio Syst. AB, Stockholm, Sweden
fDate :
9/1/2001 12:00:00 AM
Abstract :
This paper considers the problem of estimating a linear trend in noise, where the noise is modeled as independent and identically distributed (i.i.d.) random process with exponential distribution. The corresponding maximum likelihood parameter estimator of the trend and noise parameters is derived, and its performance is analyzed. It turns out that the resulting maximum likelihood estimator has to solve a linear programming problem with number of constraints equal to the number of received data. A recursive form of the maximum likelihood estimator, which makes it suitable for implementation in real-time systems, is then proposed. The memory requirements of the recursive algorithm are data dependent and are investigated by simulations using both computer-generated and recorded data sets
Keywords :
digital simulation; exponential distribution; least squares approximations; linear programming; maximum likelihood estimation; packet switching; random noise; recursive estimation; signal sampling; telecommunication networks; MLE; computer-generated data sets; exponential distribution; exponential noise; i.i.d. random process; independent identically distributed random process; least squares estimator; linear programming problem; linear trend estimation; maximum likelihood parameter estimator; maximum likelihood trend estimation; memory requirements; noise parameters; packet switched network; performance analysis; received data; recorded data sets; recursive algorithm; recursive maximum likelihood estimator; signal sampling; simulations; trend parameters; Computational modeling; Computer simulation; Exponential distribution; Linear programming; Maximum likelihood estimation; Parameter estimation; Performance analysis; Random processes; Real time systems; Recursive estimation;
Journal_Title :
Signal Processing, IEEE Transactions on
