Title :
Proportional-type NLMS algorithm with gain allocation providing maximum one-step conditional PDF for true weights
Author :
Wagner, Kevin T. ; Doroslovacki, Milos I.
Author_Institution :
Radar Div., Naval Res. Lab., Washington, DC
Abstract :
In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the maximum conditional probability density one-step algorithm is superior to the normalized least mean square algorithm and the proportionate normalized least mean square algorithm. Additionally, we argue that the algorithm we present operates for any impulse response.
Keywords :
adaptive filters; least squares approximations; optimisation; probability; transient response; adaptive filter; adaptive gain allocation; constrained optimization; impulse response; maximum one-step conditional PDF; normalized least mean square algorithm; probability density function; proportional-type NLMS algorithm; true weight; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Constraint optimization; Laboratories; Least mean square algorithms; Mean square error methods; Noise measurement; Probability density function; Radar;
Conference_Titel :
Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-2733-8
Electronic_ISBN :
978-1-4244-2734-5
DOI :
10.1109/CISS.2009.5054691
