ASA 129th Meeting - Washington, DC - 1995 May 30 .. Jun 06

2aEA10. Normalized stochastic gradient method for nonstationary environment.

K. Lee

Jung G. Shin

Dept. of Elec. Eng. and Comput. Sci., Stevens Inst. of Technol., Castle Point on the Hudson, Hoboken, NJ 07030

The normalized least-mean-squares (NLMS) method has been adopted for a wide range of adaptive filter applications due to its simple structure and low complexity of computation. However, under certain circumstances, the convergence rate may not be satisfactory. The performance may even be poorer under certain nonstationary situations. A new normalized stochastic gradient (NSG) method is suggested to overcome the drawbacks of the NLMS method. The main features of the NSG method are (1) the time varying convergence parameter and (2) the onset detection. While in the NLMS method the convergence parameter is fixed, it is a function of time in the NSG method. It is selected to minimize the conditional variance of the filter coefficients at the next step given a set of the previous coefficients. The time-varying sequence of the convergence parameter makes the NSG method converge fast. The new NSG method has the capability of the onset detection. It detects the statistical changes of the desired input and by resetting the adaptive procedure it keeps tracking the changed signal. The simulations show that the new NSG method outperforms the NLMS method under both stationary and nonstationary environments. [Work supported by Daewoo Motot Company, Seoul, Korea.]