The TDNLMS (two dimensional normalized least mean squared error) adaptive filter can be used as a pre whitening filter in small object detection application in digital image. The performance of the digital implementation of the adaptive filter is influenced by the finite precision effect. In this paper

the finite-precision effect of TDNLMS adaptive predictor used for small objects detection is analyzed

and a method of determining computation word length of the digital adaptive filter by experiments is presented. The relation between the word length of the adaptive predictor and the features of the environment

including the step size parameter

input data word length

statistic characteristic of image processed and the support region of the predictor

is discussed. Simulation results are consistent with the analysis. Through the method presented in this paper

a finite precision TDNLMS adaptive predictor is designed

and compared with an infinite precision predictor witch is implemented by double precision floating point numbers. Simulation results shown that the MSE (mean squared error) and the mean SNR (signal to noise ratio) gain produced by the finite precision TDNLMS adaptive predictor is very close to the infinite precision TDNLMS adaptive predictor.