| Project/Area Number |
08650419
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
情報通信工学
|
|---|
| Research Institution | Tohoku University |
|---|
Principal Investigator |
KAWAMATA Masayuki Tohoku University, Graduate School of Engineering Professor, 大学院・工学研究科, 教授 (70153004)
|
|---|
| Project Period (FY) |
1996 – 1998
|
| Project Status |
Completed (Fiscal Year 1998)
|
| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1998: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1997: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1996: ¥1,000,000 (Direct Cost: ¥1,000,000)
|
|---|
| Keywords | Multidimensional digital filters / 3-D signal processing / video signal processing / adaptive digital filters / 適応フィルタ |
|---|
| Research Abstract |
1. This research first proposes a bias removal algorithm for equation error-based 2-D adaptive cascade IIR filters with separable denominator function. As well known, equation error-based adaptive IIR filtering algorithms have the advantages of fast convergence and unimodal mean-square-error surface. However, they converge to biased parameter estimates in the presence of measurement noise. To handle the bias problem, the proposed algorithm uses a scaled value of the output error of each of the cascade sections as an estimate for the additive noise embedded in the signal part of the update procedure of the corresponding section. Thus, while maintaining the good convergence properties of equation error IIR filters, the effect of the measurement noise is suppressed. Input-output stability analysis is carried out, and the constraints required to maintain stability are derived.
2. This research secondly considered the steady state mean square error (MSE) analysis for 2-D LMS adaptive filtering algorithm in which the filter's weights are updated along both vertical and horizontal directions as a doubly-indexed dynamical system. The MSE analysis is conducted using the well-known independence assumption. First we show that computation of the weight-error covariance matrix for doubly-indexed 2-D LMS algorithm requires an approximation for the weight-error correlation coefficients at large spatial lags. Then we propose a method to solve this problem. Further discussion is carried out for the special case when the input signal is white Gaussian. It is shown that the convergence in the MSE sense occurs for step size range that is significantly smaller than the one necessary for the convergence of the mean.
|
