Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
The adaptive MSINR algorithm to improve error rate for channel equalization
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Digital Signal Processing
Performance analysis of the weighted decision feedback equalizer
Signal Processing
LSE and MSE optimum partition-based FIR-IIR deconvolution filters with best delay
IEEE Transactions on Signal Processing - Part I
An improved inverse filtering method for parametric spectralestimation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Robust deconvolution of deterministic and random signals
IEEE Transactions on Information Theory
Optimal Wiener interpolation filters for multiresolution coding of images
IEEE Transactions on Circuits and Systems for Video Technology
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A direct optimization method for low group delay FIR filter design
Signal Processing
| Hi-index | 0.08 |
The inverse system approximation using the finite impulse responses (FIR) and the corresponding model-order determination are essential to a broad area of science and technology utilizing signal processing. To the best of our knowledge, there exists no explicit formulation of the exact L"2 approximation error for the truncated inverse filters. The approach to determine the minimum inverse model-order subject to the maximum allowable L"2 approximation error is also in demand. In this paper, we present two L"2 approximation error measures and the two corresponding optimal finite-support approximates. Also, we derive the explicit L"2 approximation error functions with respect to roots, multiplicities and model orders for these two kinds of approximates. Then, we propose a new algorithm to determine the minimum total model order of the appropriate truncated inverse filter to achieve a specified L"2 approximation error. Our newly derived L"2 approximation error evaluation method can be employed for signal processing, telecommunication, control systems involving the inverse filtering in the future. Besides, our novel model-order determination algorithm can be utilized for efficient dynamic memory allocation in a wide variety of applications since such a minimum total model order is proportional to the memory usage for any inverse filter implementation.