Complexity of pruning strategies for the frequency domain LMS algorithm

Raghavan, Vasanthan; Prabhu, K.M.M.; Sommen, P.C.W.

doi:10.1016/j.sigpro.2005.11.015

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common practical examples relate to, e.g., the least mean squared (LMS) optimal DFT-based pruned signal filtering [2], and the complexity-reduced computational implementation of the orthogonal frequency division multiplexing systems [3]. Another practical example relates to efficient implementation of the matched spatial filtering (MSF) algorithm for performing the range and azimuth data compression in unfocused of fractionally focused synthetic aperture radar (SAR) system that both employ the pruned DFT-based MSF processing of the trajectory data signals performed in a factorized fashion in the socalled slow time and fast time data acquisition scales [4][5][6].…”

Section: Motivationmentioning

confidence: 99%

Unified commutation-pruning technique for efficient computation of composite DFTs

Castro-Palazuelos

Medina-Melendrez

Torres-Roman

et al. 2015

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

An efficient computation of a composite length discrete Fourier transform (DFT), as well as a fast Fourier transform (FFT) of both time and space data sequences in uncertain (non-sparse or sparse) computational scenarios, requires specific processing algorithms. Traditional algorithms typically employ some pruning methods without any commutations, which prevents them from attaining the potential computational efficiency. In this paper, we propose an alternative unified approach with automatic commutations between three computational modalities aimed at efficient computations of the pruned DFTs adapted for variable composite lengths of the non-sparse input-output data. The first modality is an implementation of the direct computation of a composite length DFT, the second one employs the second-order recursive filtering method, and the third one performs the new pruned decomposed transform. The pruned decomposed transform algorithm performs the decimation in time or space (DIT) data acquisition domain and, then, decimation in frequency (DIF). The unified combination of these three algorithms is addressed as the DFT COMM technique. Based on the treatment of the combinational-type hypotheses testing optimization problem of preferable allocations between all feasible commuting-pruning modalities, we have found the global optimal solution to the pruning problem that always requires a fewer or, at most, the same number of arithmetic operations than other feasible modalities. The DFT COMM method outperforms the existing competing pruning techniques in the sense of attainable savings in the number of required arithmetic operations. It requires fewer or at most the same number of arithmetic operations for its execution than any other of the competing pruning methods reported in the literature. Finally, we provide the comparison of the DFT COMM with the recently developed sparse fast Fourier transform (SFFT) algorithmic family. We feature that, in the sensing scenarios with sparse/non-sparse data Fourier spectrum, the DFT COMM technique manifests robustness against such model uncertainties in the sense of insensitivity for sparsity/non-sparsity restrictions and the variability of the operating parameters.

show abstract

Section: Motivationmentioning

confidence: 99%