Topic
Spectral density estimation
About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.
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10 Apr 1978
TL;DR: A generalization of the short-time Fourier transform is presented which performs constant-percentage bandwidth analysis of time-domain signals and is shown to exhibit frequency-dependent time and frequency resolution.
Abstract: A generalization of the short-time Fourier transform is presented which performs constant-percentage bandwidth analysis of time-domain signals. The transform is shown to exhibit frequency-dependent time and frequency resolution. A synthesis transform is also developed which provides an analysis-synthesis system which is an identity in the absence of spectral modification (given a mild analysis window constraint). The effect of stationary multiplicative modifications is discussed. Finally, similarities between the constant-Q spectral domain and the human auditory system are explored, and some implications for acoustic signal processing mentioned.
79 citations
TL;DR: The adaptation of an iterative Fourier transform algorithm for the calculation of theoretical spectral phase functions required for pulse shaping applications and is shown to converges much faster than both alternative methods.
Abstract: We demonstrate the adaptation of an iterative Fourier transform algorithm for the calculation of theoretical spectral phase functions required for pulse shaping applications. The algorithm is used to determine the phase functions necessary for the generation of different temporal intensity profiles. The performance of the algorithm is compared to two exemplary standard approaches. i.e. a Genetic Algorithm and a combination of a Simplex Downhill and a Simulated Annealing algorithm. It is shown that the iterative Fourier transform algorithm converges much faster than both alternative methods.
78 citations
23 Apr 2012
TL;DR: This paper focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements, and compares this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR).
Abstract: SAR tomography is a thriving three-dimensional imaging modality that is commonly tackled by spectral estimation techniques. As a matter of fact, the backscattered power along the vertical direction can be readily obtained by computing the Fourier spectrum of a stack of multi-baseline measurements. Alternatively, recent groundbreaking work has addressed the tomographic problem from a parametric viewpoint, thus estimating effective scattering centers by means of covariance matching techniques. In this paper, we introduce a compressed sensing based covariance matching approach that allows us to retrieve the complete vertical structure of forested areas. For this purpose, we employ sparse representations in the wavelet domain and propose suitable pre-filtering techniques. Finally, we validate this approach by using fully polarimetric L-band data acquired by the E-SAR sensor of DLR.
78 citations
TL;DR: A fast and efficient algorithm based on the alternating split Bregman technique is proposed to carry out the optimization with computational complexity of time-frequency (TF) decomposition in the framework of sparse regularization theory.
Abstract: In this paper, time-frequency (TF) decomposition (TFD) is studied in the framework of sparse regularization theory. The short-time Fourier transform is first formulated as a convex constrained optimization where a mixed l1-l2 norm of the coefficients is minimized subject to a data fidelity constraint. Such formulation leads to a novel invertible decomposition with adjustable TF resolution. Then, a fast and efficient algorithm based on the alternating split Bregman technique is proposed to carry out the optimization with computational complexity [N2 log(N)]. Window length is a key parameter in windowed Fourier transform which affects the TF resolution; a novel method is also presented to determine the optimum window length for a given signal resulting to maximum compactness of energy in the TF domain. Numerical experiments show that the proposed sparsity-based TFD generates high-resolution TF maps for a wide range of signals having simple to complicated patterns in the TF domain. The performance of the proposed algorithm is also shown on real oil industry examples, such as ground roll noise attenuation and direct hydrocarbon detection from seismic data.
78 citations
01 Jan 2004
TL;DR: In this paper, a new method for the detection and parameter estimation of multicomponent LFM signals based on the fractional Fourier transform is presented, which can effectively suppress the interferences on the detection of the weak components brought by the stronger components.
Abstract: This paper presents a new method for the detection and parameter estimation of multicomponent LFM signals based on the fractional Fourier transform. For the optimization in the fractional Fourier domain, an algorithm based on Quasi-Newton method is proposed which consists of two steps of searching, leading to a reduction in computation without loss of accuracy. And for multicomponent signals, we further propose a signal separation technique in the fractional Fourier domain which can effectively suppress the interferences on the detection of the weak components brought by the stronger components. The statistical analysis of the estimate errors is also performed which perfects the method theoretically, and finally, simulation results are provided to show the validity of our method.
78 citations