Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks

Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks

Parameter Estimation of Transient Multiexponential Signals Using SVD-ARMA and Multiparameter Deconvolution Techniques

Much has been reported about the analysis of transient multiexponentials data. In a previous paper, for example, this analysis was done using autoregressive moving average model which was applied to the deconvolved data arising from the application of Gardner transform followed by optimal compensation deconvolution to the original signal. Optimal compensation deconvolution uses a single parameter noise-reduction parameter. In this paper, a deconvolution parameter incorporating multiple noise-reduction parameters is used instead. Simulations and experimental results show that the proposed combination, despite its limitations supersedes several existing methods

Analysis of transient multiexponential signals using exponential compensation deconvolution

A three-step procedure for the parameter estimation of transient multiexponential signals is proposed. The first step involves the use of the classical Gardner transform to convert the data signal into a convolution model which is deconvolved using exponential compensation deconvolution technique in the second step. In the third step, eigenvector algorithms are used to process the resulting complex exponentials to obtain better estimates of decay rates and number of components. Simulation and experimental results show that this method outperforms previous approaches if a number of preprocessing parameters are correctly selected

Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks

35 Transient Multiexponential Data Selection Using Cramer Rao Lower Bound

Previously, analysis of transient multiexponential data using a combination of Gardner transform and parametric methods was shown to yield good results. However, one problem that remains unsolved is that of the nonstationarity of the data resulting from the associated deconvolution. Hitherto, trial and error methods have been used to select the qualitative length of the deconvolved data. In this paper, Cramer Rao Lower Bound (CRLB) is used to select the data truncation points for use with the MUSIC (Multiple Signal Classification), minimum norm and ARMA (autoregressive moving average) methods. Several simulations are made based on which truncation points are recommended for each of the three parametric methods

Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks

Effect of sampling on the parameter estimates of multicomponent transients

The need to estimate the parameters of transient multiexponential signals frequently arises in different areas of applied science. A classical technique that has been frequently used with different modifications is the Gardner transform. Gardner transform is used to convert the original data signal into a convolution model. Converting this model into a discrete type for further analysis depends on the selection of correct sampling conditions. Previously, a relationship between the sampling frequency and the weighting factor in the modified Gardner transform was derived. In this paper, the effect of this relationship on the accuracy of parameter estimates is investigated

Effect of multiple deconvolution parameters on the resolvability of decay rates of multiexponential signals

Noise reduction in deconvolution process has been a challenge to researchers in the field of signal processing. The problem is ill-posed and various algorithms have been developed to reduce noise enhancement. The effect of using multiple noise-compensating parameters in the deconvolution of multiexponential signals is considered in this paper. Three parameters are simultaneously adjusted to obtain optimal reduction in noise. It is shown that this approach performs better than a single parameter approach

Analysis of multicomponent transient signals using MUSIC superresolution technique

The problem of estimating the parameters of transient signals consisting of real decay constants has for long been a subject of study by many researchers. Such signals arise in many problems in Science and Engineering like nuclear magnetic resonance for medical diagnosis, deep-level transient spectroscopy, fluorescence decay analysis, etc. Many techniques have been suggested by researchers to analyse these signals but they often produce mixed results. A new method of analysis using modified MUSIC (multiple signal classification) subspace algorithm is successfully applied to the analysis of this signal. A noisy multiexponential signal is subjected to a preprocessing procedure consisting of Gardenerspsila transformation and inverse filtering. Modified MUSIC algorithm is then applied to the deconvolved data. The parameters of focus in this paper are the number of components and decay constants. It is shown that with this technique parameter estimates do not significantly change with signal to noise ratio. The superiority of this algorithm over conventional MUSIC algorithm is also shown