Improved Parameter Estimation Algorithm Research Articles

Health Observation of the Capital Airport South Expressway Based on Improved MT-InSAR Technology

Objectives: The Airport South Expressway in China is built in a soft soil area, which may induce great hidden danger to airport traffic safety operations. Technology or Method: A new method to improve Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, based on a novel time-series InSAR deformation model and an improved parameter estimation algorithm, is proposed for soft soil expressways monitoring. The functional relationship between the deformation and the creep parameters (viscosity and elastic modulus) based on the Maxwell model in one-dimensional linear rheology replaces the traditional InSAR linear model, and the creep physical parameters can be solved simultaneously in the solution process. The least-squares method with inequality constraints (LSIC) is induced to solve the unknown parameters. In total, 19 TerraSAR-X radar satellite images covering the South Expressway were utilized to validate the proposed method. The creep parameters for each pixel along the expressways and the time series deformation sequences from January 2012 to July 2014 were obtained. Results: As the results showed, the maximum settlement along the expressway was up to 125 mm, and the accuracy verification results showed that the modeling accuracy was 1.6 mm, with an improvement of 36.0% compared to the traditional linear model; the internal accuracy of the deformation results was 11.9 mm, accounting for 1.5% of the maximum deformation. Clinical or Biological Impact: Our method can provide data support and a reference for long-term health monitoring and early warning of infrastructure and traffic operation management in poor soil regions.

Insights on Foam Transport from a Texture-Implicit Local-Equilibrium Model with an Improved Parameter Estimation Algorithm

We present an insightful discussion on the implications of foam transport inside porous media based on an improved algorithm for the estimation of model parameters. A widely used texture-implicit local-equilibrium foam model, STARS, is used to describe the reduction of gas mobility in the state of foam with respect to free gas. Both the dry-out effect and shear-dependent rheology are considered in foam simulations. We estimate the limiting capillary pressure Pc* from fmdry values in the STARS model to characterize foam film stability in a dynamic flowing system. We find that Pc* is a good indicator of foam strength in porous media and varies with different gas types. We also calculate Pc* for different foaming surfactants and find that foam stability is correlated with the Gibbs surface excess concentration. We compare our improved parameter estimation algorithm with others reported in literature. The robustness of the algorithm is validated for various foam systems.

An Improved Parameter Estimation Algorithm of LFM Signal Based on the Fractional Fourier Transform

Linear Frequency Modulation (LFM) signal is a common signal in the radar system, how to do parameter estimation of LFM signal quickly and accurately is always the research hot spot. The fractional Fourier Transform (FRFT) as an emerging time-frequency analysis method, because of its transform nuclear is chirp matrix decomposition, so especially suitable for LFM signal processing. But it needs to do global search because the modulation frequency is unknown, which lead to a large amount of calculation. This paper proposed an improved parameter estimation algorithm of LFM signal. We made a rough estimation of modulation frequency by the fast Radon Wigner Transform (RWT) firstly, and then the amount of calculation can be reduced because it only needs to do local search for find the optimal fractional order. In addition, we made a comparison among the new method and other two LFM signal parameter estimation algorithms, and got simulation results under different SNRs. Simulation results show that, parameter estimation accuracy is improved by new algorithm compared to other two algorithms under the same calculation.

An Improved Parameter Estimation Algorithm Based on FFT for LFM Signal

Parameter estimation is essential to receive a linear frequency modulation (LFM) signal precisely, so in this paper an improved parameter estimation algorithm based on FFT is proposed. Compared to the traditional one-dimension search procedure, the algorithm just needs two FFT steps plus one correlation step only. Without searching process and low computational complexity, the algorithm is easy for project application. The simulations verify feasibility of the algorithm, when SNR is above-3 dB, the algorithm can work well.

An Improved Parameter Estimation Algorithm Based on FFT for LFM Signal

Parameter estimation is essential to receive a linear frequency modulation (LFM) signal precisely, so in this paper an improved parameter estimation algorithm based on FFT is proposed. Compared to the traditional one-dimension search procedure, the algorithm just needs two FFT steps plus one correlation step only. Without searching process and low computational complexity, the algorithm is easy for project application. The simulations verify feasibility of the algorithm, when SNR is above-3 dB, the algorithm can work well.

Improved parameter estimates based on the homodyned K distribution

Quantitative techniques based on ultrasound backscatter are promising tools for ultrasonic tissue characterization. There is a need for fast and accurate processing strategies to obtain consistent estimates. An improved parameter estimation algorithm for the homodyned K distribution was developed based on SNR, skewness, and kurtosis of fractional- order moments. From the homodyned K distribution, estimates of the number of scatterers per resolution cell (micro parameter) and estimates of the ratio of coherent to incoherent backscatter signal energy (k parameter) were obtained. Furthermore, angular compounding was used to reduce estimate variance while maintaining spatial resolution of subsequent parameter images. Estimate bias and variance from Monte Carlo simulations were used to quantify the improvement using the new estimation algorithm compared with existing techniques. Improvements due to angular compounding were quantified by the decrease in estimate variance in both simulations and measurements from tissue-mimicking phantoms and by the increase in target contrast. Finally, the new algorithm was used to derive estimates from 2 kinds of mouse mammary tumors for tissue characterization. The new estimation algorithm yielded estimates with lower bias and variance than existing techniques. For a typical pair of parameters (micro = 5 and k = 1), the bias and variance were reduced 67% and 16%, respectively, for the mu parameter estimates and 79% and 37%, respectively, for the k parameter estimates. The use of angular compounding further reduced the estimate variance, e.g., the variance of estimates for the micro parameter from measurements was reduced by a factor of approximately 90 when using 120 angles of view. Finally, statistically significant differences were observed in parameter estimates from 2 kinds of mouse mammary tumors using the new algorithm. These improvements suggest estimating parameters from the backscattered envelope can enhance the diagnostic capabilities of ultrasonic imaging.

Improved parametric reconstruction using variable projection optimization

This paper addresses the problem of image reconstruction from limited Fourier data. An improved parameter estimation algorithm is proposed for use with a polynomial model-based image reconstruction method. Results from magnetic resonance imaging data are shown to demonstrate the performance of the proposed algorithm.