Model Order Selection For Sum of Complex Exponentials

Albert, Raymundo; Galarza, Cecilia G.

doi:10.1109/urucon53396.2021.9647257

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…On the other hand, using a hard threshold as in [33] to truncate the Hankel matrix H(y) tends to overestimate the model order when the SNR is high. In this work, we used a combined scheme, as presented in [34], in which a constrained optimization problem is posed with the function used in ESTER (or SAMOS).…”

Section: Preprocessingmentioning

confidence: 99%

Robust Target Classification Using UWB Sensing^*

2023

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Contactless material characterization has received widespread attention in the radar and engineering domains. Specifically, impulsive Ultra Wideband (UWB) systems are a versatile technology for the nondestructive characterization of samples because the scattered field produced by the targets is highly dependent on their composition and shape. After the initial transient response to the transmitted pulse, the scattered signal can be decomposed as a sum of complex exponentials, called complex natural resonances (CNR), which are dependent only on the geometry and composition of the target. Using this result, a classification problem was formulated to discriminate among targets, and a processing strategy was proposed to solve it. In particular, by using spectral decomposition tools, the information obtained from the physical model can be exploited in combination with data-driven learning techniques. Consequently, a classification strategy that is robust to modeling uncertainties and experimental perturbations was designed. To assess the performance of the new scheme, it was tested using both synthetic and experimental data obtained from targets illuminated with a UWB radar. The results showed substantial gains compared to classification using time-domain signals. INDEX TERMSComplex natural resonances, pattern classification, radar signal processing, spectral analysis.

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Section: Preprocessingmentioning

confidence: 99%

Robust Target Classification Using UWB Sensing^*

2023

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An inverse method for characterization of dynamic response of 2D structures under stochastic conditions

LI,

ZINE,

ICHCHOU

et al. 2024

Chinese Journal of Aeronautics

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Measurement-efficient quantum Krylov subspace diagonalisation

Zhang,

Wang,

et al. 2024

Quantum

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The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, can be much more powerful when generalised to quantum computing. However, quantum Krylov subspace algorithms are prone to errors due to inevitable statistical fluctuations in quantum measurements. To address this problem, we develop a general theoretical framework to analyse the statistical error and measurement cost. Based on the framework, we propose a quantum algorithm to construct the Hamiltonian-power Krylov subspace that can minimise the measurement cost. In our algorithm, the product of power and Gaussian functions of the Hamiltonian is expressed as an integral of the real-time evolution, such that it can be evaluated on a quantum computer. We compare our algorithm with other established quantum Krylov subspace algorithms in solving two prominent examples. To achieve an error comparable to that of the classical Lanczos algorithm at the same subspace dimension, our algorithm typically requires orders of magnitude fewer measurements than others. Such an improvement can be attributed to the reduced cost of composing projectors onto the ground state. These results show that our algorithm is exceptionally robust to statistical fluctuations and promising for practical applications.

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Model Order Selection For Sum of Complex Exponentials

Cited by 3 publications

References 17 publications

Robust Target Classification Using UWB Sensing^*

Robust Target Classification Using UWB Sensing^*

An inverse method for characterization of dynamic response of 2D structures under stochastic conditions

Measurement-efficient quantum Krylov subspace diagonalisation

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Model Order Selection For Sum of Complex Exponentials

Cited by 3 publications

References 17 publications

Robust Target Classification Using UWB Sensing*

Robust Target Classification Using UWB Sensing*

An inverse method for characterization of dynamic response of 2D structures under stochastic conditions

Measurement-efficient quantum Krylov subspace diagonalisation

Contact Info

Product

Resources

About

Robust Target Classification Using UWB Sensing^*

Robust Target Classification Using UWB Sensing^*