Virtual Interpolation Research Articles

An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving Virtual Interpolation Array.

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.

A new virtual interpolation technology with range as object

A new virtual interpolation technology with range as object

Gridless Underdetermined DOA Estimation for Mobile Agents with Limited Snapshots Based on Deep Convolutional Generative Adversarial Network

Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array (ULA), which leads to grid mismatch issues and difficulty in ensuring accurate DOA estimation with insufficient sampling and in underdetermined scenarios. In order to solve these challenges, we propose a new DOA estimation method based on a deep convolutional generative adversarial network (DCGAN) with a coprime array. By employing virtual interpolation, the difference co-array derived from the coprime array is extended to a virtual ULA with more degrees of freedom (DOFs). Then, combining with the Hermitian and Toeplitz prior knowledge, the covariance matrix is retrieved by the DCGAN. A backtracking method is employed to ensure that the reconstructed covariance matrix has a low-rank characteristic. We performed DOA estimation using the MUSIC algorithm. Simulation results demonstrate that the proposed method can not only distinguish more sources than the number of physical sensors but can also quickly and accurately solve DOA, especially with limited snapshots, which is suitable for fast estimation in mobile agent localization.

RAPP: A Radio Tomography Localization Method Characterized by Performance Parameterization in Rapid-Moving RFID System

Exploring and developing the perception system is an important means to promote the development of intelligent transportation. In this paper, we propose RAPP, a radio tomography-based method enabling performance parameterization to achieve parameter adjustment of localization accuracy without dense deployment of passive UHF RFID devices. The reader antenna takes a rapid-moving system as the carrier and generates virtual antenna elements to inventory received signal strength (RSS) of the pre-deployed tags. To eliminate the impact of the lack of tag backward signals during the mobile process and supplement the link distribution for each segment of the monitored platform, the Cubic Spline Interpolation algorithm (CSIA) is proposed to homogenize the spacing of the virtual antenna elements by preprocessing the measurements. Then the optimized link information is adopted to perform radio tomographic imaging to recognize the locations of targets. Theoretical analysis on ill-condition is derived to analyze how the number of virtual interpolation elements affects the localization accuracy. Further derivation about the relationship between the Bayesian Cramér-Rao Bound (CRB) and the link density provides a solution to influence the localization performance through parameter adjustment. Extensive simulation and experiment show that RAPP outperforms on multiple targets localization and behaves robustly in a multipath environment.

A DOA Estimation Method for Uniform Circular Array Based on Virtual Interpolation and Subarray Rotation

A uniform linear array (ULA) or a multi-element planar array composed of uniform linear arrays is the main applied research array for direction-of-arrival (DOA) estimation in array signal processing. In recent years, the uniform circular array (UCA) is getting more attention because of its consistent azimuthal beam resolution, but the DOA estimation algorithm of a UCA is complex and difficult to implement. To solve this problem, a UCA positioning method based on virtual interpolation and subarray rotation (VISAR) is proposed. This method effectively reduces the computational complexity of DOA estimation for a UCA, and the simulation analysis of the algorithm shows that in terms of positioning performance, the proposed algorithm is better than the existing virtual interpolation algorithms in the case of a low signal-to-noise ratio (SNR).

Virtual Interpolation Images of Tumor Development and Growth on Breast Ultrasound Image Synthesis With Deep Convolutional Generative Adversarial Networks.

We sought to generate realistic synthetic breast ultrasound images and express virtual interpolation images of tumors using a deep convolutional generative adversarial network (DCGAN). After retrospective selection of breast ultrasound images of 528 benign masses, 529 malignant masses, and 583 normal breasts, 20 synthesized images of each were generated by the DCGAN. Fifteen virtual interpolation images of tumors were generated by changing the value of the input vector. A total of 60 synthesized images and 20 virtual interpolation images were evaluated by 2 readers, who scored them on a 5-point scale (1, very good; to 5, very poor) and then answered whether the synthesized image was benign, malignant, or normal. The mean score of overall quality for synthesized images was 3.05, and that of the reality of virtual interpolation images was 2.53. The readers classified the generated images with a correct answer rate of 92.5%. A DCGAN can generate high-quality synthetic breast ultrasound images of each pathologic tissue and has the potential to create realistic virtual interpolation images of tumor development.

Existence and stability in the virtual interpolation point method for the Stokes equations

In this paper, we propose a novel virtual interpolation point (VIP) method formulating discrete Stokes equations. We have formed virtual staggered structure for velocity and pressure from the actual computation node set. The VIP method by a point collocation scheme is well suited for meshfree scheme because the approximation comes from smooth kernels and kernels can be differentiated directly. This paper highlights our contribution to a stable flow computation without explicit structure of staggered grid. Our method eliminates the need to construct explicit staggered grid. Instead, virtual interpolation nodes play key roles in discretizing the conservative quantities of the Stokes equations. We have proved the inf-sup condition for VIP method with virtual structure of staggered grid and thus the existence and stability of discrete solutions follow.

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.