scholarly journals ARTI (Adaptive Radio Tomographic Imaging): One New Adaptive Elliptical Weighting Model Combining with Tracking Estimates

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1034 ◽  
Author(s):  
Chunhua Zhu ◽  
Jiaojiao Wang ◽  
Yue Chen
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Measurement Noise ◽  
Moving Target ◽  
Indoor Environments ◽  
Smoothing Filter ◽  
Model Combining ◽  
Proposed Model ◽  
Radio Tomographic Imaging ◽  
Weight Model

Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky–Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.

scholarly journals An Image Restoration Method Using Matrix Transform and Gaussian Mixture Model for Radio Tomographic Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Fei Gao ◽  
Cheng Sun ◽  
Heng Liu ◽  
Jianping An ◽  
Shengxin Xu
Keyword(s):  
Imaging System ◽  
Gaussian Mixture ◽  
Tomographic Imaging ◽  
System Model ◽  
Wireless Sensor ◽  
Target Image ◽  
Estimation Model ◽  
Rti Model ◽  
Restoration Method ◽  
Radio Tomographic Imaging

Radio Tomographic Imaging (RTI) is an attractive technique for imaging the nonmetallic targets within wireless sensor network. RTI has been used in many challenging environments and situations. Due to the accuracy of Radio Tomographic Imaging system model and the interference between the wireless signals of sensors, the image obtained from the RTI system is a degraded target image, which cannot offer sufficient details to distinguish different targets. In this paper, we treat the RTI system as an image degraded process, and we propose an estimation model based on mixture Gaussian distribution to derive the degradation function from the shadowing-based RTI model. Then we use this degradation function to recover an original image by a method called constrained least squares filtering. So far, many imaging models have been proposed for localization; however, they do not have a satisfied imaging accuracy. Simulated and experimental results show that the imaging accuracy of our proposed method is improved, and the proposed method can be used in the real-time circumstances.

scholarly journals Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 439 ◽  
Author(s):  
Shengxin Xu ◽  
Heng Liu ◽  
Fei Gao ◽  
Zhenghuan Wang
Keyword(s):  
Multipath Fading ◽  
Spatial Diversity ◽  
Tomographic Imaging ◽  
Link Quality ◽  
Small Scale ◽  
Reconstruction Method ◽  
Indoor Environments ◽  
Localization Accuracy ◽  
Measurement Variation ◽  
Radio Tomographic Imaging

Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.

ARTI: An Adaptive Radio Tomographic Imaging System

2017 ◽  
Vol 66 (8) ◽  
pp. 7302-7316 ◽  
Author(s):  
Ossi Kaltiokallio ◽  
Riku Jantti ◽  
Neal Patwari
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Radio Tomographic Imaging

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hiroyuki Kawakatsu
Keyword(s):  
Moving Average ◽  
Curse Of Dimensionality ◽  
Moving Averages ◽  
Conditional Covariance ◽  
Arch Models ◽  
Empirical Application ◽  
Proposed Model ◽  
Out Of Sample ◽  
Weight Model ◽  
Weighted Moving Average

AbstractThis paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

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