scholarly journals The Integration of Photodiode and Camera for Visible Light Positioning by Using Fixed-Lag Ensemble Kalman Smoother

2019 ◽  
Vol 11 (11) ◽  
pp. 1387
Author(s):  
Yuan Zhuang ◽  
Qin Wang ◽  
You Li ◽  
Zhouzheng Gao ◽  
Bingpeng Zhou ◽  
...  
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Estimation Error ◽  
Navigation Systems ◽  
Stochastic Sampling ◽  
Kalman Smoother ◽  
Multiple Data ◽  
Data Source ◽  
3D Positioning ◽  
Single Data

Visible Light Positioning (VLP) has become one of the most popular positioning and navigation systems in this decade. Filter-based VLP systems can provide real-time solutions but have limited accuracy. On the contrary, fixed-interval smoothers can help VLP achieve higher accuracy but require post-processing. In this article, a trade-off solution, Fixed-Lag Ensemble Kalman Smoother (FLEnKS), is proposed for VLP to achieve a semi-real-time and accurate positioning solution. The forward part of the FLEnKS is based on the Ensemble Kalman Filter (EnKF), which uses stochastic sampling with ensemble members and enables a better reflection of the features of nonlinear systems. The backward filter in the FLEnKS compensates for the estimation error from the forward filter using the linearization based on error states and further reduces the estimation error. Furthermore, multiple data from both photodiode (PD) and camera are fused in the proposed FLEnKS for VLP, which further improves the accuracy of conventional VLP with a single data source. Preliminary field test results show that the proposed FLEnKS provides a semi-real-time positioning solution with the average 3D positioning accuracy of 15.63 cm in dynamic tests.

Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Ping Yi ◽  
Songling Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Fusion ◽  
Incomplete Information ◽  
Data Sources ◽  
Fusion Technique ◽  
Crowd Management ◽  
Multiple Data ◽  
Data Source ◽  
Single Data

This paper introduces applications of the Dempster–Shafer (D-S) data fusion technique in transportation system decision making. D-S inference is a statistics-based data classification technique, and it can be used when data sources contribute discontinuous and incomplete information and no single data source can produce an overwhelmingly high probability of certainty for identifying the most probable event. The technique captures and combines the information contributed by the data sources by using Dempster’s rule to find the conjunction of the events and to determine the highest associated probability. The D-S theory is explained and its implementation described through numerical examples of a ride-hauling service and of crowd management at a subway station. Results from the applications have shown that the technique is very effective in dealing with incomplete information and multiple data sources in the era of big data.

scholarly journals Target integrity assessment based on image and track information

2021 ◽  
Vol 39 (5) ◽  
pp. 1022-1028
Author(s):  
Songyun Xie ◽  
Xianghui LIU ◽  
Xiaoliang WU ◽  
Chuanlin GAO ◽  
Dongrui SHEN
Keyword(s):  
Real Time ◽  
Probability Model ◽  
Assessment Method ◽  
Image Texture ◽  
Integrity Assessment ◽  
Evaluation Time ◽  
Before And After ◽  
Data Source ◽  
Single Data Source ◽  
Single Data

The rapid and accurate integrity assessment of targets can provide important guarantee and reference for the subsequent decision of the implementer. The current research on target integrity assessment mainly adopts single data source or complex probability model, which leads to inability to balance the needs of accuracy and real-time. In order to solve this problem, a new target integrity assessment method based on image and track information is proposed in this paper. Image texture, corner points and track parameters before and after the execution of the target are used to transform the integrity assessment issue into a classification issue, and a comprehensive assessment result is made by combining various classification results. The experimental results show that the assessment results based on both image and track information reached 97.5%, higher than the evaluation results from a single data source, and the evaluation time was controlled in milliseconds, which not only improved the accuracy rate but also ensured the real-time assessment.

Network Selection in Wireless Heterogeneous Environment Based on Available Bandwidth Estimation

2019 ◽  
Vol 12 ◽  
Author(s):  
Kiran Ahuja ◽  
Brahmjit Singh ◽  
Rajesh Khanna
Keyword(s):  
Real Time ◽  
Estimation Error ◽  
Network Selection ◽  
Bandwidth Estimation ◽  
Heterogeneous Environment ◽  
Bootstrap Approximation ◽  
Available Bandwidth ◽  
Internet Connection ◽  
Available Bandwidth Estimation ◽  
Selection Of

Background: With the availability of multiple options in wireless network simultaneously, Always Best Connected (ABC) requires dynamic selection of the best network and access technologies. Objective: In this paper, a novel dynamic access network selection algorithm based on the real time is proposed. The available bandwidth (ABW) of each network is required to be estimated to solve the network selection problem. Method: Proposed algorithm estimates available bandwidth by taking averages, peaks, low points and bootstrap approximation for network selection. It monitors real-time internet connection and resolves the selection issue in internet connection. The proposed algorithm is capable of adapting to prevailing network conditions in heterogeneous environment of 2G, 3G and WLAN networks without user intervention. It is implemented in temporal and spatial domains to check its robustness. Estimation error, overhead, estimation time with the varying size of traffic and reliability are used as the performance metrics. Results: Through numerical results, it is shown that the proposed algorithm’s ABW estimation based on bootstrap approximation gives improved performance in terms of estimation error (less than 20%), overhead (varies from 0.03% to 83%) and reliability (approx. 99%) with respect to existing techniques. Conclusion: Our proposed methodology of network selection criterion estimates the available bandwidth by taking averages, peaks, and low points and bootstrap approximation method (standard deviation) for the selection of network in the wireless heterogeneous environment. It monitors real-time internet connection and resolves internet connections selection issue. All the real-time usage and test results demonstrate the productivity and adequacy of available bandwidth estimation with bootstrap approximation as a practical solution for consistent correspondence among heterogeneous wireless networks by precise network selection for multimedia services.

Real-time subsurface scattering with single pass variance-guided adaptive importance sampling

2020 ◽  
Vol 3 (1) ◽  
pp. 1-21
Author(s):  
Tiantian Xie ◽  
Marc Olano ◽  
Brian Karis ◽  
Krzysztof Narkowicz
Keyword(s):  
Real Time ◽  
Adaptive Sampling ◽  
Monte Carlo Sampling ◽  
Diffusion Profile ◽  
Correct Result ◽  
Subsurface Scattering ◽  
Stochastic Sampling ◽  
Light Gradient ◽  
Temporal Variance ◽  
Importance Function

In real-time applications, it is difficult to simulate realistic subsurface scattering with differing degrees translucency. Burley's reflectance approximation by empirically fitting the diffusion profile as a whole makes it possible to achieve realistic looking subsurface scattering for different translucent materials in screen space. However, achieving a physically correct result requires real-time Monte Carlo sampling of the analytic importance function per pixel per frame, which seems prohibitive to achieve. In this paper, we propose an approximation of the importance function that can be evaluated in real-time. Since subsurface scattering is more pronounced in certain regions (e.g., with light gradient change), we propose an adaptive sampling method based on temporal variance to lower the required number of samples. We propose a one phase adaptive sampling pass that is unbiased, and able to adapt to scene changes due to motion and lighting. To further improve the quality, we explore temporal reuse with a guiding pass prior to the final temporal anti-aliasing (TAA) phase that further improves the quality. Our local guiding pass does not constrain the TAA implementation, and only requires one additional texture to be passed between frames. Our proposed variance-guided algorithm has the potential to make stochastic sampling algorithm effective for real-time rendering.

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