On Using “Stochastic Learning on the Line” to Design Novel Distance Estimation Methods for Three-Dimensional Environments

2019 ◽  
pp. 39-49
Author(s):  
Jessica Havelock ◽  
B. John Oommen ◽  
Ole-Christoffer Granmo
Keyword(s):  
Three Dimensional ◽  
Distance Estimation ◽  
Estimation Methods ◽  
Stochastic Learning

scholarly journals Novel Distance Estimation Methods Using “Stochastic Learning on the Line” Strategies

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 48438-48454 ◽  
Author(s):  
Jessica Havelock ◽  
B. J. Oommen ◽  
Ole-Christoffer Granmo
Keyword(s):  
Distance Estimation ◽  
Estimation Methods ◽  
Stochastic Learning

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

2020 ◽  
pp. 095440702094139
Author(s):  
Jun Liu ◽  
Rui Zhang ◽  
Shihao Hou
Keyword(s):  
Distance Estimation ◽  
Estimation Methods ◽  
Vanishing Point ◽  
Estimation Model ◽  
The Road ◽  
Attitude Angle ◽  
Driving Assistance ◽  
Lane Line ◽  
Texture Orientation ◽  
On The Road

Perceiving the distance between vehicles is a crucial issue for advanced driving assistance systems. However, most vision-based distance estimation methods do not consider the influence of the change in camera attitude angles during driving or only use the vanishing point detected by lane lines to correct the pitch angle. This paper proposed an improved pinhole distance estimation model based on the road vanishing point without the lane line information. First, the road vanishing point is detected based on the dominant texture orientation, and the yaw and pitch angles of the camera are estimated. Then, a distance estimation model considering attitude angle compensation is established. Finally, the experimental results show that the proposed method can effectively correct the influence of the camera attitude angle on the distance estimation results.

scholarly journals Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic

2020 ◽  
Vol 12 (20) ◽  
pp. 8724
Author(s):  
Paul Schwarzbach ◽  
Julia Engelbrecht ◽  
Albrecht Michler ◽  
Michael Schultz ◽  
Oliver Michler
Keyword(s):  
Three Dimensional ◽  
Virus Transmission ◽  
Distance Estimation ◽  
Signal Strength ◽  
Air Transport ◽  
Aircraft Cabins ◽  
Social Distancing ◽  
Simulation Based ◽  
Cabin Environment

With the rise of COVID-19, the sustainability of air transport is a major challenge, as there is limited space in aircraft cabins, resulting in a higher risk of virus transmission. In order to detect possible chains of infection, technology-supported apps are used for social distancing. These COVID-19 applications are based on the display of the received signal strength for distance estimation, which is strongly influenced by the spreading environment due to the signal multipath reception. Therefore, we evaluate the applicability of technology-based social distancing methods in an aircraft cabin environment using a radio propagation simulation based on a three-dimensional aircraft model. We demonstrate the susceptibility to errors of the conventional COVID-19 distance estimation, which can lead to large errors in the determination of distances and to the impracticability of traditional tracing approaches during passenger boarding/deboarding. In the context of the future connected cabin, a robust distance measurement must be implemented to ensure safe travel. Finally, our results can be transferred to similar fields of application, e.g., trains or public transport.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

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