Comparisons of Non-line-of-Sight Inter-Vehicle Communications in the urban environment between 5.9GHz and 700MHz bands (Poster)

2012 ◽  
Author(s):  
Seii Sai ◽  
Takuro Oshida ◽  
Ryokichi Onishi ◽  
Akira Yoshioka ◽  
Hideaki Tanaka
Keyword(s):  
Urban Environment ◽  
Line Of Sight ◽  
Non Line Of Sight

Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy

2007 ◽  
Author(s):  
Jonathon Emis ◽  
Bryan Huang ◽  
Timothy Jones ◽  
Mei Li ◽  
Don Tumbocon
Keyword(s):  
United States ◽  
United States Navy ◽  
The United States ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Low-cost SPAD sensing for non-line-of-sight tracking, material classification and depth imaging

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Clara Callenberg ◽  
Zheng Shi ◽  
Felix Heide ◽  
Matthias B. Hullin
Keyword(s):  
Low Cost ◽  
Line Of Sight ◽  
Depth Imaging ◽  
Material Classification ◽  
Non Line Of Sight

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

scholarly journals Non-Line-of-Sight Imaging with Picosecond Temporal Resolution

2021 ◽  
Vol 127 (5) ◽  
Author(s):  
Bin Wang ◽  
Ming-Yang Zheng ◽  
Jin-Jian Han ◽  
Xin Huang ◽  
Xiu-Ping Xie ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Thermal non-line-of-sight imaging from specular and diffuse reflections

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Masaki Kaga ◽  
Takahiro Kushida ◽  
Tsuyoshi Takatani ◽  
Kenichiro Tanaka ◽  
Takuya Funatomi ◽  
...  
Keyword(s):  
Angular Distribution ◽  
Heat Source ◽  
Real World ◽  
Diffuse Reflection ◽  
Far Infrared ◽  
Infrared Light ◽  
Line Of Sight ◽  
Thermal Camera ◽  
Reflection Component ◽  
Non Line Of Sight

Abstract This paper presents a non-line-of-sight technique to estimate the position and temperature of an occluded object from a camera via reflection on a wall. Because objects with heat emit far infrared light with respect to their temperature, positions and temperatures are estimated from reflections on a wall. A key idea is that light paths from a hidden object to the camera depend on the position of the hidden object. The position of the object is recovered from the angular distribution of specular and diffuse reflection component, and the temperature of the heat source is recovered from the estimated position and the intensity of reflection. The effectiveness of our method is evaluated by conducting real-world experiments, showing that the position and the temperature of the hidden object can be recovered from the reflection destination of the wall by using a conventional thermal camera.

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