scholarly journals Passive 3D location estimation of non-line-of-sight objects from a scattered infrared light field

2021 ◽  
Author(s):  
Takahiro Sasaki ◽  
Connor Hashemi ◽  
James Leger
Keyword(s):  
Light Field ◽  
Location Estimation ◽  
Infrared Light ◽  
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.

scholarly journals A non-line-of-sight error mitigation method for location estimation

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668273 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mobile Station ◽  
Location Estimation ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location ◽  
Non Line Of Sight

Because there are always non-line-of-sight effects in signal propagation, researchers have proposed various algorithms to mitigate the measured error caused by non-line-of-sight. Initially inspired by flocking birds, particle swarm optimization is an evolutionary computation tool for optimizing a problem by iteratively attempting to improve a candidate solution with respect to a given measure of quality. In this article, we propose a new location algorithm that uses time-of-arrival measurements to improve the mobile station location accuracy when three base stations are available. The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. An object function is used to establish the nonlinear relationship between the intersections of the three circles and the mobile station location. The particle swarm optimization finds the optimal solution of the object function and efficiently determines the mobile station location. The simulation results show that the proposed algorithm performs better than the related algorithms in wireless positioning systems, even in severe non-line-of-sight propagation conditions.

scholarly journals Ultrafast light field tomography for snapshot transient and non-line-of-sight imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaohua Feng ◽  
Liang Gao
Keyword(s):  
Applied Sciences ◽  
Light Field ◽  
Moving Objects ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Line Of Sight ◽  
Temporal Dimension ◽  
Time Resolved ◽  
Imaging Strategy ◽  
Non Line Of Sight

AbstractCameras with extreme speeds are enabling technologies in both fundamental and applied sciences. However, existing ultrafast cameras are incapable of coping with extended three-dimensional scenes and fall short for non-line-of-sight imaging, which requires a long sequence of time-resolved two-dimensional data. Current non-line-of-sight imagers, therefore, need to perform extensive scanning in the spatial and/or temporal dimension, restricting their use in imaging only static or slowly moving objects. To address these long-standing challenges, we present here ultrafast light field tomography (LIFT), a transient imaging strategy that offers a temporal sequence of over 1000 and enables highly efficient light field acquisition, allowing snapshot acquisition of the complete four-dimensional space and time. With LIFT, we demonstrated three-dimensional imaging of light in flight phenomena with a <10 picoseconds resolution and non-line-of-sight imaging at a 30 Hz video-rate. Furthermore, we showed how LIFT can benefit from deep learning for an improved and accelerated image formation. LIFT may facilitate broad adoption of time-resolved methods in various disciplines.

scholarly journals Combination linear lines of position and neural network for mobile station location estimation

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771738 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Mobile Station ◽  
Base Station ◽  
Location Estimation ◽  
Base Stations ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Marquardt Algorithm ◽  
Station Location ◽  
Non Line Of Sight

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

Accuracy Bounds for Wireless Localization Methods

2009 ◽  
pp. 380-405 ◽  
Author(s):  
Michael L. McGuire ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Lower Bound ◽  
Location Estimation ◽  
Line Of Sight ◽  
Regularity Conditions ◽  
Node Localization ◽  
Localization Accuracy ◽  
Sensory Data ◽  
Line Of Sight Propagation ◽  
Cramer Rao Lower Bound ◽  
Non Line Of Sight

Node localization is an important issue for wireless sensor networks to provide context for collected sensory data. Sensor network designers need to determine if the desired level of localization accuracy is achievable from their network configuration and available measurements. The Cramér-Rao lower bound is used extensively for this purpose. This bound is loose since it uses only information from measurements in its calculations. Information, such as that from the sensor selection process, is not considered. In addition, non-line-of-sight radio propagation causes the regularity conditions of the Cramér-Rao lower bound to be violated. This chapter demonstrates the Weinstein-Weiss and extended Ziv-Zakai lower bounds for localization error which remain valid with non-line-of-sight propagation. These bounds also use all available information for bound calculations. It is demonstrated that these bounds are tight to actual estimator performance and may be used determine the available accuracy of location estimation from survey data collected in the network area.

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