Apr 21 2017
The system tracks a target enclosed in a 'scattering box' that impedes direct imaging. As the object moves, it imposes fluctuations on the light coming out of the box. The light is then collected by an integrating detector, which uses an algorithm to distinguish natural noise from the fluctuations caused by the object. Credit: Aristide Dogariu, University of Central Florida
A new solution to track objects hidden behind scattering media has been developed by researchers. This tracking process is carried out by examining the fluctuations in optical “noise” produced the very movement of these objects.
Researchers from the University of Central Florida (CREOL) have demonstrated this technique by tracking the location of an object as it moved inside an enclosed box. A report on this has been presented in The Optical Society's journal for high impact research, Optica.
The approach could bring about improvements in real-time remote sensing for military and several other applications. For instance, this approach can be used to track aircraft or vehicles traveling through fog. It could also be used in biomedical research areas that involve fast-moving particles that cannot be directly observed, according to the researchers.
Numerous technologies are available that can detect, describe and track objects that cannot be observed visually or that are far away. However, most existing technologies, such as Light Detection and Ranging (LIDAR), need line of sight between the sensor and the object, meaning they do not work well when the object is hidden by fog, clouds or various other conditions that scatter light.
We are promoting a paradigm shift. Instead of illuminating the object with a coherent beam of light, we’re illuminating it with random, or noise-like light. Looking at how the fluctuations of the light are modified by the interaction with the object allows us to retrieve information about the object.
Aristide Dogariu, University of Central Florida
Insights without a line of sight
One of two approaches is used by the existing tracking technologies. LIDAR, and other such laser-based methods, point a beam of light at the object and then move the beam around in order to obtain details about the trajectory, shape and size of the object. On the other hand, imaging-based methods take a series of images of the object and only then carry out computations in order to track its movement over time.
“These are very good strategies that have been in place for decades, and under ideal conditions their performance cannot be surpassed,” said Dogariu. “But as soon as something in the line of sight scatters and randomizes the light, you run into problems.”
More than a decade has been spent by Dogariu’s team to learn how to infer details from the fluctuations in light. The researchers earlier used these concepts to produce new tools that help in sensing the properties of materials and also for super-resolution microscopy. In their recent research, the team planned to track moving objects in situations where it is not possible to view the object and also pinpoint or control the directionality of the light shining on it.
An object that is hidden behind some scattering diffuser is not illuminated by a spatially coherent beam. The movement of the object, the size of the object and the properties of the object affect the statistical properties of the noise-like optical field, and this effect is what we measure.
Aristide Dogariu, University of Central Florida
Dogariu’s team succeeded in developing statistical methods to separate natural noise from fluctuations that are developed by the movement of the target object because light behaves in a predictable manner.
Testing the method
The researchers tested the approach by enclosing a small object within a plastic box custom designed to scatter light. A secondary light source is created inside the box by shinning a beam of coherent light onto one of the scattering walls. This light is then scattered by the target object and the light waves are further randomized when light travels through the scattering walls. This is followed by collecting the light outside the box with the help of an integrating detector, which distinguishes natural noise from the fluctuations caused by the object with the help of an algorithm.
“If the target that is surrounded by this enclosure starts to move, then the fluctuations that it imposes on the light coming out of the box can be detected from any direction very efficiently,” said Dogariu. A non-moving object cannot be identified by the system even though it can detect the hidden object from any location outside the enclosure.
Recently, a few other technologies have been developed that permit tracking of obscured objects by repeatedly imaging or scanning them over time. However, those approaches need large-scale data processing and complex optical instruments, which can make them impractical for following fast-moving objects.
Dogariu’s team, in their experiments, was able to accurately track the object’s movement within the scattering enclosure in real time by using a more versatile and simpler setup. “The advantage of recovering information based on fluctuations is that it is more robust against external perturbations,” said Dogariu. “It is robust against disturbances between the light source and the object and between the object and the receiver.”
New opportunities
The approach efficiently senses position for all degrees of freedom (diagonal, up-down and left-right) because the system extracts information about movement in each direction independently. The tracking accuracy is not affected when the object rotates or tilts as the method follows the motion of the target’s center of mass.
A major drawback of the method refers to the limited level of detail it can offer about the target object. The method cannot reveal the object’s material, color, or necessarily its shape even though it can detect the direction and speed at which the object moves and may be able to even detect the size of the object.
You cannot recover detailed information with this method, but if you simplify the question to what you really need to know, you can solve certain task-oriented problems.
Aristide Dogariu, University of Central Florida
Moving forward, the team is working on refining the approach to handle even more complex environments, scenes with lower levels of incoming light and larger scenes. The team believes that these improvements will help in bringing the system closer to real-world applications in remote sensing, biomedicine and several other areas.
Despite the fact that the research involved light waves, it is also possible to implement similar noise-based approaches in other domains, such as microwaves or acoustics, according to Dogariu.