Radar-based Simulations of Through-wall Detection for Moving Human Targets
By Lina Sorg
As the name implies, through-wall detection is the detection of visually impaired objects—often human targets—through a wall or other physical barrier that prevents sight. The technique uses radar to pinpoint periodic motions in the form of subtle movements like swinging arms, respiration, and even heartbeat. Detection of both static and moving humans through walls and within buildings has many important applications in security and search-and-rescue operations, particularly after natural disasters. Unsurprisingly, identifying vital signs of moving human beings is much more difficult than for static targets.
During a minisymposium presentation at the 2022 SIAM Annual Meeting, which is currently taking place in Pittsburgh, Pa., Jiguang Sun of Michigan Technological University discussed his team’s use of ground-penetrating radar (GPR) to perceive the presence of humans through walls. He then utilized these experiments to model and simulate single-input multiple-outputs (SIMO) radar data for the detection of moving targets.
GPR generates electromagnetic waves at numerous frequencies to reconstruct the presence of an underground object, though Sun uses it to detect objects on the other side of a wall (see Figure 1). He explained that different configurations of GPR exhibit different frequencies. A higher frequency in the electromagnetic waves results in a shallower penetration depth. In contrast, a lower frequency leads to deeper penetration. The equipment emits and returns waves very quickly, processing as many as 800 waves in one minute. “Within one second, it can measure a lot of signals,” Sun said. “It sends them to the ground and reflects them back.” The outcome is a GPR image of whatever entity is located underground at the spot in question, and the signal appearance varies based on the precise location of the machine relative to the object.
Figure 1. Example of ground-penetrating radar equipment. Image courtesy of Jiguang Sun.
One common application of GPR is landmine detection. In certain parts of the world, particularly areas in Asia and Africa, hidden underground landmines can explode and result in casualties. GPR is able to find these landmines prior to detonation. In addition, the technique can locate underground tunnels between country borders that facilitate drug trade and other illegal activities. “More than 168 tunnel attempts [from Mexico] have been identified since the first cross-border tunnel was documented in Arizona in 1990,” Sun said. GPR has also found tunnels between North and South Korea.
Figure 2. Ground-penetrating radar image of rapid respiration. Figure courtesy of [1].
Sun’s efforts involve collaboration with geophysicists and engineers. His equipment has 270 megahertz (MHz) of GPR, which means that it can penetrate a surface—in this case, a wall—up to six meters. The experimental setting comprises a room with furniture and two people inside. One person remains still and just breathes, and the other moves around a bit and swings their arms. Sun’s team places the equipment against the wall and waits for the reflective signals. If a moving, living object is on the other side, the radar images will reflect it. Interestingly, Sun notes that a tank of water can elicit a very similar radar response because humans are composed primarily of water.
The resulting GPR images of normal and rapid respiration present similarly, but their appearances are complex (see Figure 2). The presence of something is evident, but that object could be a person, a water tank, or even a piece of furniture. Swinging arms, however, deliver strong signals of pure particle motion. Sun and his team utilize a fast Fourier transform (FFT) to find a range of frequencies by experimenting with two people who breathe quickly versus slowly and swing their arms quickly versus slowly (see Figure 3). The group can use these frequencies to calculate the rough position of the person in the room.
Figure 3. Fast Fourier transform for two people in the experimentation room who breathe quickly versus slowly and swing their arms quickly versus slowly. Figure courtesy of [1].
Sun uses these experiments of more stationary individuals to build a model and generate simulations for actively moving targets. He uses Maxwell’s equation, the Ricker wavelet, and the time-domain finite element method to simulate SIMO radar data; the permittivities and conductivities serve as the model parameters. Because the simulation has multiple receivers, Sun conducts a back projection to reconstruct the consecutive target locations and deliver it approximate position. Continuously connecting these positions reveals the target’s trajectory by creating a curve of vital signs in the radar image. “Since the target is moving, we can’t use FFT analysis on the horizontal line,” Sun said. Instead, he finds the target’s moving path, conducts Fourier analysis along that path, and looks for the presence of respiration or other similar motions. The reconstructed frequency coincides with that of the original model.
Sun concluded his presentation with a discussion of future directions for this study. Given the success of the simulations, he intends to extend the moving target analysis to real-world measurements. He also noted that other researchers are using micro-Doppler techniques for signal detection purposes, so he hopes to compare his method’s performance with that of micro-Doppler. Finally, while his current methods perceive respiration, they cannot presently recognize heartbeats or similarly weak signals. Given the importance of such signals in search-and-rescue operations, Sun would like to explore this component as well.
References
[1] Li, J., Zeng, Z., Sun, J., & Liu, F. (2012). Through-wall detection of human being’s movement by UWB radar. IEEE Geosci. Remote Sens. Lett., 9(6), 1079-1083.
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Lina Sorg is the managing editor of SIAM News. |