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. 2019 Jun 28;19(13):2879.
doi: 10.3390/s19132879.

Review-Microwave Radar Sensing Systems for Search and Rescue Purposes

Nguyen Thi Phuoc Van  1   2 , Liqiong Tang  3 , Veysel Demir  4 , Syed Faraz Hasan  3 , Nguyen Duc Minh  5 , Subhas Mukhopadhyay  6
Affiliations

Review-Microwave Radar Sensing Systems for Search and Rescue Purposes

Nguyen Thi Phuoc Van et al. Sensors (Basel). .

Abstract

This paper presents a survey of recent developments using Doppler radar sensor in searching and locating an alive person under debris or behind a wall. Locating a human and detecting the vital signs such as breathing rate and heartbeat using a microwave sensor is a non-invasive technique. Recently, many hardware structures, signal processing approaches, and integrated systems have been introduced by researchers in this field. The purpose is to enhance the accuracy of vital signs' detection and location detection and reduce energy consumption. This work concentrates on the representative research on sensing systems that can find alive people under rubble when an earthquake or other disasters occur. In this paper, various operating principles and system architectures for finding survivors using the microwave radar sensors are reviewed. A comparison between these systems is also discussed.

Keywords: Doppler radar; breathing signal; detection probability; finding survivors; heartbeat; vital signals.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Received signal in the time domain (a) and in the frequency domain (b) at a distance of 0.5 m [101].
Figure 2
Figure 2
System overview and real-life application example with measured performance [102]. LNA, linear amplifier.
Figure 3
Figure 3
Block diagram to test multiple targets’ detection [68]. PA, power amplifier.
Figure 4
Figure 4
Traditional FFT method (a) versus the variational mode decomposition (VMD)-based method (b) [68].
Figure 5
Figure 5
Continuous wave system diagram.
Figure 6
Figure 6
Schematic of the 1.15-GHz microwave radar [82].
Figure 7
Figure 7
Testing performance of Kum-Mu Chen et al.’s [82] system on the earthquake rubble model of Michigan State University.
Figure 8
Figure 8
Schematic of the life-detector system [92].
Figure 9
Figure 9
Frequency versus time of transmitting and receiving signal at frequency modulation continuous wave (FMCW) radar. BW, bandwidth.
Figure 10
Figure 10
Topology of the FMCW radar used to detect humans through a wall [121].
Figure 11
Figure 11
Experiment setup of the FMCW radar to detect a human through a wall [121].
Figure 12
Figure 12
Transmitting and receiving signal of the FMCW-CW radar in the (a) frequency domain and (b) time domain [123].
Figure 13
Figure 13
Experiment setup of the step frequency continuous wave (SFCW) radar to detect a human through brick: (a) size view, (b) top view, and (c) postures [124].
Figure 14
Figure 14
Breathing rate of a person in a face up posture at different time resolutions [124].
Figure 15
Figure 15
Diagram of a basic random noise UWB radar sensor.
Figure 16
Figure 16
A schematic of a digital random noise UWB radar sensor [127].
Figure 17
Figure 17
Detecting human movement through a wall using a digital random noise UWB radar sensor [127].
Figure 18
Figure 18
Block diagram of a typical UWB radar system. PRF, pulse repeat frequency; OSC, oscillator.
Figure 19
Figure 19
Vital sign detection experiments for a human subject by the UWB radar sensor: (a) outdoors and (b) indoors [94].
Figure 20
Figure 20
Detection algorithm [94].
Figure 21
Figure 21
The STFT in the case of human location at a distance of 9 m [94].
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