Investigation of millimetre FMCW radar for improved situational awareness for fire fighting vehicles

Barrett, Matthew (2020) Investigation of millimetre FMCW radar for improved situational awareness for fire fighting vehicles. [USQ Project]

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The importance of situational awareness during a bush fire has long been a concern and emphasis among the fire fighting community. Until recently, mitigation against low situation awareness has been addressed by experience, in-depth training, and mentoring. Emerging technologies and sensing systems have a role to play in addressing the issue. However, without in-depth empirical evidence demonstrating the effectiveness of such a system in a bush fire, they will not be trusted by the firefighting community. Millimetre frequency modulated continuous wave (FMCW) radar is one possible option for improving fire crews' situational awareness during a bush fire. With recent developments in automotive radar technology, sensors capable of detecting objects with high accuracy are now readily available and affordable. Measurement capabilities include accurate range, bearing, elevation, velocity and return intensity (radar cross section) at a distance greater than 50 meters. The technology is insensitive to environmental influences such as smoke, fog, rain, poor lighting, or extreme temperatures. Alone, or combined with other sensor technology such as infrared imaging, will allow object identification in high temperature, dense smoke environments, therefore improving risk awareness.

A Texas Instruments (TI) IWR1443 mm-wave development board along with ROS (Robot Operating System) was used to evaluate the viability of mm-wave radar for fire fighting situational awareness. Experiments were performed to evaluate (1) the maximum detectable range of typical objects / obstacles encountered in these situations, (2) the horizontal antenna pattern of the IWR1443 sensor, and (3) radar cross-section measurements of a standing and fallen gum tree (the most common cause of the collision in firefighting vehicles), along with the maximum detectable range of a standing tree. Results are validated against published TI results, and where possible, simulated against standard theoretical tools used for radar system design.

Results show the IWR1443 can detect objects with millimetre accuracy at a range greater than fifty meters, with a -3dB beamwidth of approximately +- 30 degrees. Range results were evaluated against the commonly used radar range equation to determine the range predictability of a known RCS. Results are not comparable to the simulated results of the radar range equation. However, utilising the range calibration and antenna pattern data from this study, it is possible to evaluate the expected return intensity of a known RCS at a specified angle. Finally, radar cross-section experiments of both a standing and fallen tree, show an RCS of approximately 60 m2 with a maximum detectable range of 45m.

Informing future sensor system design, it was concluded that mm-wave radar sensing can accurately detect and differentiate multiple objects at a long-range with a wide field of view. Further, using RCS measurements taken of a standing and fallen gum tree, it can be shown that radar sensing data alone cannot determine the orientation of a tree, and therefore the risk it presents to fire fighting vehicles. Finally, it was demonstrated that radar system design tools such as the radar range equation cannot be used with certainty, meaning an empirical approach is required when designing for any non-standard application.

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Item Type: USQ Project
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Supervisors: Lobsey, Craig
Qualification: Bachelor of Engineering (Honours) (Mechatronics)
Date Deposited: 22 Jul 2021 00:19
Last Modified: 26 Jun 2023 03:49
Uncontrolled Keywords: fire fighting; FMCW radar; situational awareness; robot operating system; mm-wave radar
Fields of Research (2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
07 Agricultural and Veterinary Sciences > 0705 Forestry Sciences > 070503 Forestry Fire Management
Fields of Research (2020): 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400702 Automation engineering
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3007 Forestry sciences > 300706 Forestry fire management
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics

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