Small unmanned aerial vehicles (UAVs) have become very popular in aerial photography, while radar imaging can provide more supplementary information. Therefore, installing radar on UAVs can be applied in fields such as science, agriculture, and environmental monitoring. However, UAV SAR systems are often too heavy and require support from large UAVs.
This article introduces a method and application of synthetic aperture radar (SAR) imaging using a small consumer-grade UAV. The advantage of this lightweight SAR system compared to traditional SAR systems lies in its low cost and portability, allowing measurements in places that were previously impossible. However, due to the use of consumer-grade equipment, its reliability and stability need improvement, and it is affected by environmental and weather factors, such as wind speed, temperature, and humidity, which can impact radar signal transmission and reception, affecting imaging quality.
This SAR system includes the following components, which together form a compact SAR system that can be installed on small UAVs for imaging:
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PulsON 410 USB Radar: Used for transmitting and receiving radar signals, employing back-projection imaging technology to achieve synthetic aperture radar imaging, with suitable frequency range and interfaces. -
Raspberry Pi + Wi-Fi Dongle: Used for controlling the radar and data transmission. -
Helical Antenna: A broadband 5-turn helical antenna mounted on an aluminum plane, used for transmitting and receiving radar signals, with broadband performance and high gain.
The system requires noticeable scatterers, such as people or vehicles, and the instability of UAV flight is also a challenge.The article also mentions future research directions, including continuing downward-looking SAR imaging, studying radar signals in co-polarized and cross-polarized scenarios, and conducting SAR imaging through optical obstacles (such as smoke and vegetation).
In the scientific field, this technology can be used for geological exploration, terrain measurement, and ocean monitoring; in agriculture, it can be used for land use, crop growth monitoring, and disaster early warning; in environmental monitoring, it can be used for water resource management, weather forecasting, and natural disaster monitoring. Therefore, this technology has potential prospects in scientific, agricultural, and environmental monitoring applications.
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