Drones are no longer a preserve for military organizations and governments only, as various institutions and individuals are adopting them for various purposes. Hence, with the fast adoption of drones, it is necessary to improve the detection and location pinpointing technology (Peacock & Johnstone, 2013). Though there are different ways of detection drones such as through audio, visuals, and radio frequency, radar detection remains the conventional way of UAV detection. Various studies and product development are being conducted on how to improve UAV radar capabilities, one of them being the adoption of synthetic aperture radars (SARs).
A study by Koo et al. (2012) details the development of Synthetic Aperture Radar (SAR) to be used in detecting and pinpointing unmanned aerial vehicles (UAVs). SAR is imaging radar that uses relative motion between its antenna and the target under observation and processes signals (Koo et al. 2012 p. 245). In comparison to the real aperture radar, SAR is effective in that it can obtain finer spatial resolution making it easy to pinpoint the location of the UAV.
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Koo et al. states that the concept of SAR can be traced back to Carl Wiley who suggested that a Doppler beam sharpening system should be used to improve the azimuth resolution of radar (Koo et al., 2012 p.246). Presently, SAR is an important tool with a range of applications such as sea and ice monitoring, oceanography and so forth. The potential of SAR is unlimited; thus its possibility of improving UAV detection and location pinpointing is being studied (Koo et al., 2012 p.246).
Another study by Ouchi (2013) looks at the recent developments of SAR. Ouchi asserts that SAR has the potential of producing high-resolution radar images, and since it uses microwave band it has day and night imaging capabilities (Ouchi, 2013 p. 717). According to the study, SARs on board on UAVs have been studied as early as the late 1990s and it is recently gaining attention.
According to Mccaney (2014), the army recently awarded approximately $99 million contract for small SAR systems aimed at improving radar capabilities to smaller UAV. SARs combine propagated radar signals and complex digital processing (Mccaney, 2014). Additionally, SARs can operate in any kinds of environments/ weather condition; hence it will be easy to pinpoint the location of a UAV even the UAVs used in oceanography (Mccaney, 2014). SARs will be more useful in military drones in that they have higher capabilities of mapping out terrains and identifying other improvised explosive devices.
Koo et al. (2012) discusses the benefits of using SARs, in comparison to other radars SARs are cost effective, low risk and provide timely operations. Many researchers are developing different types of SARs for the different UAVs and different conditions. An example of an effective SAR is Lynx SAR by Sandia National Laboratories; Lynx is a state of the art SAR with high resolution, provides real time action, and ground moving target indicator (Koo et al., 2012 p. 248). Lynx SAR us a Ku-band multimode radar that can scan small to large moving objects, detect speed and its vehicular movements.
In conclusion, SAR possesses certain advantages which are not found in the conventional radars. Xiang et al. (2009) argues that SARs equipped with low- accuracy inertial navigation system have the potentials of drastically reducing UAV motion errors caused by atmospheric turbulence.
References
Koo, V. C., Chan, Y. K., Vetharatnam, G., Chua, M. Y., Lim, C. H., Lim, C. S., & Bin Shahid, M. H. (2012). A New Unmanned Aerial Vehicle Synthetic Aperture Radar for Environmental Monitoring. Progress in Electromagnetic Research , 122 , 245-268.
Mccaney, K. (2014). Army to Put High-Quality Radar into Smaller Drones. Defense Systems. Retrieved From: Https://Defensesystems.Com/Articles/2014/09/02/Army-Synthetic- Aperture-Radar-Small-Uavs.Aspx
Ouchi, K. (2013). Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics. Remote Sensing , 5 (2), 716-807.
Peacock, M., & Johnstone, M. N. (2013). Towards Detection and Control of Civilian Unmanned Aerial Vehicles. ECU Security Research Institute.
Xing, M., Jiang, X., Wu, R., Zhou, F., & Bao, Z. (2009). Motion Compensation for UAV SAR Based On Raw Radar Data. Geoscience And Remote Sensing, IEEE Transactions On , 47 (8), 2870-2883.
