Modern consumer and industrial unmanned aerial vehicles (UAVs) are easy to use flying sensor platforms. They offer stable flight, good maneuverability, hovering, and even waypoint flights in autopilot mode. For stabilization and localization sensors such as internal measurement units (IMUs) including gyroscope and accelerometer, barometric sensor, and global navigation satellite system (GNSS) are used. To sense the direct environment of the UAV, for instance for collision avoidance or fully automated flights, additional sensors are needed. State-of-the-art combinations of infrared sensors, ultrasonic sensors as well as vision based sensors (monocular and/or stereo vision) capture the close vicinity. Using radar sensors is advantageous, as they are able to directly sense range and velocity and are not prone to lighting conditions and contrast. With the help of a multi-channel radar, the angular information can also be extracted. UAVs can lift a considerable payload with respect to their size. All these characteristics combined with radar sensors make them a promising tool for a large variety of applications.
Multi-carrier waveforms such as orthogonal frequency-division multiplexing (OFDM) found their way into radar applications in the last few years. However, currently, typically only a fraction of the large baseband bandwidth required to obtain high resolution is available in practice due to hardware limitations. In this paper, we propose a frequency agile sparse OFDM radar processing which allows the transmission of consecutive bandwidth-reduced OFDM pulses on different carriers and thereby covering a much larger measurement bandwidth in a measurement frame. Through joint processing of multiple narrowband pulses and compressed sensing methods, high resolution and unambiguity in the joint range-velocity profile is obtained comparable to an equivalent wideband OFDM. It shows that a baseband bandwidth of 20 % of the full channel bandwidth is sufficient to reliably obtain the same result as for an equivalent wideband OFDM signal. The proposed processing scheme is validated using simulations and radar measurements at 77 GHz.
Abstract-The application of radar sensors for driver assistance systems and autonomous driving leads to an increasing probability of radar interferences. Those interferences degrade the detection capabilities and can cause sensor blindness. This paper uses a realistic road scenario to address the problems of a common countermeasure that simply removes interferenceaffected parts of time domain radar signals and thereby introduces a gap. The paper solves the problem with the application of a sparse sampling signal recovery algorithm that is also used for compressed sensing problems. It is shown that the signal recovery can clearly overcome the shortcomings of just removing interfered signal parts. In the end of the paper, the applicability of the used algorithm is verified with measured radar data.
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