Frequency-modulated continuous wave radar techniques typically have inadequate angular resolutions due to the limited aperture sizes of antenna arrays in spite of employing multiple-input multiple-output (MIMO) techniques. Therefore, despite the existence of multiple objects, angularly close objects with similar distances and relative velocities are recognized as one single object. Autonomous driving requires the accurate recognition of road conditions. This requirement is one of the critical issues to be solved to distinguish significantly close objects. This paper proposes a technique referred to as an antenna element space pseudo-peak suppressing (APPS) method to resolve angularly close targets. The proposed APPS method aims to identify closely spaced objects on roads. These angularly close targets cause a single peak in a spatial spectrum obtained by a beamformer-based angle estimation. The APPS considers this single peak as pseudo. The APPS radar cancels this pseudo peak from the spatial spectrum. Then, the obtained residual received signal is analyzed. With these procedures, the APPS identifies the number of targets. The APPS also estimates the target angles. The proposed APPS is experimentally validated using a typical single-chip MIMO-based radar evaluation board with three transmit (TX) and four receive (RX) antennas. The experimental results confirm that the proposed APPS successfully resolves angularly close pseudo targets with an angle difference of approximately $$0.5^\circ $$ 0 . 5 ∘ .
This paper focuses on estimating the angle and size of different-sized targets using a radar system that estimates the angles of trucks, vehicles, bicycles, and pedestrians. The problem is that the presence of a sidelobe from the larger target degrades the accuracy of estimating the angle and size of the smaller target in multiple target detection. In this paper, a novel scheme, called antenna element space interference cancelling (AIC), is proposed for reducing the influence of the sidelobe. The performance of the proposed AIC radar system was evaluated through both computer simulations and experiments. The simulation results show that the proposed AIC radar system can estimate the angles of smaller targets within a 1-degree error, and the size estimation error is 1 dB. The experimental results also show that the proposed AIC radar is effective in detecting smaller targets that cannot be detected with traditional methods due to the sidelobes of larger targets.
We propose a framework to determine a secure distance between a drone with an ultrahigh-frequency band radio frequency identification (UHF band RFID) reader and metallic objects affixed with RFID tags. The secure distance avoids order changes in received signal strength indicator (RSSI) values among the identified RFID tags in the field of view of the RFID reader. This distance enables a drone operator to securely operate the drone while identifying the RFID tag on the front of an object based on the measurements of RSSI values. An RFID tag located on the front of an object provides the maximum RSSI value. However, multipath propagation alters the RSSI values. Therefore, a framework is needed to determine a secure distance considering the multipath effects. Although inventory management systems based on drones and RFID systems have been proposed to date, this article establishes a framework to determine the secure distance. In the proposed framework, RFID tag and reader radiation patterns and multipath propagation effects were considered. The proposed framework was evaluated theoretically and experimentally. To evaluate and demonstrate the secure distance, we measured the RSSI values of two RFID tags attached to a metallic balcony. The height from the ground and spacing of the two RFID tags were 1.5 m and 1.3 m, respectively. In this environment, the secure distance was 3.8 m. The experimentally obtained distance that avoids order changes in RSSI values corresponded well with that obtained by this framework. The proposed secure distance is crucial when either drones or robots are introduced to inventory management systems.
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