Estimating the Direction-of-Arrival (DoA) of radiofrequency sources is essential in many wireless applications. Traditional DoA estimation based on bearing measurements requires multi-antenna arrays, which is not suitable for portable electronics for its large form factor. In this paper, we improved a novelty single-antenna-based DoA estimation technique, called virtual multi-antenna arrays. While the device is moving and continuously receiving signals, the DoA can be estimated by measuring the intercepted signals at several positions along the receiver's trajectory. Previous virtual array technology left two unsolved problems: some receiver movements are not able to estimate the DoA, without a theoretical basis to screen feasible trajectories; the virtual array requires precise, relative coordinates of the receiver, which is challenging in reality. This paper investigates the feasibility of the virtual array and improves its robustness by addressing these two unsolved problems. A theoretical foundation for feasible receiver trajectory determination is provided: the receiver has to move with accelerations to make the DoA observable. Also, we prove the nature that the DoA could be estimated by measuring the accelerations only, where precise receiver position is not mandatory. These results indicate that the virtual array is feasible with simple receiver movements and low-cost commercial equipment, thus significantly reducing system complexity and cost. Simulations are conducted to validate our theoretical predictions. The proof of concept implemented on a software-defined radio testbed also proves the validity and suitability of the improved DoA estimation technology in applications with form factor constraints.

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A method is proposed to estimate the direction of a ground radio-frequency (RF) transmitter by using an Unmanned Aerial Vehicle (UAV) equipped with a single antenna, which is critical when considering the form factor and computational capabilities of a UAV. By considering the received signal at several locations along its trajectory, the UAV receiver implicitly creates a virtual multi-antenna array (VMA), which can estimate the direction-of-arrival (DOA) of the transmitter. The major difficulty is the Local Oscillator (LO) frequency offset that occurs between the transmitter and the UAV receiver, which adds a cumulative phase offset to the received signal at each antenna of the virtual array. Oscillators of inferior qualities will undergo severe phase and frequency drifts over time, and these LO offsets must be estimated and compensated during DOA estimation. To overcome this difficulty, we proposed two approaches by estimating the LO frequency offset jointly with the direction of the transmitter. Then we extend the Multiple Signal Classification (MUSIC) algorithm to perform multidimensional estimation (including azimuth and elevation). In this paper, the proposed VMA method is simulated and tested by considering different virtual array geometries and various LO qualities. Simulation results prove the feasibility of our proposed method, and the median estimation error for azimuth and elevation are below 9 • and 12 • on average, even with low-quality oscillators.

With precision agriculture developing rapidly worldwide, water-saving, energy-saving, environment-friendly, and efficient agricultural production activities are effective ways to address human needs for agricultural products under the conditions of intensifying climate change, limited available arable land resources, and rapid population growth. Ground-based plant-protection machinery applied to large fields has difficulty solving the pest and disease prevention needs of mountain orchards since they feature undulating topography changes and low standardization of orchards. Unmanned aerial vehicles (UAVs) have broad development prospects in pest control in mountain orchards because of their advantages of not being restricted by terrain, strong maneuverability, and hover ability. This paper reviews the recent development of plant-protection UAVs from three perspectives, i.e., positioning and navigation technology, flight attitude control technology, and route planning in mountain orchards. We highlight that the future research should focus on following technology development, including (1) positioning navigation technology with high positioning accuracy and strong anti-interference capability, (2) intelligent control technology with high dynamic stability and better calculation accuracy, and (3) the optimization of the route-planning algorithm covering multiple constraints and the cluster cooperative operation scheme of plant-protection UAVs applicable to mountain orchards. These reviewed results could provide a reference for the future development of plant-protection UAVs, which will become the focus of future research.

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