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A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input multiple-output (MIMO) technology is an attractive method to improve angular resolution. This paper proposes MIMO waveforms and their processing that can be used in HFSWR systems. This dual modulation method applies linear frequency modulation to each pulse and orthogonal polyphase codes for a few consecutive pulses to enable MIMO processing. The proposed method can effectively remove the correlation of mutual interference and exhibits excellent performance in removing multiple-time-around echoes. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) technology has recently attracted attention with regard to improving the angular resolution of small antennas such as automotive radars. If appropriately placed, the co-located transmit and receive arrays can make a large virtual aperture. This paper proposes a new method for designing arrays by adopting a structure with minimum redundancy. The proposed structure can significantly increase the virtual array aperture while keeping the transmit and receive antennas at the same size. We describe the application of the proposed method to subarray-type antennas using multi-channel transceivers, which is essential for arranging RF hardware in a small antenna operating at high frequency. Further, we present an analysis of the final beam pattern and discuss its benefits and limitations. [ABSTRACT FROM AUTHOR]

We develop a fast-deployed crop health monitoring system using state-of-the-art technologies to collect data from crop fields in order to take appropriate and timely actions. For the proposed resource optimized system, Saudi Arabian agriculture is taken as a case study. To achieve the desire goals, we harness IoT (Internet of Things) and drones in agriculture to establish rapid system deployment. This paper focuses on the data collection from crop field by organizing heterogeneous IoT devices in clusters and localise them for data harvesting. Clusters are formed by considering the path of UAVs, sensors heterogeneity, weather conditions, fluctuation of sensor nodes, and the communication cost of IoT devices. For localisation, carrying larger or heavier arrays of antennas and receivers with a small size UAV is also a major issue considered in this paper. Hence, we introduce a dynamic clustering and virtual antenna array to develop a complete data collection scheme supported by simulations and experimental tests with proof-of-concept devices. The results are analysed and found promising in terms of energy efficiency, throughput, ease of use, and deployment time. Whole the system is developed with the concept that it can install in rural and remote area with minimum deployment time and agile enough that can collect data in worst conditions (bad weather, hostile environment, fluctuating nodes, poor infrastructure, with or without an established network). In broader sense it can map easily in many similar applications where data is needed to be harvested from a wide range of heterogeneous sensors without existing any infrastructure and ground topology.