This paper presents the theoretical analysis and the experimental evaluation of a new switched beam antenna designed to operate at 2.45 GHz. The antenna enables direction of arrival estimation using six directional planar elements arranged to form a platonic solid geometry. It also supports polarization diversity, and it is suitable for single-anchor indoor positioning applications. We adopt the Cramer-Rao bound to study the estimation accuracy of the proposed antenna in absolute 2-D target positioning using received signal strength measurements. First, we describe the design principles for the radiators, we provide an extensive characterization of the switched antenna prototype, and we discuss positioning applications. We then report experimental data that support the results of the theoretical analysis and show consistency between theoretical expectation and the measurements. Finally, we discuss results from proof-of-concept operative indoor positioning example, showing an average localization error as low as 1.7 m.

Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified. This work proposes a novel data augmentation method based on a deterministic model to generate a simulated dataset of radar micro-Doppler signatures suitable for unmanned aerial vehicle (UAV) target classification, without requiring measurement data. It is shown that the DNN trained using the properly generated model-based data offers improved classification accuracy performance. Results are presented for a two-class classification of the number of UAV motors using a 77-GHz frequency-modulated continuous-wave (FMCW) automotive radar system. The effectiveness of the proposed methodology is proven: a classification accuracy of 78.68% is achieved using a convolutional neural network (CNN) trained using the synthetic dataset, while an accuracy of 66.18% is achieved by using a typical signal processing data augmentation method on a limited measured dataset.

We propose an RF-based localization system that works using a single anchor node. The anchor is equipped with a switched-beam directional antenna that is installed on the ceiling of a room and collects signal strength information sufficient for absolute 2D target positioning. Indoor measurements are used to show satisfactory localization results with range-free (proximity), range-based and fingerprinting schemes.

Motivated by recent interest in directional antennas for WSNs, we propose a four-beam patch antenna (FBPA) designed to meet the size, cost and complexity constraints of sensor nodes. We use in-field experiments with COTS motes to demonstrate substantial benefits to WSN applications. Used outdoors, the FBPA extends the communication range from 140 m to more than 350 m, while indoors it suppresses the interference due to multipath fading by reducing the signal variability of more than 70%. We also show interference suppression from IEEE 802.11 g systems and discuss the use of the antenna as a form of angular diversity useful to cope with the variability of the radio signal. Experimental data are analyzed to derive model parameters intended for use in future network simulations.

This paper introduces a new analytical method suitable for the design of the single-feed circular polarization (CP) microstrip patch antenna. Specifically, the proposed method imposes simultaneously the circular polarization condition as well as an arbitrary input impedance matching condition. The two conditions are enforced by an analytical method derived from an equivalent circuit model of a quasi-symmetrical patch antenna, and manipulated to control the modal detuning. This method can be used as an aid to speed up the design procedure for CP antennas even working with a numeric CAD tool. The validation of this approach is proven designing and fabricating a prototype implementing an original design, which consists of a circular disc slotted by a concentric elliptical cut with coaxial feed and operating at the center frequency of 2.45 GHz. The fabricated prototype exhibited a return loss of about 19 dB, within an impedance bandwidth of 130 MHz, a measured gain of 3.85 dB and a 0 dB axial ratio at 2.45 GHz with a corresponding modal phase difference of 87 degrees.

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.