No abstract

No abstract

The present study introduces an inkjet-printed flexible coplanar waveguide (CPW) patch antenna array concept. Single antenna and four-element antenna arrays were characterized, which were attached to a sub-miniature version A (SMA) connector via an innovative solderless, 3-D printed "plug and play type" tightener. &#xD;Furthermore, indoor wireless communication and internet of things (IoT) scenarios with commonly used wall materials including gypsum and plywood board, on which the patch antennas and antenna arrays can be attached, were also presented. In order to validate the concept, design and fabrication iterations in parallel with numerical and experimental investigations were executed. To elaborate, single antenna and antenna array configurations without and with wall materials were characterized to see their functionality at 2.4 GHz resonance frequency with beyond 300 MHz bandwidth, respectively. The results demonstrated that the investigated configurations fulfill the short-range radio transmission and can be utilized, e.g. for indoor backscattering type communications and wireless sensing applications, as an affordable and versatile alternative for their conventional counterparts. Being attached to their corresponding background materials, single antenna specimens were measured to have return losses beyond 18 dB and peak gains around 1 dBi while higher peak gains above 6 dBi were detected for antenna arrays.&#xD;Moreover, the antenna arrays can enable multiple-input and multiple-output (MIMO) communication. The proposed arrays had diversity performance in terms of return losses higher than 15 dB, isolation more than 20 dB, envelope correlation coefficient (ECC < 0.001), diversity gain (DG > 9.95 dB), mean effective gain (MEG < -3 dB), power ratio factor (k < 0.5 dB), and channel capacity loss (CCL < 0.4 bits/s/Hz).

No abstract

This paper presents a method for three dimension (3D) drone location estimation based on measured signals transmitted from a flying drone. During the experiment, we considered a single antenna mounted on the drone for signal transmission and a 4-by-4 rectangular array positioned at a known stationary location for receiving the incoming signal. Once the signal strength from the source is measured, the 3D position of the drone is estimated using the MUltiple SIgnal Classification (MUSIC) algorithm. The estimated values are then fed into an Extended Kalman Filter (EKF) as a measurement model, and the movement of the drone is formulated in a 3D trajectory plane. In order to evaluate the performance of our approach, we considered a histogram distribution and probability density function (pdf) of the drone position estimation error during its trajectory. The estimated 3D position of the drone is compared with the GPS values, and we ensure that the drone is localized based on the received signal from the experimental setup by first estimating the direction of the signal using MUSIC, and then tracking it using EKF in the predefined drone trajectory area.