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This paper presents a novel concept of antenna design for an electrically small unmanned aerial vehicle (UAV) using the theory of characteristic modes. With the knowledge of the characteristic modes of the UAV body, a multi-objective evolutionary algorithm is implemented to synthesize currents on the UAV body for achieving desired power patterns. Compact and low-profile feed structures are then designed to excite the synthesized currents. The feed structures are termed as probes, as they are used to excite the currents on the UAV body and cannot work as standalone antennas. The UAV body excited by these probes serves as the radiating aperture. Its reconfigurable radiation patterns are obtained by feeding each probe with proper magnitude and phase. In this method, the aperture of the UAV body is fully utilized. Practical issues associated with large antennas at low frequency band are eliminated. Knowledge for probe placement on such platform becomes more explicit. Simulated and measured results are presented to verify the design concept.

Characteristic mode (CM) analysis is carried out to understand the underlying physics of two popular circularly polarized (CP) microstrip patch antennas, namely U-slot and E-shaped CP patch antennas, for achieving better performance. Modifications of these two CP antennas with optimal feed position and compact patch size are explicitly obtained through examining the CMs, modal significance, and characteristic angle. Compared to conventional designs, the two modified designs have better axial ratio (AR) and cross-polarization performance without introducing any additional design cost or complexity. It is also interesting to find that: 1) offset probe feed in CP U-slot patch antennas will provide excellent AR performance; 2) cutting off the redundant section from CP E-shaped patch antenna will yield low cross polarization and more compact configuration.

This paper proposes a general characteristic mode-based design procedure of simple three steps for wideband circularly polarized (CP) antenna design. First of all, the characteristic mode analysis is carried out to understand the different modes of a proposed antenna geometry without feeding network. Second, modal currents and their corresponding modal fields (radiation patterns) are studied for choosing modes to shape the required radiation pattern. Finally, a suitable feeding structure is chosen to excite the desired modes at the same time owns a good impedance matching. As an example, a CP patch antenna fed with cross-shaped aperture is proposed and designed following the design procedure. Patch consisting of H-shaped unit cells is used as the radiator. Characteristic mode method is applied to analyze the modes of the proposed antenna and explains the property of wide band circular polarization. It is revealed that higher order modes of the patch can be used to achieve circular polarization over a wideband of frequency. The antenna is fabricated using printed circuit techniques. The return loss and radiation properties are measured and compared with simulation results. With the highly coupled units, a wide impedance bandwidth of 38.8% is obtained. Besides, a wide 3-dB axial ratio bandwidth of 14.3% is achieved.

This paper proposes a learning-based non-iterative method to solve electromagnetic (EM) scattering problems utilizing pix2pix, a popular generative adversarial network (GAN). Instead of calculating induced currents directly from a matrix inversion, a forward induced current learning method (FICLM) is introduced to calculate the induced current through a neural-network mapping. The scattered fields can be further calculated through a multiplication of the Green’s function with the predicted induced currents. Inspired by wave physics of scattering problems, we have designed three kinds of input schemes, covering different combinations of the given incident field and permittivity contrast, to evaluate the performance of the FICLM model under both single-incidence and multi-incidence cases. Numerical results prove that the proposed FICLM outperforms the method of moments (MoM) in terms of both computational speed and accuracy by use of reference data with a higher precision. The FICLM with the direct sum of permittivity contrast and a so-called Born-type induced current, achieves the best calculation accuracy and generalization capability compared to the other two inputs. The comparison with other types of neural networks, such as U-net, also demonstrates the superior performance of FICLM for dealing with complex scatterers due to the use of adversarial framework in pix2pix. The proposed method paves a new way for the fast solution of EM scattering problems through deep learning techniques.