Frequency diverse array (FDA), by virtue of its range-angle-dependent beam pattern, has drawn a substantial attention in recent years. However, as far as the time-variant rangeangle-coupled beam pattern is concerned, such inherent flaw has become a bottleneck of its further development. To address this issue, herein, a logarithm-based optimised static non-linear frequency offset (LOSNFO) for FDA is proposed, which can successfully alleviate its inherent flaw, thus producing a short-time range-angle-decoupled beam pattern with low sidelobe levels (SLLs) and narrow half-power beam widths (HPBWs). Moreover, for practical applications, the proposed LOSNFO considers the propagation effect, which is always neglected in its counterpart. Numerical results are reported to demonstrate the effectiveness and superiority of the proposed LOSNFO-FDA. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

This letter presents a T‐junction power divider (PD) with both tunable operating frequency and dividing ratio, simultaneously. The proposed design consists of two adjustable 90° phase shifters and a pair of shunt stubs loaded with two varactor diodes. The operating frequency can be tuned by controlling the varactor diodes loaded onto the 90° phase shifters. In addition, the tunable shunt stubs comprised of a short circuited and an open circuited stubs loaded with varactor diodes are employed to realize impedance matching and different dividing ratios at accurate operating frequencies. For verification, a prototype of the proposed PD with dimensions of 25.9 mm × 50 mm is designed, fabricated, and measured. The measured results show that the operating frequency can be tuned from 1 to 1.55 GHz, while the power dividing ratio can be tuned from 2:1 to 20:1. Good agreement between the simulation and measurement results is observed.

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Abstract-The deformation of antenna array due to external factors results in a significant degradation in the performance of the array direction of arrival (DOA) estimation. To solve this problem, an equivalent method based on the estimation of signal parameters by rotational invariance technique (ESPRIT) in single signal source for the array position errors is proposed in this paper. This method is mainly for the low-order deformation of the array and is based on the equivalent value of the position error. The DOA estimation of ESPRIT algorithm for single signal source was corrected. The simulation results show that the position error equivalent method can effectively equalize the position error caused by the vibration deformation of the array. When the equivalent position error is known, the orientation of the single signal source can be effectively corrected.

Compared with remote sensing image (RSI) captioning methods based on the traditional encoder-decoder model, two-stage RSI captioning methods include an auxiliary remote sensing task to provide prior information, which enables them to generate more accurate descriptions. In previous two-stage RSI captioning methods, however, the image captioning and the auxiliary remote sensing tasks are handled separately, which is time-consuming and ignores mutual interference between tasks.To solve this problem, we propose a novel joint-training two-stage (JTTS) RSI captioning method. We use multi-label classification to provide prior information, and we design a differentiable sampling operator to replace the traditional non-differentiable sampling operation to index the multi-label classification result. In contrast to previous two-stage RSI captioning methods, our method can implement joint-training, and the joint loss allows the error of the generated description to flow into the optimization of the multi-label classification via back-propagation. Specifically, we approximate the Heaviside step function with the steep logistic function to implement a differentiable sampling operator for the multi-label classification. We propose a dynamic contrast loss function for multi-label classification task to ensure that a certain margin is maintained between the probabilities of the positive label and the negative label during sampling. We design an attribute-guided decoder to filter the multi-label prior information obtained by the sampling operator to generate more accurate image captions. The results of extensive experiments show that the JTTS method achieves state-of-the-art performance on the RSICD, the UCM-Captions, and the Sydney-Captions datasets.