The compressive sensing (CS) based tomographic SAR (TomoSAR) three-dimensional (3D) imaging method has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver requires iterative calculation and hence is computationally expensive; second, the CS solver needs hyperparameters selection, which commonly requires cost-inefficient try-and-error attempts. Recently, the iterative CS solver is suggested to be replaced by a deep learning network for a tremendous processing speed improvement. However, the existing deep learning based TomoSAR imaging algorithms suffer from the problem of model inadaptability, i.e., being inadaptive to the observation model and the signal energy model and hence is low accuracy. This paper proposes a new model-adaptive network (MAda-Net) to implement deep learning based TomoSAR 3D imaging with a much improved processing accuracy. First, a new adaptive model-solving (AMS) module is introduced to solve the problem of the observation model inconsistency between the real spatially varying one and the approximately fixed one employed by the network. Second, a new adaptive threshold-activation (ATC) module is introduced to solve the problem of the signal energy model inconsistency between the real backscattered echo and the simulated echo for network training. The effectiveness of the proposed method has been verified by the computer simulations and the real unmanned aerial vehicle (UAV) SAR experiments.

The thrust toward 5G communication has placed an increasing weight upon the performance bounds of current technologies. As the number of spectrum users continues to grow, the need for efficient use of spectral resources grows along with it. SSDL, which stands for Sequentially-Switched Delay Lines, provides a solution to this issue. The SSDL concept is a time-switching strategy, which uses a set of switches and transmission lines to simultaneously route transmitted and received waveforms to their respective ports over a broad bandwidth. SSDL does not incur a lower bound to its operating frequency, and experimental results of two different SSDL architectures demonstrate broadband non-reciprocity.

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets.

Frequency diverse array (FDA) has been considered as a potential solution to improve radar performance, such as interference suppression and target detection. In this letter, we propose a low-cost implementation of FDA based on two-wave mixing technique. In this method, two linear frequency modulated continuous wave signals are propagating against each other, and their frequency differences caused by propagation delays are converted to a frequency offset in FDA through periodically mixing operation. The two-wave mixing-based FDA automatically creates a desired frequency distribution and phase correlation between the antennas and provides additional phase controlling capability. We also propose a mirrored two-wave mixing FDA, in which the waveform peak can be controlled to only propagate in desired directions.

Large scale phased array now becomes one of the most critical components in wireless communication system. However, the high cost and complexity of the phase controlling in conventional phased array makes it difficult to be widely deployed in commercial use. Alternatively, a frequency-controlled beam steering method with fixed radiation frequency based on sequentially mixing of the propagating IF and LO waves has been proposed as a low-cost solution. The beam pointing direction can be changed by single frequency controller rather than varying any phase shifters or tunable components on the antennas. However, this technique relies on accurately split the input IF and LO waves to a mixers array through an all passive power distribution network. This paper discusses the practical design consideration of a robust two wave mixing array and two sets of cascaded unequal power dividers are employed to efficiently distribute the IF and LO signal. To ensure constant radiation frequency, an external reciprocal mixer is used to keep the frequency difference between LO and IF being same as desired RF. The measured result shows that the proposed signal distribution network successfully achieves desired phase and amplitude of the converted RF signal.

The distributed coherent aperture radar (DCAR) utilizes full coherent processing (FCP). Compared to single radar unit observations, N radar units can achieve an N ³ times increase in signal-to-noise ratio (SNR), providing an advantage in observing distant targets. However, its stringent requirements for time and phase of multiple radar units make coherent parameters (CPs) estimation the crucial aspect of FCP. The paper introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). Firstly, the FCP procedure is outlined, and the signal model with CPs is established. Secondly, the principles for selecting satellites and observation time in the experimental setup are induced, along with an analysis of the SNR variations during processing. Furthermore, through a hierarchical processing approach using the generalized Radon-Fourier transform (GRFT), inter-pulse coherence processing, inter-unit-radar coherence processing, and inter-sub-aperture coherent or non-coherent processing are sequentially conducted. The utilization of generalized sharpness and gradient descent method facilitated CPs estimation, subsequently enhancing the SNR post-coherence processing. Finally, the proposed method has been validated through simulation and successfully applied to a real DCAR system.