Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

In this paper, a novel multi-beam transmission scheme is proposed to enhance physical layer (PHY) security. This scheme utilizes index modulation (IM) based on frequency diverse array (FDA) beamforming with cooperative legitimate users (LUs). By leveraging IM based on FDA, the indices associated with multiple LUs are directly used as additional sources to transmit information. Using this scheme, the confidential messages can be retrieved only if all the index information and the corresponding symbols are obtained. This adds an extra level of PHY security hence reduces the possibility of information interception by eavesdroppers (Eves). Here instead of maximizing the secrecy rate, we propose a minimum message transmission power criterion to design the FDA beamforming vector, subject to prescribed minimum received power at each of the LUs, in addition to the orthogonality constraints of the remaining LUs. This provides a reliable and accurate transmission assurance for the LUs to obtain the corresponding messages under minimum message transmission power. To further deteriorate the quality of the received signals at Eves, we also add artificial noise (AN). Moreover, by elaborating the time-variant characteristics, we provide a new and more accurate perspective for the range-time dimension secure transmission. Numerical simulations confirm the effectiveness of the proposed scheme.

Wireless sensors network (WSN) is widely used in the Internet of Things at present. However, limited energy source is a critical problem in the improvement and practical applications of WSN, so it is necessary to improve the energy efficiency. As another important evaluation criterion of transmission performance, throughput should be improved too. To mitigate both of the problems at the same time, by taking the advantages of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) at the medium access control (MAC) layer of WSN, we propose a hybrid TDMA/CSMA MAC layer protocol. Meanwhile, we design a novel power control scheme to further reduce the energy consumption and optimize the transmission slots. The simulation results demonstrate that the proposed protocol significantly improves the throughput and energy efficiency.

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.

Directional modulation (DM) technique based on artificial noise (AN) enables the physical layer security (PLS) for wireless communications in free space. However, on one hand, the introduction of AN results in less power efficient for a DM system; on the other hand, the AN-aided DM technique is strongly dependent on the channel state information (CSI) of transceivers, which greatly limits its practical application. What is more, when the eavesdropper (EVE) is slowly approaching the legitimate user (LU) or the EVE and the LU are in the same direction, the transmission security cannot be guaranteed. To circumvent these problems, DM technique with multiple parameters weighted-type fractional Fourier transform (MP-WFRFT) and chaotic scrambling (CS) aided is proposed in this paper to realize the power-efficient and security-enhanced wireless transmissions. Then, the symbol error rate (SER), secrecy rate, robustness, and anti-interception performance of the proposed method are analyzed and simulated. The simulation results are presented to verify that the proposed method is more power-efficient than the traditional AN-aided DM schemes, and the PLS is guaranteed due to the proposed approach, albeit knowing the intended direction for an EVE.

Microwave remote sensing instruments such as synthetic aperture radar (SAR) play an important role in scientific research applications, while they suffer great measurement distortion with the presence of radio frequency interference (RFI). Existing methods either adopt model−based optimization or follow a data−driven black−box learning scheme, and both have specific limitations in terms of efficiency, accuracy, and interpretability. In this paper, we propose a hybrid model−constrained deep learning approach for RFI extraction and mitigation by fusing the classical model-based and advanced data-driven method. Considering the temporal-spatial correlation of target response, as well as the random sparsity property for time−varying interference, a joint low−rank and sparse optimization framework is established. Instead of applying the iterative optimization process with uncertain convergency, the proposed scheme approximates the iterative process with a stacked recurrent neural network. By adopting this hybrid model−constrained deep learning strategy, the original unsupervised decomposition problem is converted to a supervised learning problem. Experimental results show the validity of the proposed method under diverse RFI scenarios, which could avoid the manual tuning of model hyperparameters as well as speed up the efficiency.