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.

This paper considers jointly optimizing the transmitting (Tx) waveform and the receivers of a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system. The proposed approach incorporates the design of the (complex-valued) communication receiving (Rx) coefficients, in addition to the radar Rx filters. We address the problem of maximizing the minimum radar signal-to-interference-plus-noise ratio (SINR) subject to communication SINR, per-antenna power and peak-to-average-power ratio (PAPR) constraints. A successive convex approximation (SCA) algorithm is developed to find a good solution for the resultant nonconvex design problem. To accelerate that algorithm and to improve the solution quality, we further propose a local-approximation-refinement SCA (LAR- SCA) algorithm which possesses guaranteed convergence properties. In addition, a sub-block design technique is developed to reduce the design complexity in the case of long Tx sequences. Numerical results show that by incorporating the communication Rx coefficients into the joint design, the radar and communication capabilities of the DFRC system can be significantly enhanced over the state-of-the-art designs, while maintaining an efficient algorithm.

Unlike the conventional phased array, the frequency-diverse array (FDA) employs a tiny frequency increment across the array elements, which is capable of providing range–angle–time-dependent beampattern, and thereby offers potential benefits in target localization and interference mitigation. Reported literature on FDA mostly focuses on its intrapulse (fast-time) property and ignores the interpulse (slow-time) feature induced by frequency offset. In this article, incorporating the neglected inherent slow-time coding feature, a novel slow-time FDA multiple-input multiple-output (FDA-MIMO) technique is proposed for the space-time adaptive processing (STAP) radar. The new coding scheme can separate transmitting (Tx) signals via slow-time Doppler filtering, and the problem of signal aliasing is tackled by appropriately designing the slow-time codes. At the receiving (Rx) side, two Rx schemes are devised for recovering range-dependent Tx degrees-of-freedom. Moreover, the relevant clutter rank and the rule for code design are derived in detail. The outstanding merits of the proposed slow-time FDA-MIMO STAP radar consist of the following: There is no specific restriction on the probing waveform; the negative effect of time-variant pattern is perfectly eliminated; and both the target localization accuracy and clutter suppression performance are superior over the state-of-the-art FDA radar systems. Numerical results corroborate the superiorities of the proposed waveform strategy for STAP application.

This paper develops a transmit (Tx) waveform de- sign technique for dual-function radar-communication systems that provide both multiple-input multiple-output (MIMO) radar and multi-user multiple-input single-output (MU-MISO) com- munication functionalities. We propose a hybrid linear-nonlinear precoding (HLNP) signaling scheme, in which the dual-use waveform is the superposition of linearly-precoded communica- tion symbols and a nonlinearly-precoded waveform that improves the radar performance. To attain good radar Tx beampattern and waveform ambiguity properties, we focus on optimizing a weighted sum of the integrated main-lobe-to-sidelobe ratio (IMSR) of the Tx beampatttern and a novel angular waveform similarity metric, while ensuring a predefined signal-to-interference-plus- noise ratio (SINR) for each communication user. Practical con- straints are imposed on the Tx waveform, including per-antenna power and peak-to-average-power ratio (PAPR) constraints. We propose an extended feasible point pursuit successive convex ap- proximation (EFPP-SCA) algorithm to solve the resultant non- convex problem and establish its convergence properties. To re- duce the computational cost of designing a long Tx waveform, we further introduce a sub-block design technique. Numerical ex- amples indicate that the proposed HLNP provides a superior performance tradeoff between sensing and communication com- pared to conventional nonlinear precoding.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.