As the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of an SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR’s antenna, causing radiometric artifacts in the focused image. This article tries to model and characterize the artifacts and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artifact, which reveals that the mutual interference can introduce a 2-D LFM radiometric artifact in image domain with a limited spatial extent. We show that the artifact is low-rank based on a range–azimuth decoupling analysis and 2-D high-order Taylor expansion. Based on the low-rank model, we show that two methods, i.e., principal component analysis and its robust variant, can be adopted to efficiently mitigate the artifact via processing in the image domain. The former method has the advantage of fast processing speed, for example, a subswath of Sentinel-1 interferometric wide swath image can be processed within 70 s via blockwise processing, whereas the latter provides improved accuracy for sparse pointlike scatterers. Experiment results demonstrate that the radiometric artifacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.

We consider a multiple input multiple output (MIMO) radar that works through one-bit sampling of received radar echoes. The application of one-bit sampling significantly reduces the hardware cost, energy consumption, and systematic complexity, but it also poses serious challenges to extracting highly accurate target information from one-bit quantized data. In this paper, we propose a maximum likelihood (ML)-based method that first iteratively maximizes the likelihood function to recover a virtual array data matrix, then jointly estimates the angle and Doppler parameters from the recovered matrix. Because the ML problem is convex, we can successfully apply a computationally efficient gradient descent algorithm to solve it. Based on our analysis of the Cramér-Rao bound (CRB) of the ML-based method, a pre-estimation assisted threshold (PET) strategy is developed to improve the estimation performance. Numerical experiments demonstrate that the proposed ML-based method, combined with the PET strategy, can provide highly accurate parameter estimation performance, close to that of classic MIMO radar.

Quadrature sampling has been widely applied in coherent radar systems to extract in-phase and quadrature ( I and Q) components in the received radar signal. However, the sampling is inefficient because the received signal contains only a small number of significant target signals. This paper incorporates the compressive sampling (CS) theory into the design of the quadrature sampling system, and develops a quadrature compressive sampling (QuadCS) system to acquire the I and Q components with low sampling rate. The QuadCS system first randomly projects the received signal into a compressive bandpass signal and then utilizes the quadrature sampling to output compressive I and Q components. The compressive outputs are used to reconstruct the I and Q components. To understand the system performance, we establish the frequency domain representation of the QuadCS system. With the waveform-matched dictionary, we prove that the QuadCS system satisfies the restricted isometry property with overwhelming probability. For K target signals in the observation interval T, simulations show that the QuadCS requires just O(Klog(BT/K)) samples to stably reconstruct the signal, where B is the signal bandwidth. The reconstructed signal-to-noise ratio decreases by 3 dB for every octave increase in the target number K and increases by 3 dB for every octave increase in the compressive bandwidth. Theoretical analyses and simulations verify that the proposed QuadCS is a valid system to acquire the I and Q components in the received radar signals.

Quadrature compressive sampling (QuadCS) is a recently introduced sub-Nyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulse-Doppler radars, the QuadCS outputs can be arranged into a two-dimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the sub-Nyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false targets are then removed in the range estimation stage by exploiting the inherent target detection capability of the recovery algorithms. Simulation results verify the effectiveness of the proposed CoSaPD scheme, which requires only one-eighth of the Nyquist rate to achieve similar performance to the classical processing with Nyquist samples, provided that the input signal-to-noise ratio (SNR) is above -25 dB.

Synthetic aperture radar (SAR) is an important imaging tool in many applications. Its imaging quality can be easily degraded by radio-frequency interferences (RFIs), among which the narrowband ones are typical. In recent years, it is shown that the narrowband RFI has a low-rank property and this property can be combined with the sparsity of radar echoes' to develop efficient RFI-suppression algorithms. However, these works usually consider the case of sparse echoes in the impulse-based radar, which is not suitable for typical SAR systems that use a chirp signal with a relatively long pulse duration. Some works adopt a large 2-D dictionary to introduce sparse representation for the echoes, which nevertheless lack efficient numerical algorithms, because the large dictionary brings high storage cost and computational burden. Motivated by these problems, this letter introduces an operator modeling approach for the echo dictionary and proposes a dictionary-based SAR RFI-suppression method under the framework of robust principle component analysis (RPCA). In the proposed method, the useful echo is sparsely represented by a dictionary, and the dictionary's analysis and synthesis operators are modeled as two sequences of low-cost operations by exploiting the chirp scaling algorithm. Then, an efficient algorithm is derived for solving the dictionary-based RPCA problem. Numerical simulations show that the proposed method is robust and efficient for SAR narrowband RFI suppression.