Due to the limited resources available on small synthetic aperture radar (SAR) platforms, such as unmanned aerial vehicles (UAVs), one-bit SAR imaging has emerged as a promising technique, particularly with the rapid development of low-altitude economy. One-bit SAR is capable of leveraging a very simple and cheap one-bit analog-to-digital converter (ADC) to complete the same sensing task as those in the conventional SAR using high-resolution ADC. Nevertheless, the one-bit quantization incurs some intractable problems, such as signal amplitude distortion and the emergence of unwanted interference, which seriously degrade the SAR image quality. To tackle these problems, this work proposes a one-bit SAR imaging strategy that devises a quantization threshold fixed in fast-time but fluctuating in slow-time. Specifically, within the one-bit quantization procedure for each echo pulse, the threshold is fixed, significantly simplifying the SAR system. On the other hand, the slow-time fluctuation of the threshold enables the SAR to reassign spectrum energies, effectively suppressing unwanted interference. The frequency of the threshold and the corresponding pulse repetition frequency (PRF) of the SAR system are elaborately designed. In addition, the fluctuation range of the threshold, which determines the fidelity of SAR images directly, is designed as well, and a closed-form expression for the threshold fluctuation range is determined. The effectiveness of the proposed scheme is demonstrated through the simulated and real-data experiments, confirming that high-quality one-bit SAR images can be achieved.

In remote sensing research, narrowband interference (NBI) suppression in synthetic aperture radar (SAR) is an urgent problem. Recently, many methods on NBI suppression are proposed via sparse recovery. In these methods, the optimal regularization constants are always hard to choose. Moreover, the number of NBI signals, equaling to the sparsity of the sparse vector, may change at different pulses, and the suppression performance might be reduced due to the regularization constants which control the sparsity of the sparse vector, are fixed. In this letter, aiming at these problems, a NBI suppression method for SAR based on sparse segmentation search (SSS) is proposed. Firstly, we build a non-convex optimization model without the regularization constant. Then the adaptive linear enhancer (ALE) is used to convert the non-convex model to a convex one. Finally, we solve this convex model and suppress NBI signals. The real-world SAR data experiments illustrate the effectiveness of the proposed method.

Deceptive jamming suppression in synthetic aperture radar (SAR) is an urgent problem. In this letter, an auxiliary-channel slow-time coding and change detection based deceptive suppression method is proposed. By coding pulses from the auxiliary channel, difference between two images, separately from the auxiliary and main channel, is appeared. Then, the change detection technology is utilized to reveal this difference as well as to form filters and to suppress deceptive jammings. Compared to some existing coding methods, in this method, deceptive jammings can be suppressed more thoroughly and a higher quality SAR image can be achieved. Results of false scene based simulation experiments demonstrate the effectiveness of the proposed method.