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.

This work is aimed at the requirements of high real-time moving target detection and localization for near-field Synthetic Aperture Radar (SAR). It addresses the challenges posed by strong spatial variation in imaging and severe coupling of Doppler parameters between the platform and the target. This work proposes a near-field Displaced Phase Center Antenna (DPCA) for clutter suppression based on instantaneous range-Doppler imaging algorithms, as well as joint dynamic target parameter estimation method using multiple channels and moments configuration. Simulation results verifies the effectiveness of the proposed approach.

The cross-range scaling technique plays a crucial role in inverse synthetic aperture radar (ISAR) imaging. It enables the determination of the absolute size of the target by transforming the ISAR image from the range-Doppler domain to the homogeneous range cross-range domain. This work devises an accurate cross-range scaling approach based on the one-bit data extracted from ISAR echoes. The proposed approach provides a novel strategy to improve the accuracy of the phase parameter estimation for cross-range scaling. It amplifies the phase of the echo via extracting one-bit data. By leveraging the amplified feature carried by the generated harmonics, the proposed approach can achieve higher accuracy while maintaining the search interval. Additionally, the proposal exhibits low computational burden and is easy to implement. The effectiveness is validated through experiments conducted on simulated and real measured data.

One-bit Synthetic Aperture Radar (SAR) imaging, which collects echoes into one-bit quantized samples, is a highly promising technique for the faster SAR images acquisition and simplified implementations in numerous applications. Harmonics, accompanied with one-bit sampling operation, are one of the fundamental factors that trigger severe aliasing artifacts problem in frequency domain. For one-bit SAR imaging, rather than sampling the echoes with extremely high rate to alleviate the effects of harmonics, a Convolutional De-Quantization Network, termed as CDQOB-net, is proposed to establish de-aliasing in a transformed imagery domain. The proposed method starts with a fresh interpretation on the regular range-Doppler imaging, which is also referred to as the 2-dimensional (2D) transformed domain of SAR echo. Then, in the light of deep learning framework for image restoration, an alternative method designed in the perspective of harmonics is specifically analyzed, wherein the degradation caused by harmonics in transformed domain are sufficiently estimated. Consequently, as a data-driven method, our two-stage CDQOB-net is proposed and trained to handle aliasing harmonics and multiple distortions, which can significantly improve the quality of one-bit SAR imaging. The proposed method is easy to implement and computationally efficient. Extensive experiments on simulated and real SAR data also demonstrate its validation and effectiveness.

The high fidelity and fraudulence of deceptive jamming can severely mislead synthetic aperture radar (SAR), which poses a big challenge in SAR imaging. In this letter, an imagery domain target reconstruction approach is proposed to suppress deceptive jamming for single-channel SAR. Specifically, the distinction between the echo signals of true targets and deceptive jamming in the azimuth phase is ascertained first. Then, the imaging process encountering deceptive jamming is formulated as a linear model, and the target reconstruction problem is converted to a linear inverse problem with the dictionary containing the explored phase characteristics. Finally, considering the sparsity of man-made targets, the imageries of the true and false targets can be reconstructed simultaneously through solving this sparse signal recovery problem. Theoretical analysis and experimental results showcase the superiority of the proposed method.

This paper presents a 1-bit quantization based on frequency shifted dechirping echo for vehicle-mounted synthetic aperture radar (SAR). By processing the dechirp echo with predesigned frequency shift and setting a proper sampling rate, the high-order harmonics caused by 1-bit quantization are effectively separated from the original echo. In this way, the 1-bit echoes can be range-compressed by FFT, and then a back projection (BP) algorithm can be used for imaging. The scheme is applied to image the real-measured data obtained a vehicle-mounted SAR and the effectiveness of the method is verified.

It has been proven that the imaging quality of 1-bit quantized synthetic aperture radar (SAR) data can be improved by using an imaging scheme of a single-frequency threshold (SFT). In this article, such a quantization model is specialized to a fixed threshold application by setting the frequency of the threshold to zero. Using multiple fixed thresholds, the conventional quantization schemes are also assimilated into this model. In this way, the performance degradation caused by low-precision quantization is analyzed in terms of harmonics, and the SFT quantization (SFTQ) strategy is addressed to handle the issue of performance degradation. The proposed quantization scheme provides significant imaging quality improvements, especially when the quantization level is low, and its advantage in computational complexity is also analyzed. Simulations on different SAR scenes show that the SFTQ, compared with the conventional quantizers, is able to guarantee 97% information in the SAR imagery while consuming less than 1/6 hardware for pulse compression. Therefore, the proposed low-precision SFTQ is a promising approach to SAR system miniaturization.

In this study, the authors address antenna selection (AS)‐aided massive multi‐user multiple‐input‐multiple‐output (MU‐MIMO) system based on maximum signal‐to‐noise ratio, where imperfect channel state information (CSI), time‐varying channel and antenna spatial correlation are considered. More explicitly, a computationally simple training‐based channel estimator is firstly employed for obtaining the imperfect down‐link CSI. Channel quantisation (CQ) is subsequently introduced by the feedback link which is used for feeding back so‐obtained CSI to base station. Time varying coefficient is utilised to characterise channel time variety and antenna spatial correlation is modelled by Kronecker model. Furthermore, the authors derive closed‐form ergodic channel capacity of the AS‐aided MU‐MIMO system with channel estimation error, CQ error and delay feedback, which has not yet been addressed in the state‐of‐the‐art approaches. Simulation results are provided to demonstrate the tightness of their theoretical computations of the ergodic channel capacity.

This paper proposes a novel 1-bit SAR imaging scheme with the assistance of a single-frequency threshold. Such a threshold is able to maintain the amplitude information lost in the 1-bit quantization. Moreover, the influence of high order harmonics is also suppressed due to the spectrum shifting effect brought by the threshold and its high order harmonics. The 1-bit SAR imaging quality is thus improved while the system simplification brought by 1-bit sampling, which is gained by replacing a conventional multiplier with a logic gate, can still be retained. Strategy for SAR parameter selection is discussed to guarantee the performance. Experiment based on the RADARSAT-2 data verifies the validity of the proposed scheme.

This paper addresses a novel SAR imaging scheme based on 1-bit sampling assisted with a single-frequency threshold. The 1-bit sampling approach is able to considerably reduce the quantization cost. However, when the sampling technique simplifies the SAR system by reducing the word length of each sample to only 1 bit, amplitude information of the SAR echo is lost and high-order harmonics are introduced, degrading the SAR imaging quality. The strategy of single-frequency threshold is able to linearly maintain the amplitude information and shifts the spectra of the harmonics away from the imaging component caused by the intermodulation. Hence, the imaging quality using 1-bit sampled data can be guaranteed. By selecting different SAR parameter groups according to a comprehensive consideration on spectrum aliasing, filter mismatching, and radio frequency interfering, a good tradeoff can be achieved between imaging quality and system simplification. Examples of ideal scatterers and a real measured scene are provided for quantitative analysis. Real measured RADARSAT-1 data are also imaged using the proposed scheme to validate its effectiveness.

Terrain observation by progressive scans synthetic aperture radar (TOPSAR) is a novel two dimensional imaging mode with wide swath coverage. However, it will cause problems when our regions of interest are being detected by hostile TOPSAR. To prevent this threaten from TOPSAR, this paper presents a corresponding deceptive jamming method by taken the effect of changing relative position between jammer and TOPSAR platform into consideration. Meanwhile, our approach decomposes the jamming problem against TOPSAR into simpler subproblems of the same type. Therefore, a parallel processing scheme could be carried out to implement effectively jamming against TOPSAR.

This paper considers the problem of reconstructing true targets in a single-channel synthetic aperture radar (SAR) imaging system, which has been disturbed by deceptive jammings. Since the deceptive jammings are usually confined to the main lobe of an SAR antenna, their time-frequency distributions are different from those of the true echoes. This enables us to utilize a dynamic synthetic aperture (DSA) scheme to extract the characteristics of the true and false targets. Dictionaries about the true and false targets are constructed by taking interactions between scatterers into account. Then a sparsity-driven optimization problem is solved to reconstruct the true and false targets separately with super-resolution. Moreover, the deceptively jammed SAR data are divided into different areas to handle various scenarios efficiently, and strategies for DSA selection are addressed as well. Simulations are provided to verify the effectiveness of the proposed algorithm.

Based on the synthetic aperture radar (SAR) geometric model, a novel, fast algorithm of large scene deceptive jamming against the space-borne SAR is proposed. First, we divide the jamming scene template into sub-templates according to the depth of focus in the range dimension. Next, each sub-template is decomposed into the slow-time-dependent and slow-time-independent terms in the range frequency-azimuth time domain. The slow-time-independent terms are generated off-line while the slow-time-dependent terms are generated by real-time 1-D frequency modulation. Then, the sub-templates are convolved with the intercepted SAR signals simultaneously. Finally, fast deceptive jamming is achieved by incorporating all the sub-templates together. In the proposed method, the two-step realization of the sub-templates and the parallel sub-block processing improves the algorithm efficiency. The simulation results prove the validity of the proposed algorithm.