This paper addresses the issue of deceptive jamming against synthetic aperture radar (SAR) by using 1-bit sampling and time-varying threshold (TVT). With 1-bit intercepted SAR signal, the multipliers involved in a convolution is replaced by xnor gates, which considerably simplify the jamming signal generation. Moreover, the TVT is used for 1-bit quantization before retransmission to retain the relative amplitude information of the jamming signal. As a result, the proposed deceptive jamming schemes are superior to their conventional counterpart in terms of realization. Effects of harmonics and oversampling are analyzed to evaluate the performance degradations caused by the 1-bit sampling and TVT. Simulation results are provided to confirm the validity of the proposed schemes.

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.

We propose a new deceptive jamming method using networked receivers to perform deceptive jamming against multi-mode synthetic aperture radar (SAR). Using multiple receivers to measure the time difference of beam arrival (TDOA), we solve the instantaneous modulation parameters precisely via linear equations. We then solve the time delays and frequency offsets of the deceptive signal without approximation to avoid accumulating electronic reconnaissance errors with traditional deceptive methods. Additionally, we design the distribution of receivers to minimize the condition number, reducing our method's sensitivity to measurement error. Simulation results prove the effectiveness of the proposed method

One-bit analog-to-digital converter (ADC), performing signal sampling as an extreme simple comparator, is an overwhelming technology for spectrum sensing due to its low-cost, low-power consumptions and high sampling rate. In this letter, we propose a novel one-bit sensing approach based on the eigenvalue moment ratio (EMR), which has been proved to be highly efficient for conventional multi-antenna spectrum sensing in $\infty $ -bit situation. The statistical covariances of the one-bit samples inherent the statistical correlation properties, thus allow us to detect the existence of primary user (PU) though the test for sphericity. Particularly, we verify the element-by-element independence of one-bit sample covariance matrix (SCM) in the absence of signal, which allows us to asymptotically determine the null distributions of statistical covariance based spectrum sensing techniques. On this basis, we formulate the asymptotic distribution of one-bit EMR under null hypothesis via the central limited theorem (CLT) and perform spectrum sensing with one-bit samples directly. Theoretical and simulation results show the new approach can provide superior sensing performance at a low hardware cost.

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.

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.