One-bit radar, performing signal sampling and quantization by a one-bit ADC, is a promising technology for many civilian applications due to its low-cost and lowpower consumptions. In this paper, problems encountered by one-bit LFMCW radar are studied and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. Firstly, the spectrum of one-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that highorder harmonics may result in false alarms (FAs) and cannot be neglected. Secondly, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are firstly adopted to perform the dimension reduction, and then, the GAMP algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of one-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about 1:3 dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low. In the presence of a strong target, it has about 1:0 dB performance loss.

In coherent direction of arrival (DOA) estimation, subspace-based methods suffer from performance deterioration because of the rank loss of the signal covariance matrix. A variety of spatial smoothing preprocessing techniques have been proposed for decorrelation, among which the enhanced spatial smoothing preprocessing (ESS) technique shows outstanding performance by exploiting the signal subspace. However, ESS is applied by squaring the matrix whose columns span the signal subspace (called signal matrix), which involves unnecessary computational loads. Besides, the smoothed covariance matrix after ESS is a linear combination of the sub-matrices of signal matrix, where the coefficients of the combination may undermine the decorrelation performance. In this context, a simplified spatial smoothing (SSS) technique is proposed for decorrelation by averaging the sub-matrices of the signal matrix directly, and avoids redundant operations in squared signal matrix. The proposed method is tested numerically in terms of the signalto-noise ratio (SNR), the number of snapshots, angle separation, and the execution time. Simulation results show the improvement of the decorrelation performance, efficiency, and robustness with the proposed method in coherent scenarios, compared with the other spatial smoothing preprocessing based methods.

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.

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

Spectrum sharing between radar and communication systems has attracted substantial recent research attention. In order to avoid the mutual interference, source separation of the aliasing signal is a promising solution for joint communication and radar (JCR) sensing. However, most of the existing source separation methods are very sensitive to wide-band signals with rapidly varying instantaneous amplitude (IA) and not applicable to scenarios with complex overlapping subsignals. To overcome the above limitations, a data-driven sparsity-based source separation method is proposed. Without depending on signal parameters, a sparse signal observation model of the received signal is established. Then, source separation is transformed into estimation of the IA and instantaneous frequency (IF), and an iterative method is developed. Moreover, the Cramér-Rao lower bound (CRLB) of the source separation error (SSE) variance is derived based on the CRLB of IA and IF. Simulation results demonstrate that our proposed method is superior to the benchmark schemes and is robust to noise and initial IF errors.

One-bit radar, adopting one-bit analog-to-digital converters to perform signal sampling and quantization, is a promising technology for low-cost and resource-limited applications. In this paper, effects of one-bit quantization on the linear signal processing for the one-bit linear frequency modulated pulse radar are studied and a target reconstruction approach is proposed. Firstly, characteristics of 3-order harmonics of the one-bit signal are analyzed. It is indicated that when conventional linear signal processing methods are carried out, 3-order harmonics may not only raise the detection background but also result in false alarms. Secondly, a two-stage approach jointly performing the linear and nonlinear signal processing is proposed to suppress harmonics and recover true targets. Specifically, in the first stage, the linear signal processing is carried out to suppress unmatched harmonics and dimension reduction. In the second stage, based on a dimension-reduced signal observation model, generalized approximate message passing is utilized to suppress matched harmonics and recover true targets. The effectiveness of the proposed approach is demonstrated by numerical results.