Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm.

The nonlinear trajectory and bistatic characteristics of general bistatic synthetic aperture radar (SAR) can cause severe two-dimensional space-variance in the echo signal, and therefore it is difficult to focus the echo signal directly using the traditional frequency-domain imaging algorithm based on the assumption of azimuth translational invariance. At present, the state-of-the-art nonlinear trajectory imaging algorithm is based on singular value decomposition (SVD), which has the problem that SVD may be not controlled, and thus may lead to a high imaging complexity or low imaging accuracy. Therefore, this article proposes a nonlinear trajectory SAR imaging algorithm based on controlled SVD (CSVD). First, the chirp scaling algorithm is used to correct the range space-variance, and then SVD is used to decompose the remaining azimuth space-variant phase, and the first two feature components after SVD are integrated to make them be represented by a new feature component. Finally, the new feature component is used for interpolation to correct the azimuth space-variance. The simulation results show that the proposed CSVD can further improve the image quality compared with SVD-Stolt.

The knowledge of heart and respiratory rates (HRs and RRs) is essential in assessing human body static. This has been associated with many applications, such as survivor rescue in ruins, lie detection, and human emotion detection. Thus, the vital signal extraction from radar echoes after pre-treatments, which have been applied using various methods by many researchers, has exceedingly become a necessary part of its further usage. In this review, we describe the variety of techniques used for vital signal extraction and verify their accuracy and efficiency. Emerging approaches such as wavelet analysis and mode decomposition offer great opportunities to measure vital signals. These developments would promote advancements in industries such as medical and social security by replacing the current electrocardiograms (ECGs), emotion detection for survivor status assessment, polygraphs, etc.

Squint ultrahigh-resolution (UHR) synthetic aperture radar (SAR) generally uses the extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However, the extra range migration and defocusing (ERMD) will be introduced by the EOK when there are motion errors. This problem will be severe as the squint angle increases, which may sharply reduce the signal-to-noise ratio (SNR) and lead to the failure of the existing autofocus methods. In this letter, a UHR autofocus algorithm based on cascaded map-drift (MD)-PGA is proposed. The MD is used for the rough estimation to improve the SNR and the phase gradient algorithm (PGA) is used to accurately estimate the residual error based on a relatively high SNR. The new algorithm combines the advantages of MD and PGA, which can solve the problem of severe defocusing. The processing of airborne real data validates the effectiveness of the proposed algorithm.

Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

As a non-contact life detecting equipment of smart campus, the ultra-wide band (UWB) radar life detector is widely used in finding victims of post-disaster search and rescue. However, due to the complex detection environment and the weak life characteristics of victims, the detecting time consuming by current detecting methods is long meanwhile the sensitivity is low, as a result, the process of search and rescue is slow and the probability of missed detection is high. To solve this problem, three works have been done in this research: first, a mathematical model of the echo of UWB radar is established; second, a concept, the critical speed of fast and slow objects, is put forward as a reference value of signal processing; and third, a new accelerated algorithm of which utilizes block data analysis to detect micro-moving objects is proposed in this paper. It has been verified both by simulation and experiment that the new algorithm can improve the detecting sensitivity and the speed of detection.