Lunar wide-swath imaging with Earth-based radar is of great significance for lunar scientific research. Conventional Earth-based radars with high frequencies and large-aperture antennas usually operate in the spotlight mode, resulting in a relatively narrow imaging swath. With the increasing demand for large-scale mapping of the lunar surface, it is necessary to develop a kind of wide-swath imaging mode. Hence, this paper proposes a novel imaging mode, namely terrain observation by beam steering (TOBS) mode. The key feature of TOBS mode is to steer the antenna beam at a non-uniform angular speed along the geographical orientation of the scene swath during the data acquisition. The key techniques are: 1) a dynamic beam control method is proposed to achieve uniform azimuth resolution; 2) a variable PRF design method is proposed for effective data acquisition; 3) an improved ground Cartesian back-projection (GCBP) algorithm based on affine geographic coordinate (AGC) system is proposed for efficient TOBS mode imaging. This paper reports the first demonstration of the TOBS mode lunar imaging with an Earth-based radar prototype system. A scene swath of about 750 km has been imaged utilizing the TOBS mode with an azimuth resolution of about 25 m, and the effectiveness of the proposed method is successfully validated.Unlike the traditional "mosaic mode", where multiple separate observations are required to obtain a long swath image leading to reduction in efficiency and enhanced complexity of data processing. The TOBS mode provides a new technical approach for lunar wide-swath imaging utilizing large-aperture and high-frequency Earth-based radars, translating to high-resolution lunar image datasets.

The back-projection (BP) algorithm has been regarded as a robust high-resolution imaging algorithm, particularly suitable for Earth-based radar lunar imaging. To offset phase errors induced by non-ideal effects, autofocus is essential to obtain well-focused lunar images. However, traditional back-projection autofocus algorithms are often computationally burdensome under long synthetic aperture time. In this paper, an effective back-projection autofocus algorithm is developed for Earth-based radar lunar imaging. To ensure that the defocusing caused by phase error lies in the azimuth direction of the BP image, the delay-Doppler coordinate system is introduced into the BP algorithm, and lunar images are generated on the delay-Doppler grid arranged on the lunar surface. Then, the Fourier transform relationship between the BP image and its wavenumber spectrum is established to facilitate the application of phase gradient autofocus (PGA) algorithm to the BP image. To further improve the estimation accuracy of phase error, the spectral characteristics are analyzed in detail. A multi-subband autofocus method based on local scene is proposed to mitigate frequency-dependence of phase error and space-variance of wavenumber spectrum. The processed results of real lunar data validate the effectiveness and efficiency of the proposed algorithm.

A ground-based radar is a potential technique for lunar surface imaging. However, due to the Earth’s rotation, the maximum azimuth resolution is limited for a single observation. To solve this problem, this letter analyzes the feasibility and performance of obtaining multiangle data from different observations and forms large virtual apertures through aperture synthesis. To ensure the quality of synthesized images, an observation baseline selection method is proposed based on the principle of spectrum continuity, specifying that for two noncontinuous observations, there should be a point on each of them whose target-to-radar vectors share the same direction. Besides, an aperture synthesis algorithm based on spectrum compression is proposed to eliminate spectrum aliasing. The effectiveness of the algorithm is verified via computer simulations and real data experiments. By forming a well-focused 500-m resolution image from two noncontinuous observations, the validation of the proposed algorithm has been proved.

Earth-based radar is a pivotal instrument in deep space exploration to obtain radar images of desired celestial bodies. However, the system performance and image resolution of conventional integrated Earth-based radars with only one radar are limited by the power-aperture product and cannot meet the higher demands of deep space exploration. Distributed coherent radar is a new radar system composed of multiple radar units and a central control system, and its system performance can be further improved by increasing the number of radar units. Distributed coherent radar provides a reliable way to build a high-performance and high-resolution Earth-based deep space exploration system. This article introduces several key technologies about distributed coherent radar astronomical imaging: 1) high-precision coherence parameter estimation, which ensures full-coherence performance of the distributed coherent radar; 2) high-precision nonideal effect compensation, which eliminates the image offset and defocusing induced by the nonideal effects; 3) fast factorization backprojection (FFBP) algorithm, which achieves high-resolution fast imaging of celestial bodies. Moreover, based on a distributed coherent radar prototype system composed of four radar units with an antenna aperture of 16 m, high-resolution imaging experiments of the moon are conducted, and the effectiveness of the distributed coherent radar is successfully validated, which could not only provide a reference for the distributed coherent radar system but also provide a reliable solution for detection and imaging of other celestial bodies in the solar system in the future.

Translational motion compensation plays an important role in inverse synthetic aperture radar (ISAR) imaging. However, existing translational motion compensation algorithms cannot work well when the signal-to-noise ratio (SNR) is low and the target has complex motion at the same time, as the algorithms usually assume that these two situations do not occur simultaneously. To address this problem, an improved parametric translational motion compensation algorithm based on signal phase order reduction (SPOR) and minimum entropy is proposed. The key is to decrease the phase order of the signal, which has a nonlinear phase and corresponds to the complex motion, and then obtain the signal with a linear phase corresponding to the noncomplex motion. Subsequently, the signal is transformed into the Doppler domain to generate the SPOR result. Obviously, when the translational motion is well compensated, the SPOR result will be coherently accumulated and has the best quality, which means that the SPOR result has good robustness against the low SNR. Thus, the translational motion is modeled as a polynomial model, the entropy of the SPOR result is taken as the optimizing target, and the relationship between the translational motion compensation parameters and the entropy is established. Finally, coarse search and particle swarm optimization (PSO) are sequentially performed to optimize the entropy of the SPOR result and estimate the translational motion compensation parameters accurately and efficiently. Computer simulation results and experimental results based on unmanned aerial vehicle (UAV) radar validate the proposed algorithm.