Long-horizon composite task planning for multi-robot systems in cross-regional complex scenarios faces dual challenges: spatial-semantic comprehension of natural language described tasks and collaborative optimization of subtask al-location. To address these challenges, this paper proposes a progressive three-stage task planning framework. First, an augmented scene graph is constructed to enable large language models (LLMs) to comprehend environmental structures, thereby generating simplified Linear Temporal Logic (LTL) task sequences. Subsequently, a novel heuristic function is employed to select optimal task allocation plans. Finally, LLMs are used to generate low-level executable robot instructions based on robotic system instruction templates. We establish a long-horizon composite task dataset for experimental validation on real-world quadrupedal multi-robot systems. Experimental results demonstrate the effectiveness of our approach in resolving cross-regional composite tasks.

Time synchronization and phase synchronization are indispensable for spaceborne bi-/multi-static SAR systems. The LuTan-1 (LT-1) bistatic SAR system employs a time synchronization scheme combining pulse-per-second (PPS) signals with GNSS-disciplined oscillators (GDOs), along with a novel non-interrupted alternate pulse (NIAP) scheme for phase synchronization. Furthermore, the time synchronization accuracy can be further enhanced by applying the two-way time transfer (TWTT) technique based on synchronization data from the NIAP scheme. To accurately evaluate the synchronization performance of the LT-1 system during in-orbit operation, an assessment method based on the two-dimensional (2D) positional offset of calibration targets is proposed. The method compares the theoretical offsets derived from imaging geometry with the measured offsets extracted from monostatic and bistatic SAR images to determine the system’s time and frequency deviations. Using 16 repeat-pass acquisitions over the calibration field in Hami, Xinjiang, during LT-1’s bistatic operation phase, the in-orbit synchronization performance of LT-1 is demonstrated. The PPS+GDO scheme achieves time synchronization accuracy under 100 ns, while the TWTT algorithm enhances this metric by three orders of magnitude, which is better than 0.10 ns. After NIAP phase synchronization, the LT-1 system exhibits a frequency deviation of approximately −2.17 × 10−3Hz, corresponding to frequency stability of 2 × 10−12.

LuTan-1 is the first spaceborne L-band bistatic Synthetic Aperture Radar(BiSAR) constellation in China aimed at highly accurate digital elevation model(DEM) and surface deformation monitoring. During the in-orbit commissioning phase, the constellation performed periodic observations over the Hami region in Xinjiang, China, while flying in a controlled helix formation. This paper preliminarily elucidates the capability and the stability of Raw DEM generation utilizing LuTan-1 BiSAR interferometric images. Interferometric processing procedures, including initial baseline estimation and absolute phase estimation, were implemented without the use of external ground control points(GCPs). In comparison with GCPs, the results showed that the mean error and the root mean square error (RMSE) of the eight Raw DEMs exhibit close alignment, accompanied by a small standard deviation, validating the excellent stability and capability of LuTan-1 Bistatic SAR.

The azimuth multichannel synthetic aperture radar (SAR), as one of the mainstream technologies for achieving high-resolution and wide-swath (HRWS) imaging, has been successfully employed in several on-orbit SAR missions. However, the unavoidable phase errors among channels result in azimuth ambiguity, deteriorating the recognizability of targets in SAR images. Additionally, the radio frequency interference (RFI) exacerbates the difficulty of phase error estimation. To address this issue, a phase error calibration method based on least spectrum difference (LSD) is proposed. Firstly, the multichannel signals are reconstructed using the linear mapping form of the reconstruction algorithm. Secondly, the objective function is established based on the continuity of the azimuth spectrum, by which only the signals near the discontinuity points are proposed. Finally, the optimal estimation of the phase differences can be obtained after iteration. In LSD method, the RFI to the objective function is mitigated due to the operation in the range-Doppler domain, and the iteration is proposed only using a few data near the discontinuity points thus saving much computational cost. Experimental results based on the simulated data and real bistatic echoes of the LuTan-1 (LT-1) mission validate the superiority of the proposed LSD method.

Baseline calibration is a fundamental procedure in generating DEM with InSAR data. However, conventional calibration algorithms heavily rely on the outcomes of phase unwrapping, which exhibit strong interdependencies with the baseline calibration results. The phase unwrapping process introduces considerations such as integer ambiguity, disparities between transmit and receive path delay, and potential errors stemming from data processing. To circumvent the inherent challenges associated with phase unwrapping errors, this paper proposes a novel calibration approach based on stereo-radargrammetry. The efficacy of the proposed method is affirmed through experimentation conducted on authentic LuTan-1 SAR images.

Phase synchronization plays a crucial role in the LuTan-1 (LT-1) bistatic synthetic aperture radar (BiSAR) system, as it aims to eliminate additional azimuthal phase modulation caused by oscillator differences. However, for the pulse alternating transmission system operating in the L-band, the presence of radio frequency interference (RFI) poses an inevitable challenge. Serious RFIs introduce phase jitters, compromising the accuracy of synchronization. In this letter, an effective method for detecting and removing phase jitters is proposed to enhance synchronization accuracy. In the proposed method, the jitter features are separated by the iteratively reweighted least squares (IRLS) in the instantaneous frequency domain based on the established synchronization phase model. Then the jitter positions are detected by correlated peaks between the designed convolutional kernels and jitters. Finally, a polynomial model is utilized to remove jitters and assist in phase unwrapping. The synchronization phases acquired by the LT-1 mission are used to verify the feasibility of the proposed algorithm. The improved imaging quality demonstrates the effectiveness of the proposed method and confirms its ability to ensure the high-precision generation of the LT-1 BiSAR images.

Airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) has drawn increasing attention in wide-area surveillance, reconnaissance, and traffic monitoring due to its short revisit times. In this article, a novel scheme for CSSAR-GMTI systems is proposed to offer high-resolution focusing of moving targets and enable efficient detection and accurate parameter estimation, which are ensured by integrating moving target imaging with a space–time adaptive processing (STAP) clutter suppression step. The presented scheme develops a target imaging algorithm that forms SAR images focused on a particular 2-D motion parameter to maximize target signal energy and recover its high resolution. Moreover, benefiting from the suitable phase compensation designed in the 2-D frequency domain, moving targets can finally be properly focused in the SAR image without displacement, and their parameters can be uniquely determined. Experiments on an emulated radar dataset are conducted to validate the effectiveness of the proposed scheme.

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

There are shadows existing in traditional SAR tomography (TomoSAR) 3D imaging results, which bring difficulties in application of TomoSAR. However, TomoSAR with multi-azimuth observations can be applied to address this problem. In this paper, a panoramic 3D reconstruction method for TomoSAR based on multi-azimuth observations will be introduced. Firstly, 2D images are achieved with backprojection (BP) algorithm on the ground plane. Secondly, 3D reconstruction of TomoSAR is realized with orthogonal matching pursuit (OMP). After coordinate transformation, the panoramic 3D reconstruction results of TomoSAR based on multi-azimuth observations are achieved with registration of point clouds, in which iterative closest point algorithm (ICP) is effectively applied. Panoramic 3D reconstruction results of airborne TomoSAR experimental data can validate correctness and effectiveness of our method.