The innovative bistatic synthetic aperture radar (BiSAR) mission LuTan-1 (LT-1) employs a non-interrupted synchronization scheme to achieve high-precision phase synchronization. While radio frequency interference (RFI) is a major factor in deteriorating phase synchronization performance. To present the effect of RFI on the synchronization phase clearly, the characteristics of RFI in the synchronization link are described in detail, and a precise analytic expression between the amplitude, frequency, and phase of RFI and synchronization phase error is established. Furthermore, a novel pulse compression-based notch (PCN) method is proposed to eliminate the phase error introduced by RFI. In the proposed method, the saturated distortion signals resulting from strong interferences are detected and discarded by the distribution features of their modes. Then, inspired by contrary thinking, the synchronization signal after pulse compression is notched instead of the RFI. A fast missing-data iterative adaptive approach (Fast MIAA) is performed to recover the gaped RFI signal and remove it from the compressed signal. Finally, the correct synchronization phases can be extracted from the peak positions of the remaining signals. Experimental results derived from using simulated and real synchronization data of the LT-1 system validate the performance of the proposed RFI mitigation method.

The challenging problem to realize high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging is the ambiguity suppression in the azimuth and range directions. According to the spatial angle difference of each ambiguity component, the current technical approach is to design the spatial filter for achieving the ambiguity suppression based on the 2-D multichannel system. Along with the increasing of HRWS imaging requirements, the number of system channels also gradually increase and further result in the complex structure design of the phased array antenna system. Meanwhile, the traditional phased array antenna cannot effectively control the direction of the transmit beampattern in range. Unlike the traditional phased array, frequency diverse array (FDA) employs a small-frequency increment across the whole array elements and forms the range-angle-dependent S-shaped transmit beampattern, which can be utilized to separate the different range ambiguous region. Considering the above-mentioned characteristics and the range periodicity problem of transmit beampattern, this letter devises a scheme for spaceborne SAR HRWS imaging mode in the view of transmit beampattern utilizing 2-D planar array, i.e., the FDA in azimuth for removing the range nonperiodicity ambiguity and the conventional phased array in elevation for removing the range periodicity ambiguity. Simulation results have been presented to validate the effectiveness of the proposed scheme.

Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm has been proposed to improve the performance. However, CFBP is ineffective in the highly squint case because its range model (RM) is imprecise. In this letter, we propose a modified CFBP (MCFBP) algorithm for highly squint spotlight SAR imaging. We employ a transformed Cartesian coordinate system to treat the transceiver mode as approximately side-looking mode. According to the transformed coordinate system, we propose a modified RM (MRM) to reduce the range error. Based on the MRM, we derive modified image spectrum compression steps and the Nyquist sampling rate of the subaperture image. MCFBP inherits CFBP's accuracy and efficiency advantages. Results of simulations and experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the improved performance of the proposed algorithm relative to BP and fast factorized BP.

The bistatic synthetic aperture radar (BiSAR) plays an important role in remote sensing. However, the deviation between the two oscillators in BiSAR systems will cause a residual modulation of the echo signal. Therefore, the phase synchronization is an important issue that must be addressed in the BiSAR system. An advanced noninterrupted phase synchronization scheme is used for LuTan-1. The synchronization pulses are exchanged immediately after the ending time of the radar echo receiving window and before the starting time of the next pulse repetition interval, which will not interrupt the normal SAR operation. In order to evaluate the accuracy of the phase synchronization scheme, the model of phase synchronization is introduced at first. The hardware design and processing flow of LuTan-1 are introduced in detail. An innovative internal calibration strategy is also described. Then, the test data acquired by the ground validation system are analyzed to verify the effectiveness of the phase synchronization scheme. The signal-to-noise ratio (SNR) and the synchronization rate are the two most important factors to influence the accuracy in phase synchronization. The conclusions have guiding significance for the synchronization module design of LuTan-1 and the future BiSAR system.

Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.