In spaceborne inverse synthetic aperture radar (ISAR) imaging of space targets, the high-speed motion of the radar and target can make the intrapulse motion not negligible. It can cause the range dimension matched filtering error, resulting in image defocusing. In traditional ISAR imaging scenarios, existing analysis and methods only focus on the high-speed motion of the target, without considering the radar, which is not suitable for spaceborne ISAR imaging of space targets. In this letter, the intrapulse motion of spaceborne ISAR imaging of space targets is analyzed in detail, and the corresponding compensation method based on modified phase difference (MPD) is proposed. First, the specific expression of the signal’s time delay considering the intrapulse motion is derived. Then, the range-dimension matched filtering error is analyzed, and the condition of ignoring the intrapulse motion is also obtained. This condition can be used to judge whether the intrapulse motion can be ignored in practical processing. According to the error analysis, an intrapulse motion compensation method based on MPD is proposed. This method does not need to estimate the motion parameters of the radar and target. The coefficient used to construct the compensation term can be directly estimated from the echo signal. Finally, the corresponding spaceborne ISAR imaging method with intrapulse motion compensation for space targets is proposed. Isolated scatterer and satellite electromagnetic calculation data experiments using the orbital data verify the effectiveness of the proposed method.

Compared with ground-based radar, spaceborne inverse synthetic aperture radar (ISAR) has advantages in space situational awareness. Since the motion of the target relative to the radar is complex and nonuniform, high-order spatial variant phase (SVP) can be induced, resulting in a defocused image. Due to the unknown spatial distribution of scatterers, the correction of high-order SVP is a hard task. In this letter, a novel SBISAR imaging method for space targets based on high-order SVP correction (HOSVPC) is proposed. First, the geometric model for SBISAR imaging of space targets is established, and the slant range model is derived. The specific form of the high-order SVP is derived, which is modeled as the sum of the range term and the velocity term. Second, to correct the high-order SVP, an orbit parameter estimation method and the HOSVPC algorithm are proposed. The range term is compensated first using the orbit parameters, the velocity term is then corrected using non-uniform resampling, and the first-order term of the range term is finally compensated back. Finally, a well-focused ISAR image is obtained through cross-range compression. Scatterer simulation and electromagnetic simulation results demonstrate the effectiveness of the proposed algorithm.

For air maneuvering targets, the higher-order components in the translational motion can induce severe range shifts and phase errors. They can deteriorate the performance of translational motion compensation (TMC) in the inverse synthetic aperture radar (ISAR) imaging process. Additionally, for weak targets, low signal-to-noise ratio (SNR) can also severely affect the accuracy of TMC. In this letter, a novel TMC method for ISAR imaging of air maneuvering weak targets based on CV-GRUNet is proposed. First, leveraging the noise robustness and capability to handle time series of the Gated Recurrent Unit (GRU), it is extended to the complex domain to construct CV-GRU. CV-GRU can effectively extract the features of range shifts and higher-order phase errors. Subsequently, CV-GRUNet is constructed to obtain the compensation function. Finally, TMC can be achieved using the compensation function, resulting in well-focused ISAR images. Compared with existing methods, the proposed method can achieve accurate TMC for ISAR imaging of maneuvering weak targets. The compensation accuracy of the simulated target has reached 1/20 of the wavelength. Simulated and real data results verify the effectiveness and superiority of the proposed method.

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne ISAR imaging of space target, even during a short coherent processing interval (such as 1s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time-frequency analysis is proposed. First, the differences in optimal imaging instants (OII) of scatterers are analyzed. Then, based on the combination of the Margenau-Hill-Spectrogram (MHS) and the S-method, the time-frequency curves can be obtained after extracting the time-frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results.

For inverse synthetic aperture radar (ISAR) imaging of the maneuvering target, when the time-varying rotational acceleration arises, the image will become blurred because of high-order phase terms. In this letter, the received signal in a range bin is modeled as the multicomponent high-order polynomial phase signal (PPS). To eliminate high-order phase terms, a novel ISAR imaging algorithm based on the cubic phase function and changing sampling rate (CPF-CSR) is proposed in this letter. First, we regard the high-order PPS as a piecewise cubic phase signal (CPS), and thus the cubic phase function (CPF) algorithm can estimate the instantaneous frequency rate (IFR). Next, to suppress cross-terms for the multicomponent PPS, an improved algorithm based on the CPF is proposed, which searches the range bin composed of multiple strong point scatterers and estimates the IFR of the dominant one. Finally, the changing sampling rate (CSR) algorithm is proposed to eliminate high-order phase terms by adjusting the instantaneous frequency of the signal, and no additional estimation for the IFR of other components is required. After the cross-range compression, a well-focused ISAR image can be obtained. The experimental results of the simulated data and real data are given to validate the effectiveness of the CPF-CSR algorithm.