We consider the problem of achieving multi-band inverse synthetic aperture radar (ISAR) fusion imaging of block structure targets with unknown block partition and develop a block-sparse recovering method based on matrix block pattern-coupled sparse Bayesian learning algorithm. Based on the sparse representation of multi-band ISAR fusion imaging model, a pattern-coupled hierarchical Gaussian prior is proposed to characterize the pattern relevance of scattering coefficients. The sparsity of each coefficient is controlled not only by its own hyperparameter, but also by the hyperparameters corresponding to its eight neighboring coefficients in the data matrix. The correlations between the coefficients in rows and columns are determined by different parameters, respectively. The proposed prior model can increase the model flexibility and promote the generation of block structures. Moreover, the whole observation scene is segmented into multiple sub-scenes to reduce the memory storage space and the computational complexity. Parameters and the fusion image result of each sub-scene are derived by the expectation-maximization method. The multi-band ISAR fusion image result of the whole scene is obtained through the stitching of the sub-scenes imaging results. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.

The linear geometry distortion caused by time variant bistatic angles induces the sheared shape of the bistatic inverse synthetic aperture radar (bistatic-ISAR) image. A linear geometry distortion alleviation algorithm for space targets in bistatic-ISAR systems is presented by exploiting prior information. First, we analyze formation mathematics of linear geometry distortions in the Range Doppler (RD) domain. Second, we estimate coefficients of first-order polynomial of bistatic angles by least square error (LSE) method through exploiting the imaging geometry and orbital information of space targets. Third, we compensate the linear spatial-variant terms to restore the linear geometry distortions. Consequently, the restored bistatic-ISAR image with real shape is obtained. Simulated results of the ideal point scatterers dataset and electromagnetic numerical dataset verify the performance of the proposed algorithm.

In the imaging process of bistatic inverse synthetic aperture radar (Bi–ISAR), the echo is easily affected by the internal interference and external environment of the radar system, resulting in the problems of sparse aperture and low echo signal noise ratio. The efficiency of conventional sparse aperture imaging methods is greatly reduced. To solve the above problems, a Bi–ISAR sparse aperture imaging algorithm based on Complex Variational Modal Decomposition (CVMD) and wavelet threshold de–noising is proposed. Firstly, the Bi–ISAR sparse aperture echo signal model is established, the sparse basis matching the echo is constructed, the echo signal is decomposed into different intrinsic mode functions (IMF) by CVMD, and the IMFs belonging to the signal are separated by the energy relationship criterion. Then, after the IMFs is de–noising by the improved wavelet threshold de–noising algorithm, the original signal is synthesized, and the signal after de–noising is reconstructed by using the orthogonal matching pursuing algorithm. Simulation results show that the proposed algorithm can achieve Bi–ISAR sparse aperture high−resolution imaging under low signal noise ratio and has fine anti−noise performance.

The linear‐geometry distortion and image defocus are induced by the time‐varying bistatic angle in a bistatic inverse synthetic aperture radar (Bi‐ISAR) system. Here, a distortion mitigation algorithm of a space target is proposed via exploiting the orbital prior information. First, the linear/quadratic distortions are analysed in the Range‐Doppler (RD) image formation mathematics. The coefficients of the time‐varying bistatic angle are estimated by means of the orbit information of the target and the imaging geometry. Second, the equivalent rotation centre (RC) is estimated iteratively based on the maximum image contrast criterion. Third, the linear‐geometry distortion and quadratic distortion related with the range position are restored by the compensation of the spatial‐varying phase terms. Finally, the well‐focused image is achieved by applying the matching Fourier transform (MFT) along the cross‐range direction. Experimental results based on both the ideal point scatterers model and numerical electromagnetic (EM) data are provided to support the algorithm.

Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging to correct the MTRC where sparse aperture data exists for Bi-ISAR systems. A joint approach of MTRC correction and sparse high-resolution imaging for Bi-ISAR systems is presented in this paper. First, a Bi-ISAR imaging sparse model-related to MTRC is established based on compress sensing (CS). Second, the target image elements and noise are modeled as the complex Laplace prior, and the Gaussian prior, respectively. Finally, the high-resolution, well-focused image is obtained by the full Bayesian inference method, without manual adjustments of unknown parameters. Simulated results verify the effectiveness and robustness of the proposed algorithm.

In practical bistatic inverse synthetic aperture radar (ISAR) imaging systems, the echo signals are modulated by non-ideal amplitude and phase characteristics of the transmitting and receiving channels, which seriously distorts image quality. However, the conventional channel calibration method based on a transponder is not applicable to bistatic ISAR imaging systems, since the baseline of the system is up to hundreds of kilometers. A channel calibration method only using calibration satellite echo information is proposed for the system, with a linear frequency modulation (LFM) waveform. Firstly, echoes of the calibration satellite are collected by tracking the satellite and multi-period echoes are aligned in the time domain, according to the pulse compression result. Then, the signal to noise ratio (SNR) is improved by accumulating multi-period echoes coherently in the time domain and the calibration coefficient is constructed based on the accumulated signal. Finally, spectrum of the echo signal is multiplied with the calibration coefficient to compensate the influence of channel characteristics. The effectiveness of the proposed method is verified by the simulation experiment with real satellite echoes.