To achieve High-Resolution Wide-Swath (HRWS) imaging, operated with variable Pulse Repetition Frequency (PRF), staggered SAR will be applied to the next-generation spaceborne SAR system. Variable PRF will lead to non-uniform azimuth sampling. To demonstrate the effects of non-uniform sampling on the sea surface and moving targets imaging, a frequency-domain simulator is proposed in this paper. First, the echoes in staggered mode with nonuniform sampling in azimuth are generated by the frequency domain simulator. Then, the raw signal is reconstructed and resampled by the Best Linear Unbiased (BLU) interpolation, and focused by SAR imaging methods. The simulation results verify the effectiveness of the simulation process.

Multiple input multiple output synthetic aperture radar (MIMO SAR) can solve the high resolution and wide swath imaging conflicts of traditional SAR. In the time domain, short term shift orthogonal (STSO) waveforms of different transmitting channels were used to separate the echoes from closely spaced scatterers. And in the space domain, digital beam forming (DBF) was used to separate the echoes from widely spaced scatterers. In this paper, a novel phase coding scheme based on Golay complementary sequence is proposed. Based on the idea of STSO waveforms, phase coding scheme could be used to separate and image the echoes of different receiving antennas. Theoretical derivations and simulation results validate the effectiveness of the proposed coding scheme.

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.

As an innovative concept of high-resolution and wide-swath system, the low-oversampled staggered synthetic aperture radar (SAR) can not only deal with the blind ranges, but also suppress range ambiguities and reduce the data volume. Due to the variation of pulse repetition interval (PRI), there will be echo pulse loss and nonuniform sampling in azimuth. Generally, the existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid, and then perform conventional focused processing. However, the accuracy of the resampling is limited, and the advantage of the nonuniformity over uniform sampling in terms of reconstruction performance is ignored, especially with low oversampling factors. In this paper, a reconstruction algorithm for low-oversampled staggered SAR is proposed based on the sparsity Bayesian learning in the presence of a nonlinear PRI variation strategy. To ensure that the blind range distribution, which depends on the PRI variation strategy, brings a superior reconstruction performance, we define a novel objective function and optimize a sequence of nonlinear PRI variation with genetic algorithm. On the basis of the optimized sequence, the proposed reconstruction algorithm performs the second-order keystone transform to achieve range curvature correction for nonuniformly sampled data in azimuth. Then, a nonuniform observation model is established. The sparsity Bayesian learning (SBL) using a hierarchical form of the Laplace prior is applied to reconstruct the focused images directly with the nonuniform sampling. Simulations and experiments on raw data generated in staggered SAR mode with low oversampling factors are performed to verify the effectiveness of the proposed method.

With the cross of Global Navigation Satellite Systems (GNSS) technology and other subject, the application of GNSS has been expanded. This paper focus on the research on the feasibility investigation of target detection with GNSS and a stationary receiver. The system is discussed as a whole and performs link calculation to verify the experimental scene. The experimental results obtained from the GNSS and stationary receiver experiments are presented and analyzed to demonstrate the performance of the algorithm.

Global Navigation Satellite Systems have provided opportunities for passive synthetic aperture radar. This paper presents bistatic synthetic aperture imaging preliminary results with GNSS transmitters and a low-orbit satellite receiver. General shift geometry, improved imaging method and coverage analysis of multi-GNSS and low-orbit satellites are discussed to prove the capability and challenges of the system. The obtained results are given to demonstrate the performance improvement of GNSS-BiSAR imaging.

This paper presents bistatic synthetic aperture radar imaging preliminary results with multiGNSS transmitters and a fixed receiver. Geometry, ambiguity function and imaging algorithms are discussed to describe multi-GNSS BiSAR system. Simulation results are provided to demonstrate the performance improvement of the amount of information of a given scene.