Search Results (7)

To satisfy the requirement of the modern spaceborne synthetic aperture radar (SAR) system, SAR imaging mode design makes a trade-off between resolution and swath coverage by controlling radar antenna sweeping. Existing spaceborne SAR systems can perform earth observation missions well in various modes, but they still face challenges in data acquisition, storage, and transmission, especially for high-resolution wide-swath imaging. In the past few years, sparse signal processing technology has been introduced into SAR to try to solve these problems. In addition, sparse SAR imaging shows huge potential to improve system performance, such as offering wider swath coverage and higher recovered image quality. In this paper, the design scheme of spaceborne sparse SAR imaging modes is systematically introduced. In the mode design, we first design the beam positions of the sparse mode based on the corresponding traditional mode. Then, the essential parameters are calculated for system performance analysis based on radar equations. Finally, a sparse SAR imaging method based on mixed-norm regularization is introduced to obtain a high-quality image of the considered scene from the data collected by the designed sparse modes. Compared with the traditional mode, the designed sparse mode only requires us to obtain a wider swath coverage by reducing the pulse repetition rate (PRF), without changing the existing on-board system hardware. At the same time, the reduction in PRF can significantly reduce the system data rate. The problem of the azimuth ambiguity signal ratio (AASR) increasing from antenna beam scanning can be effectively solved by using the mixed-norm regularization-based sparse SAR imaging method. Full article

The ambiguity suppression is a technical challenge for the present generation of spaceborne synthetic aperture radar (SAR) systems since this kind of suppression does not take the high spatial resolution and wide coverage into account simultaneously. The transmitting scheme based on the waveform diversity technique is a promising candidate for the conventional (one transmit, one receive channel) SAR systems and has been widely discussed, because it has almost no extra system costs and the ambiguity suppression performance is not closely related to pulse repetition frequency (PRF). However, the accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is still a gap. To this end, starting from the precise signal model formulated in this paper, the ambiguity evaluation for spaceborne SAR with waveform diversity has been analyzed in detail. Particularly, the modified azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) formulas are given for the single polarization SARs and quadrature-polarimetric (quad-pol) SARs, which contributes a lot, for the system designer, to precisely evaluating the ambiguity performance. Finally, detailed simulation experiments exploiting the system parameters of the LuTan (LT-1) system are carried out to corroborate the theoretical developments. Full article

Geosynchronous synthetic aperture radar (Geo-SAR) with a short revisit time can obtain wide-area images. This paper advances a new two-dimensional pitch and roll squint controlling (2D-PRSC) method that can make satellites continuously stare at any scene in the whole orbital period. The maximum attitude steering angle is less than ±7.6 degrees, and the attitude controlling time can be greatly shortened compared with the yaw steering method. Furthermore, a Geo-SAR staring mode model is illustrated and compared with that of low earth orbital SAR (Leo-SAR). Finally, Geo-SAR’s ambiguity property is discussed. The simulation results illuminate that the cross-term ambiguity to signal ratio (CASR) also needs to be considered in addition to the azimuth and range ambiguity to signal ratio (AASR, RASR), and the whole orbital ergodic analysis should be carried out. To ensure that RASR, AASR, and CASR meet the requirement of −20 dB, it is necessary to select an appropriate PRF in the range of a few hundred Hertz. Full article

Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm. Full article

Hybrid and $\pm \pi /4$ quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space can obtain all polarimetric components simultaneously but suffer from severe azimuth ambiguities in the cross-polarized (cross-pol) measurement channels. In this paper, the hybrid and $\pm \pi /4$ quad-pol SAR systems with multiple receive channels in azimuth are widely investigated to suppress the azimuth ambiguities of the cross-pol components. We first provide a more thorough analysis of the multichannel hybrid and $\pm \pi /4$ quad-pol SAR systems. Then, the multichannel signal processing is briefly discussed for the reconstruction of the quad-pol SAR signal from the aliased signals, in which the conventional reconstruction algorithm causes extremely severe azimuth ambiguities. To this end, an improved reconstruction method is proposed based on a joint optimization, which allows for the minimization of ambiguities from the desired polarization and the simultaneous power of undesired polarized signal. This method can largely suppress azimuth ambiguities compared with the conventional reconstruction algorithm. Finally, to verify the advantages and effectiveness of the proposed approach, the azimuth ambiguity-to-signal ratio (AASR), the range ambiguity-to-signal ratio (RASR) and signal-to-noise ratio (SNR) of all polarizations, as well as a set of imaging simulation results, are given to describe the effects of reconstruction on the multichannel hybrid and $\pm \pi /4$ quad-pol SAR systems. Full article

In synthetic aperture radar (SAR) images, azimuth ambiguity is one of the important factors that affect image quality. Generally, the azimuth ambiguity-to-signal ratio (AASR) is a measure of the azimuth ambiguity of SAR images. For the low signal-to-noise ratio (SNR) ocean areas, it is difficult to accurately estimate the local AASR using traditional estimation algorithms. In order to solve this problem, a local AASR estimation method based on the Doppler power spectrum in SAR images is proposed in this paper by analyzing the composition of the local Doppler spectrum of SAR images. The method not only has higher estimation accuracy under low SNR, but also overcomes the limitations of traditional algorithms on SAR images when estimating AASR. The feasibility and accuracy of the proposed method are verified by simulation experiments and spaceborne SAR data. Full article

Sparse signal processing has already been introduced to synthetic aperture radar (SAR), which shows potential in improving imaging performance based on raw data or a complex image. In this paper, the relationship between a raw data-based sparse SAR imaging method (RD-SIM) and a complex image-based sparse SAR imaging method (CI-SIM) is compared and analyzed in detail, which is important to select appropriate algorithms in different cases. It is found that they are equivalent when the raw data is fully sampled. Both of them can effectively suppress noise and sidelobes, and hence improve the image performance compared with a matched filtering (MF) method. In addition, the target-to-background ratio (TBR) or azimuth ambiguity-to-signal ratio (AASR) performance indicators of RD-SIM are superior to those of CI-SIM in down-sampling data-based imaging, nonuniform displace phase center sampling, and sparse SAR imaging model-based azimuth ambiguity suppression. Full article