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This article presents an efficient 2-D baseband azimuth scaling (2-D BAS) approach for the focusing of data acquired in the spaceborne squinted sliding spotlight and high-resolution wide-swath (HRWS) terrain observation by progressive scan (TOPS) imaging modes. The existing approaches can become inefficient or invalid due to the coexistence of central “absolute” and marginal “relative” squint in the above-mentioned modes. In this article, the signal properties of spaceborne squint synthetic aperture radar (SAR) with the antenna electronically steering in the azimuth dimension are analyzed first, based on which a new parity-decomposition-based range equation (PDRE) is presented to dedicatedly model the range histories of targets illuminated by the rotating beam. A novel 2-D BAS approach is then developed to remove the odd asymmetric part but preserve the even symmetric part of PDRE, which means a “true derotation” for eliminating the effect of both absolute and relative squints caused by the beam rotation. An even-order-multinomial perturbation function is applied and integrated into a range-Doppler-based SAR processing kernel to efficiently compensate the azimuth variation of high-order range cell migration (RCM) and azimuth phase modulation caused by the 2-D BAS. The proposed processor is efficient, since none of the data extension, the postprocessing for resolving focused-domain folding, or the 2-D frequency-domain interpolation at high squint is needed. Simulations with point targets in the squinted sliding spotlight and HRWS TOPS modes are used to validate the developed algorithm. [ABSTRACT FROM AUTHOR]

Standard synthetic aperture radar (SAR) processors use analytically derived transfer functions that rely on the assumption of hyperbolic range histories of monostatic SARs with straight flight paths. The range histories for bistatic SARs are no longer hyperbolic and simple analytic transforms do not exist. In this article, a bistatic subsection hyperbolic range history (BS-HRH) model is proposed and based on it; a generic processing scheme for bistatic SAR imaging is given with which simple analytic transforms for standard monostatic SARs can still be applied. One-dimensional and two-dimensional signal-level simulations are done using the orbit parameters of Envisat and a classic pendulum-like bistatic configuration under different baselines. It is found that the proposed BS-HRH algorithm performs well for spaceborne bistatic SARs even with extreme bistatic configurations and/or very high azimuth resolution. Compared with the existing analytical ‘method of series reversion (MSR)’ and the numerical ‘NuSAR’ algorithm, easy implementation, high efficiency and accuracy, and mathematical simplicity are among the advantages of the proposed algorithm. The approach of BS-HRH has the potential to solve the general HRH-deviation problem for other new SARs with more complex imaging geometry. [ABSTRACT FROM PUBLISHER]

Because of the sensitivity of the Kalman framework to gross errors, proper techniques for detection of gross errors are necessary. By integrating the selection of quasi-accurate observations and the Kalman framework, a new filter called the quasi-accurate filter (QUAF) is developed. The expansibility and implementation scheme of the new algorithm are then discussed in detail, and the reliability matrix for the Kalman filter is proposed to analyze the reliability of the filters with different detection technologies. Finally, the experimental results from a real world case study are used to validate the conclusions. The QUAF carries out the preliminary selection of the quasi-accurate observations (QAOs) using the innovation of the Kalman filter, and use the check QAOs to determine reasonable observations. This causes the QUAF to handle more easily and possess wider expansibility. QUAF can be reformulated to the special cases of several common detection methods, such as the innovation method, robust estimation and quasi-accurate detection (QUAD). Since only reasonable observations are used, the QUAF has better detection accuracy and stronger avoidance of gross errors than the innovation method and robust estimation. Meanwhile, compared with QUAD methods, QUAF introduces the state-predicted model, requiring fewer quasi-accurate observations and making it more suitable for systems with complicated observation structures or sparse observations. [ABSTRACT FROM AUTHOR]

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow. [ABSTRACT FROM AUTHOR]