Analysis of Low-Oversampled Staggered SAR Data

Pinheiro, Muriel; Prats-Iraola, Pau; Rodríguez-Cassolà, Marc; Villano, Michelangelo

doi:10.1109/jstars.2019.2959092

Cited by 22 publications

(9 citation statements)

References 39 publications

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“…The BLU algorithm operated with the knowledge of the power spectral density (PSD) provides the best interpolation in the least-squares sense [21]. Let us assume the raw signal in azimuth, u(t), is a zero-mean complex random process.…”

Section: The Blu Reconstruction Algorithmmentioning

confidence: 99%

“…The width of blind area is cT p /2 in the raw data. However, the width in the range compressed data is twice because the echoes not fully received should be discarded [21], [40]. Furthermore, the reconstruction algorithms should also be considered depending on the two cases: (i) resampling the raw data on a uniformly-spaced grid and (ii) resampling the range-compressed data on a uniformly-spaced grid.…”

Section: Pri Trendmentioning

confidence: 99%

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Optimized Nonlinear PRI Variation Strategy Using Knowledge-Guided Genetic Algorithm for Staggered SAR Imaging

Zhang

Jiang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Staggered synthetic aperture radar (SAR), which operates with variable pulse repetition interval (PRI), staggers blind areas to solve the blind range problem caused by constant PRI in conventional high-resolution wide-swath SAR imaging. The PRI variation strategy determines the blind area distribution, and thus has a significant influence on the imaging performance in staggered mode. Generally, the existing strategies based on linear PRI variation can control the blind areas in a straightforward way, which has achieved impressive results. However, the linearity of the PRI variation imposes regularity or even periodicity on the locations of the blind areas, which limits the distribution of the blind areas. The imaging performance has the potential to be further improved by introducing much more irregularity into the PRI sequences. To this end, this paper proposes an optimized nonlinear PRI variation strategy for staggered SAR mode. First, a novel objective function is defined which quantitatively measures the uniformity of the blind area distribution along the slant range and the discontinuity of the blind area distribution along the azimuth. Subsequently, the optimum nonlinear PRI variation strategy is found using an optimization problem and the proposed objective function. A knowledge-guided genetic algorithm is proposed to solve the optimization problem. Comparisons with the existing linear variation strategies show that the proposed strategy can provide a superior imaging performance after reconstruction with a lower objective function value. Simulations and experiments on raw data generated in staggered SAR mode are performed to verify the effectiveness of the optimized nonlinear PRI variation strategy.

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Section: The Blu Reconstruction Algorithmmentioning

confidence: 99%

Section: Pri Trendmentioning

confidence: 99%

Optimized Nonlinear PRI Variation Strategy Using Knowledge-Guided Genetic Algorithm for Staggered SAR Imaging

Zhang

Jiang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…After evaluating the previous results, the number of recorded signals surrounding point 5 may have a weak effect on interpolating the missed signal, as shown in Figure 4. For that reason, two opposite spatial interpolation locations are implemented using IDW; Table 2 shows the effect of two opposite points to interpolate the signal at the location (5). The Goodness of fit is used to evaluate the interpolated signals 1536 concerning the real one.…”

Section: The Proposed Techniquesmentioning

confidence: 99%

“…This approach is not likely to be used as a linear interpolation [4]. Estimating missing data is an essential issue in signal processing [5]. Spatial interpolation has been commonly and frequently utilized in many analyses to obtain surface data dependent on a set of sampled points, like temperature, soil properties, and precipitation [6].…”

Section: Introductionmentioning

confidence: 99%

A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

Ibrahim

Farhan

Safi

2022

IJECE

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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

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“…The continuous variation of the PRI recalls staggered SAR systems, which include BLU interpolation as an integrating part of the concept [27]- [30], are characterized by smeared and decorrelated range and azimuth ambiguities [24], [32], and are well suited for interferometry [33]- [35]. While in staggered SAR, however, a PRI variation is required that ideally shifts blind ranges to all possible positions across the swath in order to have them uniformly distributed, for the scope of this work the PRI variation should allow keeping the width of the imaged swath.…”

Section: A Pri Variation Schemesmentioning

confidence: 99%

Decorrelating Ambiguities in SAR Interferometry Through Slight PRI Variation

Villano

Peixoto

Ustalli

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Synthetic aperture radar (SAR) interferometry is a well-established technique for producing high-resolution digital elevation models (DEMs) of the Earth's surface and measuring displacements on different time scales. Observations of SAR interferograms, however, show that azimuth ambiguities can be coherently imaged and may lead to phase biases and coherence losses that significantly degrade the interferometric performance. Whereas imposing very low ambiguity levels may represent a severe design constraint for a spaceborne SAR system, a slight variation of the pulse repetition interval (PRI) is a new, simple, yet effective technique to decorrelate ambiguities, which in turn reduces the phase biases and coherence losses without substantially affecting the imaged swath width. An additional benefit of the PRI variation is that range ambiguities also become decorrelated. This paper addresses two cases: For the repeat-pass case, slightly different pulse repetition frequencies (PRFs) can be used for the two acquisitions and the minimum required PRF difference can be analytically derived resorting to the power spectral density of the ambiguous signals; For the single-pass case, a slight variation of the PRI during the common acquisition is an effective solution, in case an along-track baseline is present. In particular, a square wave PRI variation scheme outperforms sinusoidal or random ones. Finally, simulations using TanDEM-X data are presented to show the improvement in interferogram and DEM quality resulting from ambiguity decorrelation. This work is relevant for the design of future spaceborne interferometric SAR systems and for the enhanced exploitation of current ones.

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Analysis of Low-Oversampled Staggered SAR Data

Cited by 22 publications

References 39 publications

Optimized Nonlinear PRI Variation Strategy Using Knowledge-Guided Genetic Algorithm for Staggered SAR Imaging

Optimized Nonlinear PRI Variation Strategy Using Knowledge-Guided Genetic Algorithm for Staggered SAR Imaging

A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

Decorrelating Ambiguities in SAR Interferometry Through Slight PRI Variation

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