Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach

Liu, Jin; Wang, Wei; Song, Hongjun

doi:10.3390/s20020419

Cited by 8 publications

(4 citation statements)

References 28 publications

(46 reference statements)

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“…In signal processing, a window function is used to reduce the sidelobes generated by the Gibbs phenomenon in accordance with the frequency transform [1]. For applications using the window function, we raise the suppression of co-channel interference [2], harmonic analysis in real-time systems [3], satellite altimeter waveforms [4], radar systems [5]- [7], sensor arrays [8], and audio systems [9], [10]. The window function has been studied in various ways [11]- [22].…”

Section: Introductionmentioning

confidence: 99%

New Class of Cosine-Sum Windows

Yamaoka

Kageme

2023

IEEE Access

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Window functions are required to achieve excellent frequency resolution, peak sidelobe ratio, and sidelobe decay. The cosine-sum window function satisfies these requirements. However, conventional cosine-sum windows limit the number of terms and coefficient values. Therefore, this study proposes a new class of cosine-sum windows that has more terms than those in the conventional cosine-sum window and considers positive and negative coefficients. For such windows, we present three design examples. The first is a window function with low sidelobe decay, which reduce the sidelobes by adding correction terms to the conventional windows without damaging the sidelobe decay. The second is a window function that improves the frequency resolution by emphasizing the discontinuities of the extracted signal. The third is a window function that reduces sidelobes further without deteriorating the frequency resolution by adding correction terms to the window functions, which are designed in advance. We confirmed the effectiveness of these examples of the proposed window function.INDEX TERMS Window function, cosine-sum windows, raised-cosine windows, Hann window, Blackman window.

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Section: Introductionmentioning

confidence: 99%

New Class of Cosine-Sum Windows

Yamaoka

Kageme

2023

IEEE Access

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“…In addition to the high geometric resolution, low SLL and high SNR are two other important performances to measure the SAR image quality. However, there has always been a contradiction between the low SLL and the high SNR, since the low sidelobes are usually obtained by window weighting during focusing process, which results in the SNR reduction in the focused SAR images [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

Zhang

Fang³

et al. 2021

Sensors

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In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.

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“…In the same context, Liu et at. presented a convex optimization problem to optimize the sidelobe attenuation or the Signalto-Noise Ratio (SNR) [18]. Zaytsev and Khzmalyan also proposed a method to synthesize optimal windows with sidelobe spectrum falloff rate multiple of 12 dB per octave (dB/oct) [15].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a major need for a more systematic procedure to develop window functions. In this sense, this investigation proposes a generalization for the representation of window functions for a wide range of applications, e.g., suppression of co-channel interference [19], harmonic analysis in real-time systems [20], satellite altimeter's waveform [21], radar systems [18], [22], [23], sensor arrays [24], and audio systems [3], [25].…”

Section: Introductionmentioning

confidence: 99%

Generalized Adaptive Polynomial Window Function

Justo

Beccaro

2020

IEEE Access

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We present a novel method to design and optimize window functions based on combinations of linearly independent functions. These combinations can be performed using different strategies, such a sums of sines/cosines, series, or conveniently using a polynomial expansion. To demonstrate the flexibility of this implementation, we propose the Generalized Adaptive Polynomial (GAP) window function, a nonlinear polynomial form in which all the current window functions could be considered as special cases. Its functional flexibility allows fitting the expansion coefficients to optimize a certain desirable property in time or frequency domains, such as the main lobe width, sidelobe attenuation, and sidelobe falloff rate. The window optimization can be performed by iterative techniques, starting with a set of expansion coefficients that mimics a currently known window function and considering a certain figure of merit target to optimize those coefficients. The proposed GAP window has been implemented and several sets of optimized coefficients have been obtained. The results using the GAP exemplify the potentiality of this method to obtain window functions with superior properties according to the requirements of a certain application. Other optimization algorithms can be applied within this strategy to further improve the window functions. INDEX TERMS Discrete Fourier transforms, signal processing, optimization methods, adaptive algorithms.

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Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach

Cited by 8 publications

References 28 publications

New Class of Cosine-Sum Windows

New Class of Cosine-Sum Windows

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

Generalized Adaptive Polynomial Window Function

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