An omega‐<i>k</i> algorithm for multireceiver synthetic aperture sonar

Zhang, Xuebo; Yang, Peixuan; Sun, Haixin

doi:10.1049/ell2.12859

Cited by 23 publications

(18 citation statements)

References 14 publications

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“…RDA represents the most conventional imaging algorithm. Both CSA and wKA are improved frequency domain algorithms that achieve higher accuracy [34,35]. Ref.…”

Section: Methods Of Low Sidelobe and Anti-jammingmentioning

confidence: 99%

“…Ref. [35] extended the widely used wKA based on LBF, yielding high-resolution results akin to [23], Bernoulli-FM signal [16,32], tent-FM signal [33], and C-NLFM signal.…”

Section: Methods Of Low Sidelobe and Anti-jammingmentioning

confidence: 99%

“…Ref. [35] extended the widely used wKA based on LBF, yielding high-resolution results akin to those achieved by BPA.…”

Section: Methods Of Low Sidelobe and Anti-jammingmentioning

confidence: 99%

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A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

Li,

Wang,

Zhang

et al. 2023

Remote Sensing

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Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

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“…RDA represents the most conventional imaging algorithm. Both CSA and wKA are improved frequency domain algorithms that achieve higher accuracy [34,35]. Ref.…”

Section: Methods Of Low Sidelobe and Anti-jammingmentioning

confidence: 99%

“…Ref. [35] extended the widely used wKA based on LBF, yielding high-resolution results akin to [23], Bernoulli-FM signal [16,32], tent-FM signal [33], and C-NLFM signal.…”

Section: Methods Of Low Sidelobe and Anti-jammingmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

Li,

Wang,

Zhang

et al. 2023

Remote Sensing

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“…8,9 InSAS is a relatively new threedimensional imaging sonar technique. [10][11][12] Compared with SAS, [13][14][15][16][17][18][19] its advantage is that it has the ability of bathymetric. InSAS adds one or more receiver arrays based on SAS to obtain the height value of targets or the deformation of the seafloor by using the phase difference.…”

Section: Introductionmentioning

confidence: 99%

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Huang,

Ding,

Wang

et al. 2023

Advances in Mechanical Engineering

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Interferometric synthetic aperture sonar (InSAS) is a novel three dimensional underwater mapping system, which is widely used in various fields. Interferometric phase filtering is a crucial process in InSAS signal processing. The study of interferogram filtering method has great significance to improve the signal-to-noise ratio (SNR) of interferogram, reduce the difficulty of phase unwrapping, and improve the accuracy of digital elevation maps. Firstly, this article introduces the sources of interferometric phase noise. Secondly, it illustrates the difference between real domain filter and complex domain filter by simulation experiment. Thirdly, the various methods of InSAS interferogram filtering are systematically studied, which can be classified into spatial domain filtering and transform domain filtering. The transform domain filtering can be further divided into frequency domain and wavelet transform filtering. Moreover, the advantages and disadvantages of various methods are analyzed theoretically. Finally, numerical simulation and real-data processing experiments are performed, and the advantages and shortcomings of several methods are compared using residue numbers, root mean square error (RMSE), and CPU time as evaluation criteria.

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“…The novelty of SAIR lies in the application of the Synthetic Aperture technique, which was initially used in SAR (Synthetic Aperture Radar) to achieve high spatial resolution and is now playing a broader role in other domains, such as the underwater SAS (Synthetic Aperture Sonar) (Yang, 2023;Zhang et al, 2023) and the discussed SAIR. The envision of SAIR was proposed early in 1983 (Levine and Good, 1983), but not until 2009 the realization of MIRAS did the technique find its way into supporting SSS observation (Font et al, 2001), mainly because the optimal bandwidth for the task is L-band, a relatively low frequency that would otherwise adopt an oversized real aperture.…”

Section: Introductionmentioning

confidence: 99%

RFI mitigation for 2D Synthetic Aperture Interferometric Radiometers using combined theoretical and machine learning technique

Xu,

Li,

Yin

2023

Front. Mar. Sci.

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Synthetic Aperture Interferometric Radiometer (SAIR) as one of the most advanced instruments for Sea Surface Salinity (SSS) observation, has been in service on SMOS mission for years and is planned on the Chinese Ocean Salinity Satellite in the near future. However, a lot of Radio Frequency Interference (RFI) emissions are found in SMOS views, which contaminate the brightness temperature measurements of the SAIR instrument, and further impede the retrieval of SSS fields. Concerning SAIR’s operating mode, this study proposes an RFI mitigation method comprising two algorithms for co- and cross-polarization, respectively. First, RFI signatures are identified based on a series of thresholds defined by radiation theory, and then mitigated through a simple machine learning technique of Support Vector Regression (SVR), leveraging either SAIR’s multi-angle measurements or sea surface roughness descriptors, depending on the specific polarization mode. Finally, the outputs of all polarizations are merged and written back to the Level 1C brightness temperature product as the final result. Using the proposed method, the notable outliers arose from RFI contamination are attenuated, and the variation of standard deviations over nearby snapshots is smoothed, as expected on a homogeneous ocean. Furthermore, with the official L2OS software implementing the SSS retrieval procedure from the rewritten Level 1C brightness temperatures, the data re-gain of SSS fields is achieved in some places that are not attainable for the current SMOS Level 2 SSS products, with a reasonable error compared to WOA2009 SSS, confirming the validity of the proposed method. Hopefully, this work could provide a practical solution to current and future SAIR observing predicaments.

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An omega‐k algorithm for multireceiver synthetic aperture sonar

Cited by 23 publications

References 14 publications

A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

RFI mitigation for 2D Synthetic Aperture Interferometric Radiometers using combined theoretical and machine learning technique

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