Filtering approaches for interference suppression in low-frequency SAR

Lamont-Smith, T.; Hill, Robert D.; Hayward, S.D.; Yates, Graeme K.; Blake, A.P.

doi:10.1049/ip-rsn:20050092

Cited by 37 publications

(11 citation statements)

References 12 publications

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“…Li et al proposed a normalized LMS filter with clipping of strong scatterers to reduce the asymmetric range side-lobes level [56]. To improve the computation efficiency, the Wiener filter is also applied on the SAR data [57,58]. The LMS-based methods suffer from dealing with the wide band interferences and time-varying sources.…”

Section: Recursive Filtermentioning

confidence: 99%

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

et al. 2019

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Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

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Section: Recursive Filtermentioning

confidence: 99%

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

et al. 2019

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“…Another disadvantage of stepped-frequency chirp is the sensitivity to inference [4], such as radio-frequency interference, self-induced interference, and artificial interference, etc. [5]. Therefore, partial pulses with these interferences may be unusable.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing of Stepped-Frequency Radar Based on Transfer Learning

Liu

et al. 2015

IEEE Trans. Signal Process.

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It usually suffers from long observing time and interference sensitivity when a radar transmits the high-range-resolution stepped-frequency chirp signal. Motivated by this, only partial pulses of the stepped-frequency chirp are utilized. For the obtained incomplete frequency data, a Bayesian model based on transfer learning is proposed to reconstruct the corresponding full-band frequency data. In the training phase, a complex beta process factor analysis (CBPFA) model is utilized to statistically model each aspect-frame from a set of given full-band frequency data, whose probability density function (pdf) can be learned from this CBPFA model. It is important to note that the numbers of factors and dictionaries are automatically learned from the data. The inference of CBPFA can be performed via the variational Bayesian (VB) method. In the reconstruction phase for the incomplete frequency data that "related" to the training samples, its corresponding full-band frequency data can be analytically reconstructed via the compressive sensing (CS) method and Bayesian criterion based on the transfer knowledge of the previous pdfs learned from the training phase. About the "relatedness" between each training frame and the incomplete test frequency data, we utilize the frame condition distribution of incomplete test frequency data to represent. The proposed method is validated on the measured high range resolution (HRR) data.Index Terms-Bayesian, complex beta process factor analysis, compressive sensing, stepped-frequency, transfer learning, variational inference. 1053-587X

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“…There are many examples of interference effects upon synthetic aperture radar (SAR) images in the literature, and associated mitigation techniques. [5][6][7][8][9] Many interference mitigation techniques are tuned to a particular type of interference for a specific radar. Although most of these techniques are not designed to mitigate chirped sources of interference, many of the techniques could be applicable.…”

Section: Introductionmentioning

confidence: 99%

Application of equalization notch to improve synthetic aperture radar coherent data products

Musgrove

West

2015

SPIE Proceedings

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Interference and interference mitigation techniques degrade synthetic aperture radar (SAR) coherent data products. Radars utilizing stretch processing present a unique challenge for many mitigation techniques because the interference signal itself is modified through stretch processing from its original signal characteristics. Many sources of interference, including constant tones, are only present within the fast-time sample data for a limited number of samples, depending on the radar and interference bandwidth. Adaptive filtering algorithms to estimate and remove the interference signal that rely upon assuming stationary interference signal characteristics can be ineffective. An effective mitigation method, called notching, forces the value of the data samples containing interference to zero. However, as the number of data samples set to zero increases, image distortion and loss of resolution degrade both the image product and any second order image products.Techniques to repair image distortions, 1 are effective for point-like targets. However, these techniques are not designed to model and repair distortions in SAR image terrain. Good terrain coherence is important for SAR second order image products because terrain occupies the majority of many scenes. For the case of coherent change detection it is the terrain coherence itself that determines the quality of the change detection image. This paper proposes an unique equalization technique that improves coherence over existing notching techniques. First, the proposed algorithm limits mitigation to only the samples containing interference, unlike adaptive filtering algorithms, so the remaining samples are not modified. Additionally, the mitigation adapts to changing interference power such that the resulting correction equalizes the power across the data samples. The result is reduced distortion and improved coherence for the terrain. SAR data demonstrates improved coherence from the proposed equalization correction over existing notching methods for chirped interference sources.

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Filtering approaches for interference suppression in low-frequency SAR

Cited by 37 publications

References 12 publications

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

Compressive Sensing of Stepped-Frequency Radar Based on Transfer Learning

Application of equalization notch to improve synthetic aperture radar coherent data products

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