Dynamic clutter mitigation using sparse optimization

Uysal, Faruk; Selesnick, Ivan; Pillai, Unnikrishna; Himed, Braham

doi:10.1109/maes.2014.130137

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…The selection of the sequence interval L should fulfill the following condition: the detection probability of precipitation P d is close to 1 and the false alarm rate P f a is as low as possible. Setting L in the interval of [16,26], we used Ray 68 of IDRA data measured at 02:00 UTC on 1st July 2011, to calculate the P d and P f a which are shown in Fig. 7.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…To explore the influence of N and L, a simulation was conducted by setting N in the interval of [4,40] in a step of 2 and L in the interval of [16,26] with the same step. This was done for Ray 68 of IDRA data measured at 02:00 UTC on 1st July 2011.…”

Section: A Spectrogram Sequence Parameter Selectionmentioning

confidence: 99%

“…The undesirable effect of WTC on weather radar is illustrated in [20], [21], on air traffic control radar in [22], and on marine radar in [23]. Methods such as interpolation [24] [25], signal decomposition [26] [9] and machine learning [27], have been investigated to mitigate the WTC. Last but not least, radio frequency interference (RFI) arising from the Radio Local Area Network (RLAN) is an increasing concern for the radar community, for example, the C-band European operational weather radar network (EUMETNET/OPERA Radar Network) [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radar Target and Moving Clutter Separation Based on the Low-Rank Matrix Optimization

Yin

Unal

Schleiss

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-A novel algorithm is put forward to separate radar target and moving clutter based on a combination of the low-rank matrix optimization (LRMO) and a decision tree. Similar to the moving object detection in the field of automated video analysis, the proposed separation method, which is carried out in the range-Doppler domain, makes use of different motion variation of radar target and clutter in the spectrogram sequence. The technique is very general but the focus of this paper is on narrowband moving clutter suppression in weather radar. The first step in implementing this method is the generation of a rangeDoppler spectrogram sequence. Then, the LRMO is applied to the obtained sequence to divide target and moving clutter into foreground and background. From the foreground sequence which is obtained by solving the LRMO, foreground frequency and spectral width are combined in a decision tree to obtain a filtering mask to mitigate the narrow-band moving clutter and noise. Data collected by a polarimetric Doppler weather radar known as the IRCTR Drizzle Radar (IDRA) are used to validate the performance of the proposed algorithm. Moreover, its effectiveness in removing narrow-band moving clutter is quantitatively assessed through comparisons with another clutter mitigation method.

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Section: Parameter Selectionmentioning

confidence: 99%

Section: A Spectrogram Sequence Parameter Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radar Target and Moving Clutter Separation Based on the Low-Rank Matrix Optimization

Yin

Unal

Schleiss

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…The success of the approach was based on sparse signal representation, exploited in many applications such as compressive sensing [9]- [11] and signal separation [12]- [14]. The TQWT, first discussed in [15], is a key part of this theory and is used to describe the non-sustained (low Q) and sustained (high Q) oscillatory features of the signal.…”

Section: Resonance Based Separationmentioning

confidence: 99%

Target detection in sea clutter using resonance based signal decomposition

Rosenberg

Nguyen

2016

2016 IEEE Radar Conference (RadarConf)

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Detection in the maritime domain requires the radar return from an object of interest (target) to be distinguishable from the background interference (sea clutter and noise). These radars traditionally use non-coherent processing methods due to the time-varying and range-varying nature of the Doppler spectra. However, the radar backscatter may contain sea-spikes which can last for seconds and resemble targets. In this paper, a method of sparse signal separation known as 'basis pursuit denoising' has been applied to two different sea clutter data sets to demonstrate the potential for improved detection performance. We also explore how the parameters required for this technique can be selected depending on the target velocity.

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“…Methods such as interpolation [4] [5], signal decomposition [6] [7] and machine intelligence technique [8], to mitigate the WTC are investigated by different scholars. It is well recognized that radar polarimetry plays an indispensable role in improving the retrieval of microphysical parameters and in removing non-meteorological scatters.…”

Section: Introductionmentioning

confidence: 99%

Spectral Polarimetric Features Analysis of Wind Turbine Clutter in Weather Radar

Yin

Krasnov

Unal

et al. 2017

2017 11th European Conference on Antennas and Propagation (EUCAP)

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Abstract-Wind turbine clutter has gradually become a concern for the radar community for its increasing size and quantity worldwide. Based on the S-band polarimetric Doppler PARSAX radar measurements, this paper demonstrates the micro-Doppler features and spectral-polarimetric characteristic of wind turbine clutter, the probability distribution functions of different spectralpolarimetric variables. Finally, a simple thresholding method to remove wind turbine clutter is put forward, and its effectiveness can be verified by the measured data. This work is expected to contribute to developing effective algorithms for this dynamic clutter suppression for operational weather radar.

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Dynamic clutter mitigation using sparse optimization

Cited by 13 publications

References 24 publications

Radar Target and Moving Clutter Separation Based on the Low-Rank Matrix Optimization

Radar Target and Moving Clutter Separation Based on the Low-Rank Matrix Optimization

Target detection in sea clutter using resonance based signal decomposition

Spectral Polarimetric Features Analysis of Wind Turbine Clutter in Weather Radar

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