RFI suppression in ultra-wideband SAR systems using LMS filters in frequency domain

Luo, Xiaodong; Ulander, Lars M. H.; Askne, J.; Smith, G.; Frölind, P.-O.

doi:10.1049/el:20010153

Cited by 59 publications

(25 citation statements)

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“…RFI signal has comparatively narrow band, and can be considered but not limit to complex exponential signal. Previous researches [1][2][3][4][5][6] pointed that the power of RFI signals is tens of dBs greater than the power of the SAR echoes. So, s echo (t r , t a ) and s Noise (t r , t a ) can be together considered as wideband background noise which is independent from the RFI source, denoted as s WB (t r , t a ) = s echo (t r , t a )+s Noise (t r , t a ).…”

Section: Signal Model and Filtering Interpretation Of Ssamentioning

confidence: 99%

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RFI suppression in SAR based on filtering interpretation of SSA and fast implementation

Wang

Xiang-yang

et al. 2012

EURASIP J. Adv. Signal Process.

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Synthetic aperture radar (SAR) has proven to be a powerful remote sensing instrument for underground and obscured object detection. However, SAR echoes are often contaminated by radio frequency interferences (RFI) from multiple broadcasting stations. Essentially, RFI suppression problem is one-dimensional stationary time series denoising problem. This article proposed a novel RFI suppression algorithm based on singular spectral analysis (SSA) from a linear invariant systems perspective. It can be seen that SSA algorithm has an equivalence relation with finite impulse response (FIR) filter banks. Besides, this article first introduce two approximated methods which can remarkably speed up spectral decomposition-Nyström method and Column-Sampling approximation-to obtain the coefficients of above SSA-FIR-filter. Simulation results and imaging results of measured data have proved the efficiency and validity of this algorithm.

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Section: Signal Model and Filtering Interpretation Of Ssamentioning

confidence: 99%

“…Their main drawback is degradation in the range domain of SAR imagery when multiple RFI sources are present. Then, more attractive methods are based on least mean square (LMS) adaptive filters [4]. However, they have a tradeoff between length of the filter and numerical sensitivity of adaptation.…”

Section: Introductionmentioning

confidence: 99%

RFI suppression in SAR based on filtering interpretation of SSA and fast implementation

Wang

Xiang-yang

et al. 2012

EURASIP J. Adv. Signal Process.

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“…Fabrizio proposes adaptive beamforming techniques in spatial domain [9,10]. Suppressing interference in time domain and frequency domain fundamentally http://asp.eurasipjournals.com/content/2014/1/159 involves estimating parameters of interference and then suppressing, with iterative algorithm widely employed, like least-mean-square filters [11,12] and orthogonal subspace projection filtering [13]. Guo evaluates the interference cancelation performance of various schemes based on the minimum variance distortionless response (MVDR) criterion, in both time domain and Doppler domain [14].…”

Section: Introductionmentioning

confidence: 99%

Wideband signal design for over-the-horizon radar in cochannel interference

Luo

Lü

Chen

et al. 2014

EURASIP J. Adv. Signal Process.

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Ship detection in heavy sea clutter is a big challenge for over-the-horizon (OTH) radar. Wideband signal is helpful for improving range resolution and the signal-to-clutter ratio. In this paper, to support OTH radar employing wideband in cochannel interference, we propose environmental sensing-based waveform (ESBW) strategy, by considering transmit waveform design as an active approach and cognitive loop for the time-varying environment. In ESBW strategy, OTH radar monitors the environment in real time, estimates interference characteristics, designs transmit waveform adaptively, and employs traditional signal processing structure to detect targets in the presence of interference. ESBW optimization problem employs the criteria of maximizing the output signal-to-interference-plus-noise ratio (SINR) of matched filter and similarity constraint for reasonable range resolution and sidelobe levels. The analytic solution to this constrained problem is developed, so that ESBW design algorithm's efficiency is guaranteed, with adjustable SINR and autocorrelation function. A simulated scenario with strong interference and colored noise has been introduced. Simulation results demonstrate that OTH radar with ESBW strategy detects the target successfully in the background of cochannel interference.

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“…An early approach, which adaptively filters RFI in a simulated baseband SAR signal (40 MHz), is introduced in [2]. Another approach in the frequency domain with amplitude normalization aiming at low-frequency UWB SAR systems is presented in [3]. The limitations of these approaches can mainly be found in the least mean square (LMS) adaptive filter, which may be sensitive to nonstationary SAR scenes.…”

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confidence: 99%

“…The evaluation of their performance may be insufficient. For example, the efficiency of RFI suppression in the approaches presented in [2] and [3] is not clearly defined or is not compared with other methods. The computational burden demanded by these approaches is not discussed, and no solution to reduce the computational complexity is suggested.…”

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confidence: 99%