Digital adaptive robust algorithms for radar image filtering

Melnik, Vladimir P.; Pogrebnyak, Oleksiy; Zelensky, Alexander A.; Astola, Jaakko; Saarinen, Kari

doi:10.1117/12.240715

Cited by 27 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Technique 3-Combined technique [20] using the mean filter (8) for smoothing the Fourier magnitude and phase spectrum estimates separately according to (6) and (7). Technique 4-The novel combined technique using the median filter (9) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11). Technique 5-The novel combined technique using the mean filter (8) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11) Tec • Technique 5 provides a better ε than Technique 4 for a fixed N .…”

Section: Analysis Of Numerical Simulation Resultsmentioning

confidence: 99%

“…Technique 4-The novel combined technique using the median filter (9) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11). Technique 5-The novel combined technique using the mean filter (8) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11) Table 2). Because Techniques 4 and 5 produce better results than Techniques 2 and 3, we can conclude that it is reasonable to concentrate on filtering the real and imaginary parts of the recovered spectrum.…”

Section: Analysis Of Numerical Simulation Resultsmentioning

confidence: 99%

“…The following five different signal reconstruction techniques are considered and compared: (2) The combined technique [20] employing the median filter (9) for smoothing the Fourier magnitude and phase spectrum estimates separately according to (6) and (7) (Technique 2); (3) The combined technique [20] using the mean filter (8) for smoothing the Fourier magnitude and phase spectrum estimates separately according to (6) and (7) (Technique 3); (4) The novel combined technique using the median filter (9) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11) (Technique 4); (5) The novel combined technique using the mean filter (8) for smoothing the real and imaginary parts of the Fourier spectrum estimate separately according to (10) and (11) (Technique 5).…”

Section: Initial Data For Computer Simulationsmentioning

confidence: 99%

“…In this section, we exploit the sliding window filters assuming that the window size can be locally varying and data adaptive [7]- [10], [12]. According to the observations of the previous section, it is assumed that these filters are applied to process the real (10) and imaginary (11) parts of the Fourier spectrum.…”

Section: Local Adaptive Filtering Of Recovered Spectrummentioning

confidence: 99%

“…Section 4 contains a description of the adaptive filters [4], [5], [7]- [10], [12], [17] that can be effectively used for the case considered. Numerical simulation results obtained for the conventional bispectrum reconstruction technique and the proposed combined bispectrum filtering techniques are provided.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

Totsky

Fevralev

Katkovnik

et al. 2006

Circuits Syst Signal Process

Self Cite

View full text Add to dashboard Cite

The problem of reconstructing a signal waveform when the observed realizations are corrupted by intensive noise and random shifts is considered in this paper. Several ways of performing bispectrum filtering are proposed and investigated. First, it is shown that the signal reconstruction is more efficient if one applies smoothing to the recovered real and imaginary parts of the Fourier spectrum separately instead of filtering the magnitude and phase spectra recovered from a bispectrum estimate. Second, several nonadaptive filters are studied, and it is demonstrated that the proper choice of the filter type and its parameters is critical. Some adaptive filtering techniques based on the Z -parameter and on local polynomial approximation (LPA)-intersection of confidence intervals (ICI) are discussed. The performances of nonadaptive and adaptive filtering techniques in the bispectrumbased signal reconstruction are studied using the mean-squared error as the criterion. It is shown that the use of LPA-ICI and other adaptive filters provides improvement of signal reconstruction in comparison to the conventional bispectrum method and the combined bispectrum filtering methods proposed earlier for nonadaptive filters. The benefits achieved are mainly observed for low (smaller than unity) signal-to-noise ratios.

show abstract

Section: Analysis Of Numerical Simulation Resultsmentioning

confidence: 99%

Section: Analysis Of Numerical Simulation Resultsmentioning

confidence: 99%

Section: Initial Data For Computer Simulationsmentioning

confidence: 99%

Section: Local Adaptive Filtering Of Recovered Spectrummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

Totsky

Fevralev

Katkovnik

et al. 2006

Circuits Syst Signal Process

Self Cite

View full text Add to dashboard Cite

show abstract

Time Series Pattern Recognition Based on MAP Transform and Local Trend Associations

Batyrshin

Sheremetov

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The methods of pattern recognition in time series based on moving approximation (MAP) transform and MAP image of patterns are proposed. We discuss main properties of MAP transform, introduce a concept of a MAP image of time series and distance between time series patterns based on this concept which were used for recognition of small patterns in noisy time series. To illustrate the application of this technique to recognition of perception based patterns given by sequence of slopes, an example of recognition of water production patterns in petroleum wells used in expert system for diagnosis of water production problems is considered.

show abstract

Robust Approach to the Signal and Data Processing

Прокопенко

2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Digital adaptive robust algorithms for radar image filtering

Cited by 27 publications

References 0 publications

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

Time Series Pattern Recognition Based on MAP Transform and Local Trend Associations

Robust Approach to the Signal and Data Processing

Contact Info

Product

Resources

About