Anatolii Kononov
Yonsei University, School of Integrated Technology, Department Member
This paper introduces a Doppler domain localized (DDL) implementation of the adaptive matched filter (AMF) for radar target detection in severely heterogeneous clutter environments with limited training data. The proposed detector uses... more
This paper introduces a Doppler domain localized (DDL) implementation of the adaptive matched filter (AMF) for radar target detection in severely heterogeneous clutter environments with limited training data. The proposed detector uses the concept of a region of possible target detection (RPTD), a small set of Doppler bins that captures most of the target signal power. This RPTD-based DDL-AMF detector outperforms an earlier suggested DDL implementation of the generalized likelihood ratio (GLR) test, which employs the region of detection improvement (RODI) concept. Unlike the RODI-based DDL-GLR detector, the proposed DDL-AMF detector requires no information on clutter spectrum parameters and no measurements to determine the number and locations of RODIs. Moreover, the RODI-based DDL-GLR detector's performance falls far below the optimum when the target Doppler frequency is unknown. In contrast, the RPTD-based DDL-AMF detector ensures rapid adaptive detection with near-optimum performance under unknown target Doppler frequency and multimodal clutter spectra.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
This paper introduces a new Doppler domain localized (DDL) implementation of the adaptive matched filter (AMF) for radar target detection in severely heterogeneous clutter environments with limited training data. The proposed detector... more
This paper introduces a new Doppler domain localized (DDL) implementation of the adaptive matched filter (AMF) for radar target detection in severely heterogeneous clutter environments with limited training data. The proposed detector uses the concept of a region of possible target detection (RPTD), a small set of Doppler bins that captures most of the target signal power. This RPTD-based DDL-AMF detector outperforms an earlier suggested DDL implementation of the generalized likelihood ratio (GLR) test, which employs the region of detection improvement (RODI) concept. Unlike the RODI-based DDL-GLR detector, the proposed DDL-AMF detector requires no information on clutter spectrum parameters and no measurements to determine the number and locations of RODIs. Moreover, the RODI-based DDL-GLR detector’s performance falls far below the optimum when the target Doppler frequency is unknown. In contrast, the RPTD-based DDL-AMF detector ensures rapid adaptive detection with near-optimum per...
ABSTRACT The effect of look angle on the accuracy performance of fixed-baseline interferometric synthetic aperture radar is studied. It is shown that there exists an optimal look angle that minimizes the variance of the surface height... more
ABSTRACT The effect of look angle on the accuracy performance of fixed-baseline interferometric synthetic aperture radar is studied. It is shown that there exists an optimal look angle that minimizes the variance of the surface height estimate for a resolution cell, and it depends upon the system as well as surface parameters. Numerical analysis confirming the existence of the optimal look angle is presented
Research Interests:
Research Interests:
Research Interests: Mathematics, Geophysics, Computer Science, Statistical Analysis, Computer Model, and 15 moreSynthetic Aperture Radar, Geomatic Engineering, Vegetation, Physical Optics, Data Model, Analytical Model, Mixture Model, Statistical Properties, Radar Cross Section, Ground Truth, Lognormal Distribution, Electrical And Electronic Engineering, Gamma Distribution, Backscattering, and backscatter
This paper introduces a class of model-matched Toeplitz covariance matrix estimation (MM TCME) algorithms for adaptive arrays. Adaptive filters employing these algorithms are referred to as TMI filters. When the angular separation between... more
This paper introduces a class of model-matched Toeplitz covariance matrix estimation (MM TCME) algorithms for adaptive arrays. Adaptive filters employing these algorithms are referred to as TMI filters. When the angular separation between the interference sources is not too close to a certain statistical resolution limit (SRL), the convergence rate for TMI filters is superior to that of loaded persymmetric covariance matrix inversion (LPMI) filters and to that of the well-known loaded SMI (LSMI) filters. In terms of the 3dB average SNR loss, for the TMI filters, the required training sample size is about ml2 (m is the number of interference sources), while that for the LPMI and LSMI filters is about $m$ and 2m, respectively. Since the TMI filters may suffer severe SNR degradation when the angular separation between the sources is near the SRL two remedies for dealing with this problem are discussed herein.
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This paper is intended to introduce a class of signals based on frequency coding sequences that may be constructed by means of perfect difference sets. A regular algorithm for the construction of the frequency coding sequences is... more
This paper is intended to introduce a class of signals based on frequency coding sequences that may be constructed by means of perfect difference sets. A regular algorithm for the construction of the frequency coding sequences is described. These sequences give rise to multifrequency signals having considerably lower sidelobes in the region surrounding the main lobe of the time autocorrelation function against those exhibited by conventional frequency-hopped signals. In addition, the signals exhibit a thumbtack ambiguity function with relatively low sidelobes over a wide region of the time-frequency plane.
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ABSTRACT An algorithm for detection of continuous-wave noise jamming in two-dimensional surveillance radar is proposed. The algorithm is highly immune to strong interfering spikes due to using censored video integration of range noise... more
ABSTRACT An algorithm for detection of continuous-wave noise jamming in two-dimensional surveillance radar is proposed. The algorithm is highly immune to strong interfering spikes due to using censored video integration of range noise samples collected during dwell time for each azimuth resolution cell. To detect noise jamming the algorithm applies order statistics CFAR to a sequence of azimuthal samples resulted from censored video integration in range. The detection performance of the algorithm in the absence of interference is evaluated numerically by means of an integral equation and verified using statistical simulations. In the presence of infinitely strong spikes the detection and false alarm performances are estimated using statistical simulations.
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Research Interests:
Abstract-The paper introduces a new class of adaptive CFAR methods to cope with the problem of outliers due to the presence of clutter edges and interfering targets. A fundamental distinction between the proposed approach and existing... more
Abstract-The paper introduces a new class of adaptive CFAR methods to cope with the problem of outliers due to the presence of clutter edges and interfering targets. A fundamental distinction between the proposed approach and existing adaptive CFAR approaches is that in order to maintain robust performance the former uses information on positions at which estimated outlier-free cells appear in the full reference window and the statistics of the sample in the cell under test. The performance of one of the possible implementations of new adaptive CFAR methods is studied and compared with that of an existing adaptive CFAR approach. The results show significant advantages of the proposed class of adaptive CFAR methods in both the false alarm regulation property and detection performance.
This paper introduces a robust constant false alarm rate (CFAR) method to detect continuous noise jamming in coherent radar systems with a single antenna having no pattern control. The proposed detector is robust to interfering signals... more
This paper introduces a robust constant false alarm rate (CFAR) method to detect continuous noise jamming in coherent radar systems with a single antenna having no pattern control. The proposed detector is robust to interfering signals such as strong spikes from neighboring radars and returns from targets of interest and is resistant to land, sea, and weather clutter. The detector operates on data vectors extracted from a real-valued Range-Doppler data matrix generated at the output of Doppler processing for each azimuth cell within the entire scanning sector. Each data vector consists of statistically independent range samples associated with one of the specified Doppler bins. These samples are selected from non-overlapping range intervals allocated within the noise-dominant region in the full range coverage to mitigate the effect of clutter on the detector’s performance. To perform jamming detection for each cell under test (CUT) in the current antenna scan, the proposed detector ...
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ABSTRACT This paper presents an algorithm for retrieving the stem volume of forest stands on relatively flat ground. A forest backscatter model at the individual tree level is used to derive the algorithm. This model treats the trunk... more
ABSTRACT This paper presents an algorithm for retrieving the stem volume of forest stands on relatively flat ground. A forest backscatter model at the individual tree level is used to derive the algorithm. This model treats the trunk volumes as random quantities and employs a concept of random forest reflection coefficient. The algorithm is derived under the assumption that the only information on areal tree density and mean value of the forest reflection coefficient is available. Performance of the algorithm is investigated by means of Monte-Carlo simulation for different scenarios in terms of statistical distributions for the trunk volume and forest reflection coefficient. The results of simulation have shown that the algorithm exhibits robustness to the distributions and provides accurate stem volume estimation over a relatively wide range of the unknown variances of the distributions.
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An algorithm based on the exact maximum likelihood (ML) estimator for retrieving the mean stem volume of mature forest stands on relatively flat ground is presented. A VHF-band forest backscatter model at the individual tree level is used... more
An algorithm based on the exact maximum likelihood (ML) estimator for retrieving the mean stem volume of mature forest stands on relatively flat ground is presented. A VHF-band forest backscatter model at the individual tree level is used to derive the algorithm. The model interprets the tree trunk volume as a random variable and employs a concept of random forest reflection coefficient to characterize fluctuations of radar returns from individual trees. The algorithm is derived under the condition that both the trunk volume and forest reflection coefficient are non-random constant values. Performance (normalized standard deviation and bias) of the algorithm is analyzed by means of Monte-Carlo simulation for various scenarios in terms of statistical distributions for the trunk volume and forest reflection coefficient. It is shown that the algorithm exhibits robustness to the distributions and provides nearly unbiased and accurate stem volume estimation over a wide range of the varia...