Accurate K-distributed clutter model for scanning radar application

Roy, Lakshi Prosad; Kumar, R.V.R.

doi:10.1049/iet-rsn.2009.0108

Cited by 34 publications

(28 citation statements)

References 21 publications

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“…When choosing number of samples, one must be aware of temporal and spatial correlation of the clutter. As already pointed out in [38], medium resolution radar with scanning antenna (such as conventional radar systems for maritime surveillance), results with texture temporal correlation of the order of the time-on-target and spatial correlation of the order of few tens of resolution range bins, provided that illuminated area is not highly irregular. If we denote a set of L range bins, fixed in azimuth j, as…”

Section: Network Designmentioning

confidence: 86%

“…GP is the solution of equation (37) and (38) as input parameters. Detection threshold T θ n for RBF network is determined by solving (28).…”

Section: Detection Threshold T √mentioning

confidence: 99%

“…Exact false and detection probabilities are compared with the design ones, for the method which employs RBF network and for the [zlog(z)] method which is either matched to the true distribution, or mismatched. While the index K emphasizes that the estimation of shape parameter is based on prior K-distribution according to (35) and (36), the index √ GP emphasizes that estimation is based on prior Pareto Type II distribution of intensity, where parameters are estimated from (37) and (38). Results are based on average of 100 runs for each false detection probability point, 1024 clutter samples and lognormal basis density.…”

Section: Detection Threshold T √mentioning

confidence: 99%

“…3.2), it takes on average 500 ms for the EM algorithm and 0.2 ms for the [zlog(z)] method which is about 2500 times faster. This is due to more intense calculation in steps (12)-(15) than in (35)- (38) and iterative nature of EM algorithm. On average, it takes about 100 iterations to achieve convergence of logarithmic likelihood (16) to the precision of 10 −6 .…”

Section: Detection Threshold T √mentioning

confidence: 99%

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Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network

Vondra¹,

Bonefačić²

2020

RADIOENGINEERING

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In this paper, we investigate feasibility of employing Radial Basis Function (RBF) network into non-coherent detection process, for detection of targets embedded in sea clutter of unknown statistics. We particularly have in mind Croatian part of Adriatic Sea, the local sea whose clutter statistic properties are not available in open literature. Performance of the detection process employing proposed RBF network is tested with simulated clutter samples based on real sea clutter data. These data were collected under sea state conditions that represent two thirds of the total wave heights in Adriatic and are chosen to represent unknown clutter statistics due to the fact that no single probability density function equally well fits amplitude distribution of the range bins under test. It is demonstrated that, compared to the traditional [zlog(z)] method, RBF network with just four components and lognormal basis function, yields operating characteristics that better match designed ones.

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Section: Network Designmentioning

confidence: 86%

“…GP is the solution of equation (37) and (38) as input parameters. Detection threshold T θ n for RBF network is determined by solving (28).…”

Section: Detection Threshold T √mentioning

confidence: 99%

Section: Detection Threshold T √mentioning

confidence: 99%

Section: Detection Threshold T √mentioning

confidence: 99%

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Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network

Vondra¹,

Bonefačić²

2020

RADIOENGINEERING

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“…However, the correlation in the time dimension differs from the temporal correlation of the sea clutter in the spotlight mode due to the antenna scanning. The correlation of the sea clutter data in the slow time domain becomes the combination of the azimuth correlation and the temporal correlation [23], [36], wherein the azimuth correlation mainly refers to the correlation of the texture components in the same range bin between different pulses.…”

Section: B Correlated Characteristics Of Sea Clutter Intensitymentioning

confidence: 99%

Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Wang

2015

IEEE Trans. Geosci. Remote Sensing

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It is known that the major difficulty of sea clutter simulation is the controlled generation of a continuous correlated non-Gaussian random process. In particular, for sea clutter at low grazing angles and over a long time scale, the spiky and nonstationary nature makes the simulation more difficult. This paper proposes a novel procedure for the simulation of continuous correlated low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. The Pareto distribution is utilized to describe the statistics of the sea clutter intensity. In addition, different correlation characteristics for sea clutter over both short and long time scales are considered in this paper. First, the memoryless nonlinear transform is adopted to simulate the intensities of the correlated sea clutter. Second, the Doppler spectra of different range bins are generated separately. In particular, for the sea clutter over long time scales, successive time-varying Doppler spectra are modeled to characterize the nonstationarity of the sea clutter series. Then, the phases of the sea clutter are retrieved by constraining to the desired Doppler spectra and given magnitudes. Different methods are adopted for the phase retrieval of sea clutter on short and long time scales. Finally, simulation results show that the proposed procedure can generate continuous low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. In particular, for the sea clutter over a long time scale, the simulated sea clutter series enjoys the time-varying Doppler spectrum while maintaining continuity. In addition, the proposed method is also suitable for other models, such as the K-distribution, the Weibull model, and so on.

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PSO-RBF Small Target Detection in Sea Clutter Background

Yin

Jia

2019

Lecture Notes in Electrical Engineering

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Accurate K-distributed clutter model for scanning radar application

Cited by 34 publications

References 21 publications

Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network

Mitigation of the Effects of Unknown Sea Clutter Statistics by Using Radial Basis Function Network

Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

PSO-RBF Small Target Detection in Sea Clutter Background

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