Multistatic adaptive CFAR detection in non-Gaussian clutter

Palamà, Riccardo; Greco, Maria; Gini, Fulvio

doi:10.1186/s13634-016-0408-1

Cited by 20 publications

(22 citation statements)

References 40 publications

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“…The target signal consists of its complex amplitude, αq, and steering vector, = exp − 2 , where fq is the normalized Doppler frequency of the target, obtained by the projection of the target velocity onto the target-transmitter and target-receiver line-of-sight [3].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Performance Evaluation of Two Multistatic Radar Detectors on Real and Simulated Sea-Clutter Data

Palamà

Rosenberg

Griffiths

2018

2018 International Conference on Radar (RADAR)

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In this paper we evaluate the performance of two multistatic radar detection schemes on real and syntheticallygenerated sea clutter data. The analysis utilizes both monostatic and bistatic radar data, simultaneously collected by the nodes of the NetRAD system. The first contribution is a demonstration of how the evolved Doppler spectrum model can accurately simulate multistatic sea-clutter with statistical and spectral properties which match the real data. The second contribution is an analysis of the detection performance obtained from different bistatic angles and a demonstration that the results from both real and simulated data are comparable.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…In this work, the clutter covariance matrix was estimated using the normalized sample covariance matrix estimate [10] which has been shown to achieve a better accuracy with respect to alternative methods [3]. It is given by…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Performance Evaluation of Two Multistatic Radar Detectors on Real and Simulated Sea-Clutter Data

Palamà

Rosenberg

Griffiths

2018

2018 International Conference on Radar (RADAR)

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“…Since sea clutter possesses obviously non‐stationary nature, the performance of the ordinary CFAR‐based detection algorithms [3, 4] is highly dependent on the choice of the neighbourhood size and the guard region around the target. Due to its low dependence on domain‐specific knowledge, the back propagation algorithm for supervised learning with multilayered feed‐forward NN is utilised to classify the received radar signal into the two hypotheses in (15).…”

Section: Multilayered Feed‐forward Nn‐based Classifiermentioning

confidence: 99%

“…Conventionally, most detection schemes apply thresholding tests strategy to target identification, particularly the constant false alarm rate (CFAR)‐based algorithm [3, 4], on the basis of statistical properties of the sea clutter. These schemes consume much processing time to integrate the echo energy of numerous scans to average the clutter.…”

Section: Introductionmentioning

confidence: 99%

Sea‐surface weak target detection scheme using a cultural algorithm aided time‐frequency fusion strategy

Cai

Min

Liu

2018

IET Radar, Sonar & Navigation

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“…Therefore, the method of clutter simulation based on a specific parameter model is particularly important in radar system analysis and performance verification [3,4]. Because of the complexity and rapidity of the sea condition, the research of sea surface remote sensing also presents the development trend from monostatic radar to multistatic radar [5,6], from conventional band radar to microwave radar [7], and from traditional radar to multi-element radar [8,9]. Compound Gaussian distribution, such as K distribution and Pareto distribution, which can better describe many kinds of clutter with high resolution and low grazing angle, has becoming a crucial statistical model for clutter simulation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters

Chen

2020

Sensors

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Sea clutter simulation is a well-known research endeavour in radar detector analysis and design, and many approaches to it have been proposed in recent years, among which zero memory non-linear (ZMNL) and spherically invariant random process (SIRP) are the most two widely used methods for compound Gaussian distribution. However, the shape parameter of the compound Gaussian clutter model cannot be a non-integer nor non-semi-integer in the ZMNL method, and the computational complexity of the SIRP method is very high because of the complex non-linear operation. Although some improved methods have been proposed to solve the problem, the fitting degree of these methods is not high because of the introduction of Beta distribution. To overcome these disadvantages, a novel Gamma distributed random variable (RV) generation method for clutter simulation is proposed in this paper. In our method, Gamma RV with non-integral or non-semi-integral shape parameters is generated directly by multiplying an integral-shape-parameter Gamma RV with a Beta RV whose parameters are larger than 0.5, thus avoiding the deviation of simulation of Beta RV. A large number of simulation experimental results show that the proposed method not only can be used in the clutter simulation with a non-integer or non-semi-integer shape parameter value, but also has higher fitting degree than the existing methods.Sensors 2020, 20, 955 2 of 11 of its large computation. A method based on zero memory non-linear (ZMNL) is proposed in [12]. The ZMNL is suitable for non-coherent clutter model, and the principle of it is simple. However, the main drawback of ZMNL is that the shape parameter of the clutter model must be an integer or semi-integer. If the shape parameter does not satisfy the requirement, the ZMNL method will lead to clutter simulation error. What's worse, the ZMNL cannot control the power spectrum and amplitude independently. With the principle that each quadrature component of K-distributed clutter can be modelled, exactly or approximately, by a weighted sum of products of two independent Gaussian variables in [13], a new method for modelling and simulation of correlated K-distributed clutter is proposed in [14]; the new method can simulate the clutter with arbitrary and specified power spectrum, and is a much more simple calculation compared with the traditional ZMNL and SIRP, which makes it easier to realize both in software and hardware. Nevertheless, an approximate approach is made for the simulation when the shape parameter is non-integer or non-semi-integer. Besides, a method of simulation of coherent compound Gaussian clutter based on memorials non-linear transformation (MNLT) is proposed in [15]. Through the generation of arbitrary correlated Gamma random variable (RV) by MNLT, this method can simulate clutter specifically. However, similar to SIRP method, the computational complexity of MNLT is high due to nonlinear computation.To solve this problem, the additive property of Gamma RV is used to improve the ZMNL and SIRP methods in [16,17...

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Multistatic adaptive CFAR detection in non-Gaussian clutter

Cited by 20 publications

References 40 publications

Performance Evaluation of Two Multistatic Radar Detectors on Real and Simulated Sea-Clutter Data

Performance Evaluation of Two Multistatic Radar Detectors on Real and Simulated Sea-Clutter Data

Sea‐surface weak target detection scheme using a cultural algorithm aided time‐frequency fusion strategy

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters

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