Coherent Detection of Swerling 0 Targets in Sea-Ice Weibull-Distributed Clutter Using Neural Networks

Vicen-Bueno, R.; Rosa-Zurera, Manuel; Jarabo-Amores, María-Pilar; Mata-Moya, David

doi:10.1109/tim.2010.2047579

Cited by 19 publications

(17 citation statements)

References 31 publications

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“…In [19], RBF and Multi Layer Perceptron (MLP) networks were used, trained with different K-distribution shape parameters and Signal to Clutter Ratios (SCR), believed to be a good description of expected clutter and signal statistics. In [20], sea-ice clutter data were analyzed and used to train neural network with the result that such trained network, applied in coherent detection of non-fluctuating target echo embedded in Weibull sea-ice clutter, demonstrated enhanced detection and robustness compared to a conventional approximation of optimum Neyman-Pearson likelihood ratio detector. Amplitude statistics of the returned echoes and their temporal correlations were used in shore-based and non-coherent surveillance radar for detection of land areas and their exclusion from monitoring.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Vondra¹,

Bonefačić²

2020

RADIOENGINEERING

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“…Though regression is a statistical process for estimating the relationships among variables, it can be used for computing arbitrary functions. Being available and due to the increasing computing power, the ANN usage has been accelerated in almost every domain of computer science [27,28]. A neural network may be a good solution to analyze the sensor data described in (i) to estimate a physical quantity [29].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Sensor Array Training Sets for Neural Networks

2019

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It is often hard to relate the sensor’s electrical output to the physical scenario when a multidimensional measurement is of interest. An artificial neural network may be a solution. Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources. The same issue arises when the physical measurement is expected to extend across a wide range of values. This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network. Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest. We discuss to which systems our method may be applied. We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array. We present the experimental process, its results, and the resulting accuracies.

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“…There have been many studies in the literature about radar target detection [148][149][150][151][152][153][154][155]169] and recognition [70,[159][160][161][162][163][164][165][166]. The first set of works presents how different techniques have been successfully applied to detect radar targets in different environments such as sea [152,153] and ground [154,155].…”

Section: Introductionmentioning

confidence: 99%

“…The first set of works presents how different techniques have been successfully applied to detect radar targets in different environments such as sea [152,153] and ground [154,155]. The typical techniques are neural-network based detectors [148,149] and constant false alarm rate (CFAR) detectors [150,151]. The works of the second set [148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163][164] present how the above-mentioned or other techniques have been successfully applied in radar target recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

“…The typical techniques are neural-network based detectors [148,149] and constant false alarm rate (CFAR) detectors [150,151]. The works of the second set [148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163][164] present how the above-mentioned or other techniques have been successfully applied in radar target recognition tasks. Natural frequency is a commonly used radar signature for radar target recognition [159][160][161][162][163][164][165][166][167].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Threshold Value on the Performance of Natural Frequency-Based Radar Target Recognition

Cho¹,

Lee²

2013

PIER

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Abstract-In this paper, the performance analysis of the natural frequency-based radar target recognition in the time domain is considered. We investigate the dependence of the probability of correct classification on a specific threshold value, and determine the optimum threshold value for two targets, and the sub-optimal threshold for multiple targets to maximize the probability of correct classification. Based on the probability density function (PDF) of some quantity consisting of the projections of the late time response onto the column spaces of the matrices constructed using the natural frequencies of the specific targets, we propose how to determine an optimum threshold in the sense that the probability of correct classification of two targets is maximized. By extending the scheme for two targets, we show how to determine a threshold value close to the optimal threshold for multiple targets. The scheme is validated by comparing the performance using the analytic method with that using the Monte-Carlo simulation.

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Coherent Detection of Swerling 0 Targets in Sea-Ice Weibull-Distributed Clutter Using Neural Networks

Cited by 19 publications

References 31 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

Synthetic Sensor Array Training Sets for Neural Networks

Effect of Threshold Value on the Performance of Natural Frequency-Based Radar Target Recognition

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