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

The detection of Swerling 0 targets in movement in sea-ice Weibull-distributed clutter by neural networks (NNs) is presented in this paper. Synthetic data generated for typical sea-ice Weibull parameters reported in the literature are used. Due to the capability of NNs for learning the statistical properties of the clutter and target signals during a supervised training, high clutter reduction rates are achieved, reverting on high detection performances. The proposed NN-based detector is compared with a reference detector proposed in the literature that approximates the Neyman-Pearson (NP) detector. The results presented in the paper allow empirically demonstrating how the NN-based detector outperforms the detector taken as reference in all the cases under study. It is achieved not only in performance but also in robustness with respect to changes in sea-ice Weibull-distributed clutter conditions. Moreover, the computational cost of the NN-based detector is very low, involving high signal processing speed.

[1]  Jesús Grajal,et al.  Convolutional Neural Networks for Radar Detection , 2002, ICANN.

[2]  Robert J. Inkol,et al.  On the Performance Gain of the FFT Filter-Bank-Based Summation and Majority CFAR Detectors , 2009, IEEE Transactions on Instrumentation and Measurement.

[3]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[4]  Uday B. Desai,et al.  Radial basis function neural network for pulse radar detection , 2007 .

[5]  Jr . L. James Marier,et al.  Correlated K-distributed clutter generation for radar detection and track , 1995 .

[6]  A. Farina,et al.  Radar Detection of Correlated Targets in Clutter , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[7]  V. Anastassopoulos,et al.  Optimal CFAR detection in Weibull clutter , 1995 .

[8]  Gareth J. Janacek,et al.  Monte Carlo simulation and random number generation , 1988, IEEE J. Sel. Areas Commun..

[9]  M. Sekine,et al.  Weibull radar clutter , 1990 .

[10]  R Vicen-Bueno,et al.  Sea clutter power reduction in radar measurement systems by feedforward multilayer perceptrons with medium input data integration rate , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[11]  A. Farina,et al.  Radar detection of target signals in non- Gaussian clutter: theory and applications , 1987 .

[12]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[13]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[14]  Viswanath Ramamurti,et al.  Neural networks for signal detection in non-Gaussian noise , 1997, IEEE Trans. Signal Process..

[15]  P. Swerling.  Radar probability of detection for some additional fluctuating target cases , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[16]  G. B. Goldstein,et al.  False-Alarm Regulation in Log-Normal and Weibull Clutter , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Kai-Bor Yu,et al.  Modelling and simulation of coherent Weibull clutter , 1989 .

[18]  Manuel Rosa-Zurera,et al.  MLP and RBFN for Detecting White Gaussian Signals in White Gaussian Interference , 2003, IWANN.

[19]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[20]  Manuel Rosa-Zurera,et al.  Low Complexity MLP-Based Radar Detector: Influence of the Training Algorithm and the MLP Size , 2007, IWANN.

[21]  Francisco López-Ferreras,et al.  Combining MLPs and RBFNNs to Detect Signals With Unknown Parameters , 2009, IEEE Transactions on Instrumentation and Measurement.

[22]  Sergio Barbarossa,et al.  Theory of radar detection in coherent Weibull clutter , 1987 .

[23]  Manuel Rosa-Zurera,et al.  Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System , 2009, Sensors.

[24]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[25]  T. Foreman,et al.  Multiple pulse detection in distributed Rayleigh clutter , 2000, Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037].

[26]  Alfonso Farina,et al.  Coherent radar detection in log-normal clutter , 1986 .

[27]  Alfonso Farina.  Linear and non-linear filters for clutter cancellation in radar systems , 1997, Signal Process..

[28]  Mourad Barkat,et al.  Performance analysis of ML-CFAR detection for partially correlated chi-square targets in Rayleigh correlated clutter and multiple-target situations , 2006 .

[29]  O.K. Ersoy,et al.  Neural network learning of low-probability events , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[30]  Amir Zaimbashi,et al.  Weighted order statistic and fuzzy rules CFAR detector for Weibull clutter , 2008, Signal Process..

[31]  Faouzi Soltani,et al.  Threshold optimization of decentralized CFAR detection in weibull clutter using genetic algorithms , 2008, Signal Image Video Process..

[32]  Manuel Rosa-Zurera,et al.  NN-Based Detector for Known Targets in Coherent Weibull Clutter , 2006, IDEAL.

[33]  A. Farina,et al.  Radar detection in coherent Weibull clutter , 1987, IEEE Trans. Acoust. Speech Signal Process..

[34]  Francisco López-Ferreras,et al.  Study of Two Error Functions to Approximate the Neyman–Pearson Detector Using Supervised Learning Machines , 2009, IEEE Transactions on Signal Processing.

[35]  Bin Liu,et al.  A Modified RBF Neural Network and Its Application in Radar , 2007, ISNN.

[36]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[37]  Tayeb A. Denidni,et al.  CA-CFAR detection performance of radar targets embedded in "non centered chi-2 gamma" clutter , 2008 .

[38]  R Vicen-Bueno,et al.  Medium-high correlated Weibull-distributed clutter reduction by neural networks in coherent radar systems , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.