Robust CFAR Detection for Multiple Targets in KDistributed Sea Clutter Based on Machine Learning
Abstract
:1. Introduction
 We propose a novel CFAR processor based on the DBSCAN clustering algorithm, termed DBSCANCFAR, to eliminate outliers in the leading and lagging windows that are symmetrical about the CUT. Without a priori knowledge on the number and distribution of interference targets, the DBSCANCFAR can achieve an accurate estimation of the clutter background level for multipletarget scenarios. The configuration parameters of DBSCAN are predetermined according to the characteristics of the clutter data to ensure realtime performance of the detector.
 We design and train an ameliorative ANN model with a symmetrical architecture to evaluate the shape parameter of Kdistributed sea clutter with high precision. Through deriving the numerical relationship between the threshold factor and the shape parameter under different false alarm probabilities, the optimal parameter estimation value offered by the ANN method is instrumental in maintaining the CFAR property for DBSCANCFAR.
 We demonstrate the effectiveness and superiority of the proposed ANNbased DBSCANCFAR processor over several relevant competitors with respect to the variation of interference target numbers, shape parameters, and false alarm probabilities through extensive simulations. This performance improvement is at the expense of time that elapses.
2. Preliminary Theories
2.1. KDistributed Sea Clutter Model
2.2. CFAR Detection
2.3. Shape Parameter Estimation
3. Proposed Methods
3.1. DBSCANCFAR Processor
Algorithm 1 Proposed DBSCANCFAR Processor 
Input: Number of reference cells N, number of guard cells M, complex samples of radar return in reference window $\left[{x}_{1I}+j{x}_{1Q},\cdots ,{x}_{NI}+j{x}_{NQ}\right]$, and clustering parameters of $Eps$ and $MinPts$. Output: Target detection result (${H}_{1}$ or ${H}_{0}$).

3.2. ANNBased Shape Parameter Estimation
4. Results and Analysis
4.1. Impact of Parameter Estimation Accuracy
4.2. Impact of Interference Target Number
4.3. Impact of Various Shape Parameter
4.4. Impact of False Alarm Probability
4.5. Analysis of Time Elapsed
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ANN  Artificial neural network 
BN  Batch normalization 
BPNN  Back propagation neural network 
CACFAR  Cell averaging constant false alarm rate 
CFAR  Constant false alarm rate 
CMLDCFAR  Censored mean level detector constant false alarm rate 
CUT  Cell under test 
DBSCAN  Densitybased spatial clustering of applications with noise 
GOCFAR  Greatestof constant false alarm rate 
ICR  Interferingtoclutter ratio 
LOF  Local outlier factor 
LReLU  Leaky rectified linear unit 
MLE  Maximum likelihood estimation 
MOM  Method of moments 
OSCFAR  Ordered statistics constant false alarm rate 
Probability density function  
ReLU  Rectified linear unit 
RMSE  Root mean squared error 
SAR  Synthetic aperture radar 
SCR  Signaltoclutter ratio 
SOCFAR  Smallestof constant false alarm rate 
Tanh  Hyperbolic tangent 
TSCFAR  Truncated statistic constant false alarm rate 
VICFAR  Variability index constant false alarm rate 
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Parameter  Value 

Training function  LevenbergMarquardt 
Error measurement  RMSE 
Presentation of samples  Batch training 
Size of minibatch  $1\times {10}^{3}$ 
Maximum number of epochs  20 
Frequency of network validation  50 
Initial learning rate  $1\times {10}^{3}$ 
Number of epochs for dropping the learning rate  5 
Factor for dropping the learning rate  0.3 
Activation Function  Mathematical Expression  Training Error  Training Time (s) 

Sigmoid [48]  $f\left(x\right)=\frac{1}{1+{e}^{x}}$  0.0753  930 
Tanh [48]  $f\left(x\right)=\frac{{e}^{x}{e}^{x}}{{e}^{x}+{e}^{x}}$  0.0795  941 
ReLU [49]  $f\left(x\right)=\mathrm{max}(0,x)$  0.0649  821 
LReLU [50]  $f\left(x\right)=\left\{\begin{array}{c}x,\phantom{\rule{4pt}{0ex}}x\ge 0\hfill \\ 0.01x,\phantom{\rule{4pt}{0ex}}x<0\hfill \end{array}\right.$  0.0643  845 
Method  Result  $0.1\le \mathit{v}\le 1$  $1<\mathit{v}\le 2$  $2<\mathit{v}\le 30$  $0.1\le \mathit{v}\le 30$ 

Best  $1.77\times {10}^{5}$  $9.08\times {10}^{6}$  $1.26\times {10}^{4}$  $9.67\times {10}^{5}$  
MOM12 [38]  Worst  1.6411  1.0543  0.9334  13.0210 
Mean  1.2209  0.4716  0.8009  0.8595  
Best  $6.93\times {10}^{7}$  $1.94\times {10}^{6}$  $1.15\times {10}^{6}$  $4.35\times {10}^{6}$  
MOM24 [38]  Worst  0.4589  1.0784  5.9198  20.9395 
Mean  0.0494  0.1281  0.6145  0.4860  
Best  $3.23\times {10}^{6}$  $1.18\times {10}^{7}$  $6.03\times {10}^{6}$  $2.70\times {10}^{7}$  
BPNN [27]  Worst  5.5290  1.9183  1.8608  21.4984 
Mean  0.1097  0.3019  0.2052  0.2088  
Best  $5.58\times {10}^{7}$  $2.94\times {10}^{6}$  $2.66\times {10}^{6}$  $1.35\times {10}^{6}$  
ANN [proposed]  Worst  0.9631  3.3025  1.7511  1.7246 
Mean  0.0474  0.1102  0.1672  0.1601 
Processor  Runtime (ms)  

ANN  BPNN  MOM12  MOM24  
CACFAR  81.1  80.6  47.8  55.8 
SOCFAR  79.3  78.8  46.0  54.0 
GOCFAR  78.4  77.9  45.1  53.1 
OSCFAR  79.6  79.1  46.3  54.3 
CMLDCFAR  81.7  81.2  48.4  56.4 
DBSCANCFAR  325.1  324.6  291.8  299.8 
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Zhao, J.; Jiang, R.; Wang, X.; Gao, H. Robust CFAR Detection for Multiple Targets in KDistributed Sea Clutter Based on Machine Learning. Symmetry 2019, 11, 1482. https://doi.org/10.3390/sym11121482
Zhao J, Jiang R, Wang X, Gao H. Robust CFAR Detection for Multiple Targets in KDistributed Sea Clutter Based on Machine Learning. Symmetry. 2019; 11(12):1482. https://doi.org/10.3390/sym11121482
Chicago/Turabian StyleZhao, Jiafei, Rongkun Jiang, Xuetian Wang, and Hongmin Gao. 2019. "Robust CFAR Detection for Multiple Targets in KDistributed Sea Clutter Based on Machine Learning" Symmetry 11, no. 12: 1482. https://doi.org/10.3390/sym11121482