scholarly journals Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1482
Author(s):  
Jiafei Zhao ◽  
Rongkun Jiang ◽  
Xuetian Wang ◽  
Hongmin Gao
Keyword(s):  
Machine Learning ◽  
False Alarm ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Optimal Parameter ◽  
False Alarm Probability ◽  
Masking Effect ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Cfar Detection

For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

scholarly journals Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

2021 ◽  
Vol 13 (19) ◽  
pp. 3856
Author(s):  
Xiaolong Chen ◽  
Jian Guan ◽  
Xiaoqian Mu ◽  
Zhigao Wang ◽  
Ningbo Liu ◽  
...  
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
False Alarm Probability ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Detection Model ◽  
Radar Images ◽  
Frame Fusion ◽  
Target Characteristics

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

scholarly journals Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance

2019 ◽  
Vol 8 (2) ◽  
pp. 28 ◽  
Author(s):  
Xiao-Li Hu ◽  
Pin-Han Ho ◽  
Limei Peng
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
False Alarm Probability ◽  
Energy Detection ◽  
Radio Spectrum ◽  
Statistical Properties ◽  
Constant False Alarm Rate ◽  
Noise Variance ◽  
Total Noise ◽  
Noise Receiver

In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments.

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

Erratum

1993 ◽  
Vol 46 (3) ◽  
pp. 447-447
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Signal Differentiation ◽  
Signal Vidéo ◽  
Video Amplifier ◽  
Marine Navigation ◽  
Adaptive Signal

There was an error in Mr Richard Trim's paper, ‘Some causes of problems in the observation of standard racon marine beacons when observed by means of standard marine navigation radars’, which was published in the May 1993 issue of the Journal of Navigation. Section 3, paragraph 4 of page 276 should read:‘A third and very important cause of radar received-signal differentiation arises if a widely used form of automatic anti-sea-clutter processing is employed, since part of this processing is to differentiate the radar-received video so as to remove the d.c. term in the sea clutter echoes as part of the Constant False Alarm Rate (CFAR) processing. When such automatic sea clutter supression facilities are in operation, the gain level applied to the radar receiver video amplifier has an adaptive signal superimposed upon it which, while slow acting, generally follows the shape of the clutter returns on the received signal video, while being largely unaffected by the wanted echo returns such as those from ships, navigation marks, coastlines, etc. This effect may be reduced in the case of the very latest radar designs’.

Detection Performance of Several Nonparametric Detectors under K-Distributed Clutter

2012 ◽  
Vol 433-440 ◽  
pp. 6417-6421
Author(s):  
Fu Yong Qu ◽  
Xiang Wei Meng
Keyword(s):  
False Alarm ◽  
False Alarm Probability ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Multiple Targets ◽  
Input Noise ◽  
Simulation Results ◽  
Homogeneous Background ◽  
Detector Parameter ◽  
Parameter Values

Because of nonparametric detectors’ ability of ensuring constant false alarm rate (CFAR) for a wide class of input noise distributions and engineering implementation simply, much efforts have been directed towards the study of nonparametric methods of signal detection. This paper deals with a comparative analysis of nonparametric detectors-GS, MW, Savage detector under K-distributed clutter in homogeneous and nonhomogeneous background caused by multiple targets and clutter edge. Some results of detection probability versus signal-to-clutter ratio (SCR) are presented in curves for different detector parameter values in homogeneous and multiple targets background. And the ability to control the false alarm probability for the three nonparametric detectors is presented in table. The simulation results show that S detector performs robustly in homogeneous background and clutter edge background, and can tolerate more interfering targets through increasing the number of reference cells and pulse sweeps. Therefore as a compromise solution, S detector with moderate parameters can be used in actual radar system.

Detection performance characteristics of the digital undersampling receiver

2021 ◽  
pp. 95-107
Author(s):  
A.V. Smolyakov ◽  
A.S. Podstrigaev
Keyword(s):  
False Alarm ◽  
Mathematical Description ◽  
Detection Threshold ◽  
False Alarm Probability ◽  
Single Frequency ◽  
Constant False Alarm Rate ◽  
Detector Performance ◽  
Pearson Criterion ◽  
Early Stages ◽  
Signal Detector

Multichannel digital receivers based on the signal processing technology involving undersampling are used for the instantaneous wideband analysis of the electronic environment. One of the most common algorithms for measuring input signal’s carrier frequency in such receivers includes unfolding of the signal’s spectrums from the first Nyquist zone of all receiver’s channels to the single frequency axis and searching for the frequency where the spectrum components from all of the receiver’s channels coincided. Performance of the signal detector, which uses this algorithm in its operation, was not studied. In the absence of a mathematical description of such a detector, evaluating the digital undersampling receiver’s sensitivity becomes possible only in the late stages of prototyping when it can be done through experimental study. Additionally, it is impossible to set a detection threshold in the receiver according to the Neyman-Pearson criterion, which hardens building constant false alarm rate (CFAR) systems based on this type's receivers. This paper aims to develop the mathematical description of the digital undersampling receiver's detector and then, using this model, to get expressions and computer models to evaluate the characteristics of such receiver even in early stages of its development. This paper's main result is the developed mathematical tools necessary to evaluate the multichannel digital undersampling receiver’s signal detector performance. It is shown that the false alarm probability in such a detector does not exceed some value no matter how small the detection threshold is. The expression for evaluating the maximum false alarm probability by the receiver’s parameters is also presented in the paper alongside the true positive rate plots as a function of signal-to-noise ratio for the three-channel receiver. These results can be used in evaluating the digital undersampling receiver’s characteristics in the early stages of its development. It allows one to choose optimal values of the receiver’s parameters which are hard and expensive to change after prototyping is done, and there is an opportunity to evaluate the receiver’s characteristics experimentally. Moreover, the obtained mathematical expressions make it possible to set the receiver's detection threshold according to the Neyman-Pearson criterion and build on its base a CFAR-systems widely used for wideband signal analysis.

scholarly journals CFAR Strategy Formulation and Evaluation Based on Fox’s H-function in Positive Alpha-Stable Sea Clutter

2020 ◽  
Vol 12 (8) ◽  
pp. 1273 ◽  
Author(s):  
Xu Liu ◽  
Shuwen Xu ◽  
Shiyang Tang
Keyword(s):  
False Alarm ◽  
Density Function ◽  
Closed Form Expression ◽  
Suitable Model ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Strategy Formulation ◽  
Non Gaussian ◽  
Probability Density Function Pdf ◽  
Fox’S H Function

The problem of target detection in impulsive non-Gaussian sea clutter has attracted a lot of attention in recent years. The positive alpha-stable (PαS) distribution has been validated as a suitable model for the impulsive non-Gaussian sea clutter. Since the probability density function (PDF) of the PαS variable cannot be expressed as a closed-form expression, the research into constant false alarm rate (CFAR) detectors in PαS distributed sea clutter is limited. This paper formulates and evaluates some CFAR detectors, such as Greatest Of-CFAR (GO-CFAR), Smallest Of-CFAR (SO-CFAR), Order Statistic-CFAR (OS-CFAR) and censored mean level (CML) detectors, in PαS distributed sea clutter. Firstly, the Fox’s H-function is adopted to express the PDF of the PαS variable, and the cumulative density function based on Fox’s H-function is derived in this paper. Then, by use of the properties of the H-function and PαS distribution, exact expressions of the probabilities of false alarm and detection for CFAR detectors in the PαS background are derived. Some CFAR properties of these detectors in the PαS background are also explored. Numerical results based on derived expressions are given and verified by Monte Carlo simulation. Some analyses of detection performance from a practical perspective are also given.

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