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.

scholarly journals An Anti-FOD Method Based on CA-CM-CFAR for MMW Radar in Complex Clutter Background

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1635 ◽  
Author(s):  
Xiaoqi Yang ◽  
Kai Huo ◽  
Jianwei Su ◽  
Xinyu Zhang ◽  
Weidong Jiang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Millimeter Wave ◽  
Radar System ◽  
Experimental Results ◽  
Foreign Object ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Homogeneous Background ◽  
Distributed Test

Traditional constant false alarm rate (CFAR) methods have shown their potential for foreign object debris (FOD) indication. However, the performance of these methods would deteriorate under the complex clutter background in airport scenes. This paper presents a threshold-improved approach based on the cell-averaging clutter-map (CA-CM-) CFAR and tests it on a millimeter-wave (MMW) radar system. Clutter cases are first classified with variability indexes (VIs). In homogeneous background, the threshold is calculated by the student-t-distributed test statistic; under the discontinuous clutter conditions, the threshold is modified according to current VI conditions, in order to address the performance decrease caused by extended clutter edges. Experimental results verify that the chosen targets can be indicated by the t-distributed threshold in homogeneous background. Moreover, effective detection of the obscured targets could also be achieved with significant detectability improvement at extended clutter edges.

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.

Multi-Sensor N-P Criterion Fusion Detection Based on Weighting by SNR

2014 ◽  
Vol 1044-1045 ◽  
pp. 818-824
Author(s):  
Bo Fan Yang ◽  
Rui Wang ◽  
Gang Wang ◽  
Li Zhao
Keyword(s):  
Signal Detection ◽  
False Alarm ◽  
Sensor Fusion ◽  
Detection Probability ◽  
Detection System ◽  
False Alarm Probability ◽  
Radar Target ◽  
Low Snr ◽  
Simulation Results ◽  
Fusion Detection

Aiming at signal detection of radar target, concerning about on the basis of the influence of SNR on detection probability when false alarm probability is given based on N-P criterion, a kind of multi-sensor fusion detection based on SNR is put forward. It can improve system’s detection probability under the condition of required false alarm probability in the detection of low SNR signal. The simulation results show that the detection performance is significantly increased, no matter fusion detection system is composed of same sensors working in the same working point or different sensors.

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.

A Reliable Spectrum Sensing Strategy Based on Multiple-Antenna Technique

2013 ◽  
Vol 765-767 ◽  
pp. 2305-2308
Author(s):  
Shou Tao Lv ◽  
Ze Yang Dai ◽  
Jian Liu
Keyword(s):  
Performance Analysis ◽  
False Alarm ◽  
Closed Form ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Multiple Antenna ◽  
Single Antenna ◽  
Simulation Results ◽  
Present Simulation

In this paper, we propose a reliable spectrum sensing strategy based on multiple-antenna technique, called RSS-MAT, to combat the channel uncertainties. We derive the closed-form expressions of the false alarm probability and detection probability for RSS-MAT. Finally, we present simulation results to validate our performance analysis. As expected, the simulation results show that RSS-MAT outperforms the spectrum sensing strategy with single antenna.

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 Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4162
Author(s):  
Alberto Izquierdo ◽  
Lara del Val ◽  
Juan J. Villacorta
Keyword(s):  
False Alarm ◽  
Detection Probability ◽  
Light Propagation ◽  
Microphone Array ◽  
Pedestrian Detection ◽  
False Alarm Probability ◽  
Constant False Alarm Rate ◽  
Sound System ◽  
Emergency Braking ◽  
Mems Microphone

Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.

scholarly journals Design and Implementation of a New CFAR Based on Weighted and Statistical Algorithms

2020 ◽  
Author(s):  
Thamir Saeed ◽  
Gufran Hatem ◽  
Jafar Abdul Sadah ◽  
Hadi Ziboon
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Simulation Analysis ◽  
Adaptive Threshold ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Receiver Side ◽  
Design And Implementation ◽  
Statistical Algorithms

In the radar system, detection represents a basic and important stage in the receiver side. The detection process is based on the thresholding criteria; two philosophies of this criteria, constant and adaptive threshold. The constant threshold is simple in design, but it has a mis-detection and does not control the false alarm rate. As for the adaptive threshold, it is powerful in target detection, and better control of the false alarm rate, where it is called Constant False Alarm Rate (CFAR). Lots of research in the CFAR design, but the gap in the previous works is that there is no CFAR algorithm can be working with all or most environmental fields and all or most target situations.In this paper, The CFAR, which can work with the most environment and most of the target situations, has been presented. The producing the design and implementation of the new practical CFAR processor is presented. Where, the new CFAR is a combination of the properties of three different CFAR algorithm (CA, OSGO, and OSSO), and from two different families; averaging and statistical. Where it has overperformed of it's is 97.25% for simulation and 96.25% for the implementable version for different target situations. The simulation analysis is made by using Matlab 2015, while the implementation is done by using Xilinx Spartan 700 3a.

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.

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