scholarly journals Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3904
Author(s):  
Jeong Hoon Shin ◽  
Youngjin Choi
Keyword(s):  
Robust Control ◽  
False Alarm ◽  
Detection Threshold ◽  
Detection Performance ◽  
Time Interval ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Probability Of False Alarm ◽  
Feedback Structure ◽  
Improved Performance

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

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).

scholarly journals Inhomogeneous Analysis of Novel Model of CFAR Approaches to Detect Two-Degrees of Freedom Partially-Correlated χ2-Targets

2021 ◽  
Vol 20 ◽  
pp. 28-43
Author(s):  
Mohamed Bakry El-Mashade
Keyword(s):  
False Alarm ◽  
Degrees Of Freedom ◽  
Detection Threshold ◽  
Detection Performance ◽  
Closed Form Expression ◽  
False Alarms ◽  
Artificial Noise ◽  
Constant False Alarm Rate ◽  
Two Degrees Of Freedom ◽  
Novel Strategy

Reliable and high performance radar systems have ubiquitous demand. The operation of such systems is affected by the presence of natural and artificial noise sources. One of the basic radar concepts is to decide whether the target is present or not. Meanwhile, the general objective of all radar detection schemes is to ensure that false alarms don't fluctuate randomly. Thus, to cope with an inhomogeneous changing clutter environment, it is beneficial to be able to detect both high- and low-fidelity targets while maintaining the rate of false alarm fixed. This calls for an adaptive thresholding strategy that vary the detection threshold as a function of the sensed environment, and most modern radars implement this approach automatically. The feature of constant false alarm rate (CFAR) activates the threshold in such a way that it becomes adaptive to the local clutter environment. Many alternatives have been proposed to achieve such demanded property. Owing to the diversity of the radar search environment (target multiplicity & clutter edges), there exists no universal CFAR procedure. This prompts the necessity to investigate the composite architecture as a novel strategy. The goal of this paper is to analyze the fusion of CA, OS, and TM processors in post-detection integration of M-pulses. The primary and outlying targets are assumed to obey χ 2 -distribution with two-degrees of freedom in their fluctuation. Closed-form expression is derived for the detection performance. Our simulation results show robust behavior of the new model in the absence as well as in the presence of outlying targets. In addition, a significant improvement of the detection performance of novel strategy over the individual CFAR detectors is noticed. Moreover, the outweighing, over Neyman-Pearson (N-P) detector, of the fusion model, in ideal background, is evidently demonstrated. This ability to obtain improved performance compared to existing models is the major contribution of this work.

scholarly journals Rao and Wald Tests for Adaptive Detection in Partially Homogeneous Environment with a Diversely Polarized Antenna

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Chaozhu Zhang ◽  
Jing Zhang ◽  
Chengyuan Liu
Keyword(s):  
False Alarm ◽  
Wald Test ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Adaptive Detection ◽  
Constant False Alarm Rate ◽  
Wald Tests ◽  
Probability Of False Alarm ◽  
Rao Test ◽  
Homogeneous Environment

This study considers Rao test and Wald test for adaptive detection based on a diversely polarized antenna (DPA) in partially homogeneous environment. The theoretical expressions for the probability of false alarm and detection are derived, and constant false alarm rate (CFAR) behaviour is remarked on. Furthermore, the monotonicities of detection probability of the two detectors are proved, and a polarization optimization detection algorithm to enhance the detection performance is proposed. The numerical simulations are conducted to attest to the validity of the above theoretical analysis and illustrate the improvement in the detection performance of the proposed optimization algorithm.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

AN AUTOMATIC EVENT DETECTOR AT THE TONTO FOREST SEISMIC OBSERVATORY

Geophysics ◽  
1974 ◽  
Vol 39 (5) ◽  
pp. 633-643 ◽  
Author(s):  
R. R. Blandford
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Kuril Island ◽  
Detection Threshold ◽  
Maximum Effect ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Statistical Bias ◽  
Short Period ◽  
On Line

The on‐line operation of an automatic event detector has been evaluated at the Tonto Forest Observatory short‐period seismic array. For 31 seismometers and one fixed threshold, the 90 percent incremental detection threshold on the Kuril Island beam, centered at Δ=70 degrees, is [Formula: see text] with an experimentally determined false alarm rate of 0.17 per day. This compares favorably with the capabilities of a human operator. Storms in the Kurils significantly affect the distribution of amplitudes of the F-statistic detection trace, and we estimate that most of the false alarms observed at the operating threshold can be traced to the statistical bias introduced by this storm‐generated energy. If the threshold were adjusted to maintain a constant false alarm rate, the maximum effect on the threshold magnitude would be [Formula: see text].

scholarly journals Parameter Estimation and Target Detection of Phased-MIMO Radar Using Capon Estimator

2020 ◽  
Vol 20 (2) ◽  
pp. 60
Author(s):  
Syahfrizal Tahcfulloh ◽  
Muttaqin Hardiwansyah
Keyword(s):  
Parameter Estimation ◽  
False Alarm ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Adaptive Estimator ◽  
Probability Of False Alarm

Phased-Multiple Input Multiple Output (PMIMO) radar is multi-antenna radar that combines the main advantages of the phased array (PA) and the MIMO radars. The advantage of the PA radar is that it has a high directional coherent gain making it suitable for detecting distant and small radar cross-section (RCS) targets. Meanwhile, the main advantage of the MIMO radar is its high waveform diversity gain which makes it suitable for detecting multiple targets. The combination of these advantages is manifested by the use of overlapping subarrays in the transmit (Tx) array to improve the performance of parameters such as angle resolution and detection accuracy at amplitude and phase proportional to the maximum number of detectable targets. This paper derives a parameter estimation formula with Capon's adaptive estimator and evaluates it for the performance of these parameters. Likewise, derivation for expressions of detection performance such as the probability of false alarm and the probability of detection is also given. The effectiveness and validation of its performance are compared to conventional estimator for other types of radars in terms of the effect of the number of target angles, the RCS of targets, and variations in the number of subarrays at Tx of this radar. Meanwhile, the detection performance is evaluated based on the effect of Signal to Noise Ratio (SNR) and the number of subarrays at Tx. The evaluation results of the estimator show that it is superior to the conventional estimator for estimating the parameters of this radar as well as the detection performance. Having no sidelobe makes this estimator strong against the influence of interference and jamming so that it is suitable and attractive for the design of radar systems. Root mean square error (RMSE) on magnitude detection from LS and Capon estimators were 0.033 and 0.062, respectively. Meanwhile, the detection performance for this radar has the probability of false alarm above 10-4 and the probability of detection of more than 99%.

scholarly journals Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

2021 ◽  
Vol 13 (21) ◽  
pp. 4315
Author(s):  
Zongyong Cui ◽  
Yi Qin ◽  
Yating Zhong ◽  
Zongjie Cao ◽  
Haiyi Yang
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
Target Detection ◽  
Detection Threshold ◽  
Graph Model ◽  
Conditional Entropy ◽  
Image Features ◽  
Feature Model ◽  
Sar Image ◽  
Constant False Alarm Rate

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

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 Performance Analysis of an Environmental Adaptive CFAR Detector

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Sung Won Hong ◽  
Dong Seog Han
Keyword(s):  
Performance Analysis ◽  
False Alarm ◽  
False Alarm Rate ◽  
Computer Simulations ◽  
Mathematical Analysis ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Constant False Alarm Rate ◽  
Sonar Systems

The constant false alarm rate (CFAR) is a detection algorithm that is generally used in radar or sonar systems, but its performance depends greatly on the environment. This means that the detection performance cannot be satisfied with only a single CFAR detector. This paper evaluates mathematically a proposed environmental adaptive (EA) CFAR detector. The proposed CFAR detector selects an optimal CFAR detector depending on the environment. Computer simulations validate the mathematical analysis and robustness of the detector in homogeneous and nonhomogeneous backgrounds.

Timing Acquisition with Cooperation of Two Distributed Receive Antennas over Flat-Fading Channels

2013 ◽  
Vol 347-350 ◽  
pp. 1965-1969
Author(s):  
Chao Jin Qing ◽  
Jin Cheng Wei ◽  
Ling Xia ◽  
Ying Liu ◽  
Chuan Hui Ma ◽  
...  
Keyword(s):  
False Alarm ◽  
Fading Channels ◽  
Detection Threshold ◽  
Mobile Station ◽  
False Alarm Probability ◽  
Central Processor ◽  
Timing Acquisition ◽  
Threshold Detection ◽  
Probability Of False Alarm ◽  
Distributed Antenna

In the flat Rayleigh channels of linear cell, two distributed receive antennas are employed to receive the signal transmitted from the mobile station (MS) with a single antenna. We exploit the false alarm probability at the central processor to guarantee that the false alarm probability at each distributed antenna does not exceed the pre-defined probability of false alarm. Based on the exploited probability of false alarm at the central processor, a cooperative detection threshold of each antenna is derived for threshold detection. According to the threshold detection, a maximum-likelihood (ML)-based timing acquisition method is proposed for distributed antenna systems (DAS). Without increasing the pre-defined probability of false alarm, the analysis and simulation results show that the correct acquisition probability and the missed detection probability for each distributed antenna can be improved with the proposed method wherever the MS is located.

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