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].

The Canadian seismic array monitor processing system (cansam)

1976 ◽  
Vol 66 (4) ◽  
pp. 1381-1403
Author(s):  
D. H. Weichert ◽  
M. Henger
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection System ◽  
Detection Threshold ◽  
Processing System ◽  
Digital Beamforming ◽  
Canadian Experience ◽  
Great Slave Lake ◽  
Short Period ◽  
On Line

abstract An on-line digital beamforming and detection system has been operating at the Canadian short-period array near Yellowknife since early 1974. The installation followed the renewed recognition of the important contribution this array can make to worldwide detection and location of small seismic events. System design is based on earlier Canadian experience. Daily event detection logs are available in Ottawa via dialed telephone line. Nonlinear beamforming is used to lower the false alarm rate at given detection level, and it is shown to perform as predicted. During the 6 winter and spring months, the detection threshold is mb 4.0 to 4.1 at 50 per cent false alarm rate. During summer and fall, organized surface-wave noise originating on Great Slave Lake raises this level to mb 4.6 to 4.7.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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 Increasing the Radar Equivalent Energy Potential by “Track Before Detect” Method

2021 ◽  
Vol 65 (4) ◽  
pp. 404-411
Author(s):  
S. M. Kostromitsky ◽  
V. M. Artemiev ◽  
D. S. Nefedov
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Probability ◽  
Strong Dependence ◽  
Detection Threshold ◽  
Probability Of Detection ◽  
False Alarms ◽  
Energy Potential ◽  
Observation Area ◽  
Track Before Detect

The problem of radar detection of small-sized targets using the traditional methods of selection of signals embedded in background noise is considered. It is shown that for a false alarm rate of 10–5, which provides for 1–2 false alarms within the entire coverage of the modern 3D radar, the probability of detection of a small-sized target is getting unacceptably low. Repeatedly decreasing the threshold can provide an acceptable level of the detection probability at ultra-low signal-tonoise ratio (SNR) values. At the same time, decreasing the threshold will result in an unacceptable increase of the false alarm rate. A new target detection procedure using the “track before detect” method (TBD) is proposed. In the TBD procedure, the target is considered detected when two conditions are met: the signal exceeds once a definite threshold; the target is detected within a strictly defined observation area (acquisition or tracking gate). For low SNR values in the range of 3–8 dB and equal false alarm rate, the detection probability increases by 20–50 % compared to the traditional detection method. The simulation results showed a strong dependence of efficacy of the TBD algorithm on the threshold value and the decision rule. The possibility is noted of adaptive control over the threshold due to the use the detection results in the preceding scanning cycles, as well as the introduction of matrix radar surveillance not only by the target coordinates and parameters, but also by the detection threshold, decision rules, etc. Examination of these issues is the subject of further research.

A Constant False Alarm Rate-(CFAR) Based Change Detection Approach to Helicopter Diagnostics

2008 ◽  
Author(s):  
Kenneth Ranney ◽  
Hiralal Khatri ◽  
Jerry Silvious ◽  
Kwok Tom ◽  
Romeo del Rosario
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Detection Approach

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.

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