Blind Detection with Deterministic Arrange and Scale to Underwater Acoustic Source

2013 ◽  
Vol 380-384 ◽  
pp. 854-859
Author(s):  
Wei Zhao ◽  
Bao An Hao
Keyword(s):  
Roc Curve ◽  
Detection Probability ◽  
Operating Characteristic ◽  
Signal To Noise Ratio ◽  
False Alarm Probability ◽  
Blind Detection ◽  
Acoustic Source ◽  
Signal To Noise ◽  
Distance Data ◽  
Ratio Detection

t is a valid anti-interference method that blindly separates the contaminated radiant acoustic source object from interferences, but the separated radiant acoustic source object with arrange and scale ambiguity makes the likelihood ratio detection impossible. This paper solves the ambiguity problems by utilizing the correlation and energy consistency of the same source in the same overlapped frame data; Known delay distance, data sum length and signal to noise ratio (SNR), the paper analyzes algorithms performance and draws the receiver operating characteristic (ROC) curve in different distance, which offers an accord to design detector parameters in different false alarm probability and detection probability.

scholarly journals Analysis Study of Radar Probability of Detection for Fluctuating and Non-fluctuating Targets

2017 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Naceur AOUNALLAH ◽  
Ali KHALFA
Keyword(s):  
Detection Probability ◽  
Signal To Noise Ratio ◽  
False Alarm Probability ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Statistical Techniques ◽  
Signal To Noise ◽  
Analysis Study ◽  
Graphical Simulation ◽  
Probability Number

The radar analyst can develop and use mathematical and statistical techniques that lead to accurate prediction or adapting models for estimating the target detection performance. In radar detection theory, detection probability, false alarm probability, number of samples non-coherently integrated for a detection test, and signal-to-noise ratio (SNR) are closely interrelated. The present paper is intended to provide an overview of the calculations of radar probability of detection and its related parameters. The main methods and procedures for predicting the detection performance of either non-fluctuating or fluctuating targets are described. Performance’s analysis of the studied models is included, along with some graphical simulation examples.

A New Approach for Coverage Area Computing of Radar Network

2011 ◽  
Vol 328-330 ◽  
pp. 1990-1994
Author(s):  
Shen Shen Wang ◽  
Wan Fang Che ◽  
Jin Fu Feng ◽  
Ming Zhong Li
Keyword(s):  
Detection Probability ◽  
Signal To Noise Ratio ◽  
Probability Model ◽  
False Alarm Probability ◽  
Coverage Area ◽  
Detection Range ◽  
New Approach ◽  
Radar Network ◽  
The Relationship ◽  
Ratio Detection

Traditional methods regard coverage area of radar network as the union of every radar’s coverage area. Aiming at this issue, the relationship among radar detection range, radar cross section, signal-to-noise ratio, detection probability and false alarm probability is analyzed. Detection probability model for single radar is established. Calculation method of detection probability for radar network is also researched. Coverage area of radar network can be obtained according to the detection probability. Simulation results show coverage area of radar network is not simply the union of every radar’s coverage area and it is decided by the detection probability. Research of this paper provides a theoretical base of detecting, tracking and placement for radar network.

scholarly journals On the application of frequency tagging

2020 ◽  
Author(s):  
Zoltan Derzsi
Keyword(s):  
Phase Noise ◽  
Detection Probability ◽  
Signal To Noise Ratio ◽  
Lower Probability ◽  
Weak Signal ◽  
Signal To Noise ◽  
Noise Component ◽  
Spectral Evaluation ◽  
Human Electrophysiology ◽  
Additional Phase

To detect a weak signal in human electrophysiology that is a response of a periodic external stimulus, spectral evaluation is mostly used. The recorded signal’s amplitude and phase noise components of the signal are statistically independent from each other, but both of them are decreasing the signal-to-noise ratio, which results in a lower probability of successful signal detection. Provided that the phase information of the stimuli is preserved, we found that a way to reject an additional phase noise component, which improves the detection probability considerably, by analysing the signal’s phase coherency instead of its spectrum.

ASSESSMENT OF TARGET DETECTION ABILITY FOR LASER FUZES IN FOG CONDITIONS

2021 ◽  
pp. 8-16
Author(s):  
Linh
Keyword(s):  
Target Detection ◽  
Detection Probability ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Evaluation Model ◽  
Laser System ◽  
Weather Conditions ◽  
Signal To Noise ◽  
Detection Ability ◽  
Bad Weather

The article presents a method to evaluate the target detection efficiency of laser fuzes operating in foggy conditions. The evaluation model is built from: the distance equation of the laser system, the attenuation of the beam in two-way propagation, the disturbances affecting the system; the signal to noise ratio SRN has determined the detection probability of the receiver. The model was used to evaluate with wavelengths: 850 nm, 1000 nm and 1550 nm, when propagating in three different bad weather conditions. The results show that the most effective detection of the target when using a wavelength of 1550 nm in visibility in haze and mist conditions (visibility V > 500 m). In fog conditions (visibility V < 500 m), the above three wavelengths provide the same detection efficiency. The article provides the method and instructions for choosing the wavelength of the laser fuze.

scholarly journals Handclap for Acoustic Measurements: Optimal Application and Limitations

Acoustics ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 224-245 ◽  
Author(s):  
Nikolaos M. Papadakis ◽  
Georgios E. Stavroulakis
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Acoustic Parameter ◽  
Reverberation Time ◽  
Octave Band ◽  
Acoustic Measurements ◽  
Frequency Range ◽  
Acoustic Source ◽  
Signal To Noise ◽  
Acoustic Parameters

Handclap is a convenient and useful acoustic source. This study aimed to explore its optimal application and limitations for acoustic measurements as well for other possible utilizations. For this purpose, the following steps were performed: investigation of the optimal hand configuration for acoustic measurements and measurements at different microphone source distances and at different spaces and positions. All measurements were performed with a handclap and a dodecahedron speaker for comparison. The results indicate that the optimal hand configuration (among 11) is with the hands cupped and held at an angle due to the superior low frequency spectrum. This configuration produced usable acoustic parameter measurements in the low frequency range in common room background levels unlike other configurations. The reverberation time was measured across different spaces and positions with a deviation less than three and just a noticeable difference of the signal-to-noise ratio within or near the ISO 3382-1 limits for each corresponding octave band. Other acoustic parameters (i.e., early decay time, clarity) were measured with greater deviations for reasons discussed in the text. Finally, practical steps for measurements with a handclap as an acoustic source are suggested.

An Effective Spectrum Sensing Method Based on Maximum Eigenvector

2014 ◽  
Vol 556-562 ◽  
pp. 4522-4525
Author(s):  
Rui Yan Du ◽  
Fu Lai Liu ◽  
Ya Ping Wu
Keyword(s):  
Spectrum Sensing ◽  
Detection Probability ◽  
Fundamental Problem ◽  
Signal To Noise Ratio ◽  
Power Method ◽  
Smoothing Technique ◽  
Signal To Noise ◽  
Radio System ◽  
Temporal Smoothing ◽  
Spectrum Holes

Spectrum sensing is a fundamental problem for cognitive radio system as it allows secondary user (SU) to find spectrum holes for opportunistic reuse. This paper presents a new spectrum sensing method based on the data stacking technique (temporal smoothing technique) and power method. The “maximum eigenvector” is used to detect the spectrum holes. Compared with the previous works, the proposed approach can provide better performance, such as higher detection probability in the lower signal-to-noise-ratio (SNR) scenario, etc.

Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling

2012 ◽  
Vol 468-471 ◽  
pp. 2296-2303
Author(s):  
Xiao Ping Zhang ◽  
Yang Wang
Keyword(s):  
Source Localization ◽  
Feature Vector ◽  
Signal To Noise Ratio ◽  
Parameters Optimization ◽  
Support Vector ◽  
Acoustic Source ◽  
Signal To Noise ◽  
Localization Method ◽  
Acoustic Source Localization ◽  
Noise Ratio

To solve the problem of acoustic source localization in wireless sensor networks (WSN) under interference of environmental noise, a novel acoustic source localization method in WSN based on Least Square Support Vector Regression (LSSVR) modeling (ASL-LRM) was proposed. The ideal measured values of acoustic sensors were used to compose feature vector at first. Then LSSVR models were built by LSSVR modeling on the mapping relation between feature vector and acoustic source coordinate. The acoustic source was then located by inputting feature vector composed of real measured values of the sensors into LSSVR models. The modeling parameters optimization method based on localization effect in sample locations was also discussed. Experiments were performed in 100 test locations. RMSE values by ASL-LRM method in 72-76 test locations were less than MLE method and reduced by 60%-74% at most. In lower signal-to-noise ratio case, there were 87 test locations where RMSE values by ASL-LRM method were less than 2 meters, while there were only 12 test locations by MLE method. It shows ASL-LRM method achieves better localization effects in a large part of the region surrounded by sensor nodes. It especially has advantage on the occasions like lower signal-to-noise ratio or high precision localization.

Sign in / Sign up

Export Citation Format

Share Document