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.

An Improved Signal Detection Technique for OFDM System

2013 ◽  
Vol 347-350 ◽  
pp. 3527-3531
Author(s):  
Xiao Hong Wang ◽  
Feng Ming Li
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Signal Detection ◽  
Detection Probability ◽  
Roc Curves ◽  
Detection Technique ◽  
Ofdm System ◽  
Low Snr ◽  
Awgn Channel ◽  
Simulation Results

In this paper a signal detection technique based on pilots which are transmitted for channel estimation in OFDM system is proposed in AWGN channel. We analyse the algorithm based on pilots and derive an improved signal detection technique. The performance is compared in terms of detection probability and ROC curves are given. The simulation results show that the improved detection technique whose computational complexity is not high can increase the precision of the detection probability at low SNR.

Weak Coding Signal Detection under the Background of Sea Clutter

2011 ◽  
Vol 383-390 ◽  
pp. 2077-2082
Author(s):  
Shi Hua Liu ◽  
Zhi Jian Sun ◽  
Wen Sheng ◽  
Zhong Ming Hu
Keyword(s):  
Signal Detection ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Multi Layer Perceptron ◽  
Sea Clutter ◽  
Low Snr ◽  
Chaotic Signals ◽  
One Step ◽  
Three Stages ◽  
Barker Code

A new method of detection of coding signals under the background of sea clutter is presented. The process of signal detection consists of three stages: modeling sea clutter signals based on chaos, one-step ahead prediction of chaotic signals and detection decision making. In this method, models of chaotic signals were created in the form of multi-layer perceptron neural networks, coded signals take on 13-element Barker code. The experiment results show detection of coding signal by using this method has higher detection probability and lower false alarm probability and good performance of the whole detection although with a low SNR. This method turned out to be very robust to different chaotic signals.

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 A Fast Detection Algorithm for the X-Ray Pulsar Signal

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Hao Liang ◽  
Yafeng Zhan
Keyword(s):  
False Alarm ◽  
Navigation System ◽  
Detection Probability ◽  
Autonomous Navigation ◽  
Detection Algorithm ◽  
Observation Time ◽  
X Ray ◽  
Fast Detection ◽  
Simulation Results ◽  
Short Observation Time

The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

scholarly journals A Novel Sensing Strategy Based on Energy Detector for Spectrum Sensing

2019 ◽  
Vol 9 (21) ◽  
pp. 4634 ◽  
Author(s):  
Hai Huang ◽  
Jia Zhu ◽  
Junsheng Mu
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Energy Detector ◽  
Accuracy Improvement ◽  
Classical Scheme ◽  
Sensing Performance

Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper.

scholarly journals A PRACH Preamble Generation and Detection Model for 5GNR Systems

2021 ◽  
pp. 98-105
Author(s):  
Puneeth K M ◽  
Poornima M S
Keyword(s):  
False Alarm ◽  
Detection Probability ◽  
Additive White Gaussian Noise ◽  
Random Access ◽  
False Alarm Probability ◽  
Probability Of Detection ◽  
Detection Model ◽  
High Data ◽  
Access Channel ◽  
New Radio

The basic idea of 5th generation New Radio (5GNR) is to have very high data rate and to make it work efficiently for all Internet of Things (IOT) applications like healthcare, Automotive, Industrial etc. applications. This paper provides the Orthogonal Frequency Division Multiple Access (OFDM) baseband signal generation and detection method for Physical Random-Access Channel (PRACH). The proposed model provides four scenarios of preamble detection i.e., Preamble detection probability, Miss-detection probability, False alarm probability and null. We achieved the target of 99% of Probability of Detection and less than 0.1% of False-alarm probability at certain SNR as specified according to 3gpp standard requirements when tested in Additive White Gaussian Noise (AWGN) channel and Extended Typical Urban (ETU) channel.

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 analysis of energy detector based on improved approximations of the chi-square distributions

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Felipe G. M. Elias ◽  
Evelio M. G. Fernández
Keyword(s):  
False Alarm ◽  
Detection Probability ◽  
Total Error ◽  
Detection Threshold ◽  
False Alarm Probability ◽  
Optimal Number ◽  
Energy Detector ◽  
Chi Square ◽  
Total Error Rate ◽  
Wide Range

AbstractClosed-form expressions for the detection probability, the false alarm probability and the energy detector constant threshold are derived using approximations of the central chi-square and non-central chi-square distributions. The approximations used show closer proximity to the original functions when compared to the expressions used in the literature. The novel expressions allow gains up to 6% and 16% in terms of measured false alarm and miss-detection probability, respectively, if compared to the Central Limit Theorem approach. The throughput of cognitive network is also enhanced when these novel expressions are implemented, providing gains up to 9%. New equations are also presented that minimize the total error rate to obtain the detection threshold and the optimal number of samples. The analytical results match the results of the simulation for a wide range of SNR values.

Cepstrum-Based Spectrum Hole Search in Different Fading Scenario in Cognitive Radio Network

2017 ◽  
pp. 49-62
Author(s):  
Srijibendu Bagchi
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Channel Fading ◽  
Spectral Density Estimation ◽  
Power Spectral ◽  
Detection Probabilities ◽  
Spectrum Scarcity ◽  
Spectrum Hole

Cognitive radio is now acknowledged as a potential solution to meet the spectrum scarcity problem in radio frequency range. To achieve this objective proper identification of vacant frequency band is necessary. In this article a detection methodology based on cepstrum estimation has been proposed that can be done through power spectral density estimation of the received signal. The detection has been studied under different channel fading conditions along with Gaussian noise. Two figures of merit are considered here; false alarm probability and detection probability. For a specific false alarm probability, the detection probabilities are calculated for different sample size and it has been established through numerical results that the proposed detector performs quite well in different channel impairments.

scholarly journals Robust Spectrum Sensing Demonstration Using a Low-Cost Front-End Receiver

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Daniele Borio ◽  
Emanuele Angiuli ◽  
Raimondo Giuliani ◽  
Gianmarco Baldini
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
High Frequency ◽  
Detection Probability ◽  
Low Cost ◽  
False Alarm Probability ◽  
Primary Users ◽  
Front End ◽  
Challenging Tasks

Spectrum Sensing (SS) is an important function in Cognitive Radio (CR) to detect primary users. The design of SS algorithms is one of the most challenging tasks in CR and requires innovative hardware and software solutions to enhance detection probability and minimize low false alarm probability. Although several SS algorithms have been developed in the specialized literature, limited work has been done to practically demonstrate the feasibility of this function on platforms with significant computational and hardware constraints. In this paper, SS is demonstrated using a low cost TV tuner as agile front-end for sensing a large portion of the Ultra-High Frequency (UHF) spectrum. The problems encountered and the limitations imposed by the front-end are analysed along with the solutions adopted. Finally, the spectrum sensor developed is implemented on an Android device and SS implementation is demonstrated using a smartphone.

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