Spectrum Detection Method Research Articles

Cognitive Radio Spectrum Sensing-Based QAM Technique Using Blockchain

Cognitive radio (CR) is a novel concept developed to solve concerns such as spectrum underutilization and scarcity. Spectrum detection methods are employed in the blockchain-based CR to make optimum use of the spectrum reserves. In this paper, an attempt is made to evaluate the effectiveness of an energy sensor using collaborative spectrum detection. Wideband is defined as the frequency range between 470 MHz and 790 MHz, and additive white Gaussian noise (AWGN) is employed. The probability of detection ( P d ) under different situations is examined using detection in the receiver operational curve (ROC). According to the findings, the P d increases with the number of samples. This form of sensing, which is thought to be the easiest and best, uses energy-detecting spectrum sensing. To address ambiguity, the M-ary QAM technique is provided, which increases aggregate effectiveness in terms of the percentage of false alarm ( P f ) and probability of missed detection ( P m ) by 5% at a comparable delay period. When CR encounters shadowing and impacts situations, the client cannot tell the difference between an underutilized zone and fading. In comparison to the existing model, this study increases the likelihood of detecting a 3 dBm SNR for a 64-QAM modulated signal by at least 15%.

Abnormal Electromagnetic Wave Detection Method Before Earthquake Based on Feature Extraction of Radio Frequency I/Q Signal

Earthquake is one of the more common natural disasters. At present, it is mainly through the deployment of monitoring stations to provide the earthquake information broadcasting service. Earthquakes are caused by the Earth’s plates pushing against each other. Before an earthquake occurs, the energy gathered by plate compression will produce abnormal electromagnetic radiation due to the compression magnetic effect. The energy detection method of abnormal electromagnetic radiation has been proposed by researchers for a long time. Anomalous electromagnetic radiation is difficult to catch because its energy is very weak. Based on the distributed electromagnetic spectrum detection method, this paper proposes the original radio frequency (RF) I/Q sample data. Combined with I/Q component cumulative distance fluctuation estimation method, abnormal electromagnetic radiation phenomenon is detected. Combined with source location information, magnitude and depth, a prediction model of cumulative distance fluctuation of I/Q component is proposed based on the RF I/Q sample data of electromagnetic spectrum. The results of actual monitoring data show that the prediction model of I/Q component cumulative distance fluctuation estimation can accurately describe the abnormal electromagnetic radiation phenomenon generated by earthquake information above magnitude 6. Combined with a large-scale distributed electromagnetic spectrum detection system, an earthquake prediction system with higher accuracy can be realized.

Microwave Spectrum Detection at Microscopic Scale Based on Nitrogen Vacancy Center in Diamond

A machine learning‐based microwave spectrum detection method based on the nitrogen vacancy (NV) color centers in diamonds is proposed. The functional relationship between the fluorescence spectrum and standard microwave spectrum is established. The response matrix is calculated using the Tikhonov regularization technique, and an unknown microwave spectrum is reconstructed. Diamond particles with a size of only 5 × 5 μm2 are placed in the microfluidic structures. Consequently, the frequency detection range of the microwave spectrum is from 2.892 to 6.214 GHz with a resolution of 22 kHz. The proposed research opens new paths for microwave spectrum detection and imaging at the microscopic scale.

Application of electrochemical impedance spectroscopy in battery management system: State of charge estimation for aging batteries

For electric vehicles (EVs) and hybrid electric vehicles (HEVs), the accurate estimation of state of charge (SoC) has always been the focus of research. The commonly used SoC estimation methods are based on equivalent circuit model (ECM) and require high model accuracy. With battery aging, its impedance and capacity will change, which inevitably affects the estimation accuracy. In this paper, the impedance spectrum detection method is integrated into the battery management system (BMS), and a new model updating strategy based on electrochemical impedance spectroscopy (EIS) is proposed for SoC algorithm. Firstly, the evolution of impedance spectrum with battery aging is analyzed through accelerated aging test. It is found that the aging mainly causes the increase of ohmic resistance for the tested Li-ion battery but has no significant impact on the charge transfer and diffusion processes. Therefore, the model parameters and capacity are modified according to the change rate of ohmic impedance. Secondly, the proposed method is verified under charging and discharging conditions. Results show that as the battery ages, the SoC estimation value will be lower than the real value under the discharging condition and higher than the real value under the charging condition. Meanwhile, the maximum absolute error (MaxAE) will reach 29.9 % after 1000 cycles. In contrast, based on EIS correction, the MaxAE can be controlled within 5.4 %, which effectively enhances the estimation accuracy.

SPECTRUM SENSING ALGORITHM BASED ON SHAPIRO WILK TEST

In this study, spectrum algorithm based on Shapiro Wilk test is discussed and a spectrum detection method is proposed for Cognitive Radios with Shapiro Wilk test. In this method, it is tested whether the energy of the received signal has a Gauss distribution. Simulation studies were carried out with different parameters to evaluate the performance of the proposed detection method. It has been compared with the Energy detector in simulation studies. Under some conditions, the proposed method was found to be more successful than ED.

A line spectrum detector based on improved coherent power spectrum estimation

The line spectrum detection method based on coherent power spectrum estimation is one of the important research directions of weak signal line spectrum detection. For the purpose of resolving the problem of phase compensation inaccurate introduced by limited frequency search accuracy with the existing coherent power spectral estimate method, a modified line spectrum detector is proposed. The zero padding is used to improve the line spectrum frequency search accuracy in discrete Fourier transform (DFT) for phase compensation calculations, thereby obtaining more accurate line spectrum frequencies and phase compensation, which further improves Line spectrum detection performance. The detection performances of three detections based on Welch’s method, coherently averaged power spectral estimate(CAPSE) and modified method are compared each other by computer simulations. The results show modified line spectrum detection can obtain a gain of 2-3dB, which verifies the effectiveness of the modified method.

A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio.

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

Fuzzy Hypothesis Test for Cognitive Radios

This article presents a statistical approach called hypothesis testing for Cognitive Radio systems. Hyposthesis tests have a wide range of applications from detection theory to radar systems. Hypothesis testing is frequently used in Cognitive Radio systems, which are the solution to the spectrum shortage problem today. Hypothesis testing in Cognitive Radio systems is the basis of spectrum detection. By means of hypothesis testing, spectrum gaps are determined so that spectrum gaps are opened to different users' access. In this study, a fuzzy hypothesis test based spectrum detection method is proposed with the signal detected by Cognitive Radio users. Theoretical bases and simulation results of the proposed spectrum detection model are also given. Simulation studies prove the computational cost advantage and detection performance success of the proposed detection method.

Antenna Selection and Detection Performance on Correlation Based Detection Systems

The increase data size transmitted in digital communication systems necessitates the use of high-capacity communication channels for the transmission of these data. To increase the channel capacity, MIMO systems are widely used. Antenna selection in MIMO systems is very important for efficient use of communication sources. Cognitive Radio systems generally aim to increase spectrum efficiency by using MIMO systems. Therefore, Cognitive Radio must find empty spectrum regions with spectrum detection methods. Spectrum detection performance is related to the antenna numbers in MIMO systems. Therefore, this study examines the effect of antenna numbers on detection performance in correlation-based detection method. Thanks to this study, the relationship between channel capacity, detection performance and antenna number for MIMO systems was investigated.

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection.

Offshore Electromagnetic Spectrum Detection System Based on Self-Organizing Network

The offshore electromagnetic spectrum resource is one of the important marine resources. It is the physical channel for wireless communication between ships, ships and islands, waterways, and bays. The traditional discrete electromagnetic spectrum energy detection method extracts few signal features, and the data analysis is of little significance. This method is not suitable for offshore electromagnetic spectrum detection activities. We propose a distributed detection system based on self-organizing network to meet real-time large-scale offshore electromagnetic spectrum detection activities. Combining space energy spectrum, phase information, radio frequency (RF) I/Q multiple signal characteristics to study offshore electromagnetic spectrum sensing methods. We propose to replace the traditional electromagnetic spectrum detection method with signal feature description method. Combined with the attitude of the antenna, we introduced the wave spectrum to fit the complex sea surface under different sea conditions, and realized the offshore distributed electromagnetic spectrum prediction system. The research of offshore distributed electromagnetic frequency detection system is of great significance to improve the accuracy of offshore electromagnetic spectrum detection. It has theoretical guiding significance in analyzing the utilization of offshore electromagnetic spectrum resources, managing electromagnetic spectrum resources, and formulating dynamic spectrum access strategies.

UWB Simultaneous Breathing and Heart Rate Detections in Driving Scenario Using Multi-Feature Alignment Two-Layer EEMD Method

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car.

Research on Laser Absorption Spectrum Detection Technology for CO2 and NH3 Regional Emission

High-sensitivity emission monitoring of CO2 and NH3 in farmland region is very important for analyzing environment and climate change. The on-line detection technology was studied based on open-path tunable diode laser absorption spectroscopy technology (OP-TDLAS). The detection spectrum of open path was extracted to realize accurate concentration inversion by designing the multi-line fitting algorithm. The system detection stability was evaluated by Allan variance that the detection limit of NH3 and CO2 were about 0.048 L/L and 4.31 L/L respectively. The detection experiment was carried out in farmland of north Anhui to prove that both of the fertilization and straw returning to the field were the emission source of CO2 and NH3 from soil. The growth effect of the former was slower but higher than that of fertilization. This stable spectrum detection method can obtain large-scale concentration results and clear emission rules, which provide technical support for the environment-friendly agriculture

Application of laser remote sensing technology and super continuous spectrum laser

Fiber optic super continuous spectrum laser technology is a new technology developed in recent years. It takes into account the advantages of good alignment of laser source, high energy density and wide wavelength range of ordinary white light source, which can not only accurately remote sense distant target, but also obtain hyperspectral information of distant target. Super continuous spectrum of laser radar is a kind of remote sensing monitoring instrument, it can obtain three-dimensional spectral information of the target, and can be accurately detected in the night of distant high spectral information, the all-weather, three-dimensional spectrum detection method with the traditional passive remote sensing compared with single/multiband laser radar technology has incomparable advantages. Based on the development trend of lidar abroad, the development status of super-continuous spectrum lidar in China and the problems in remote sensing detection of oil spill, this paper introduces the feasibility of using super-continuous spectrum laser to monitor oil spill in the sea, which lays a foundation for further research.

Ultra-high sensitive fiber-optic Fabry-Perot cantilever enhanced resonant photoacoustic spectroscopy

A new scheme of fiber-optic cantilever enhanced resonant photoacoustic spectroscopy (CERPAS), combining high sensitive fiber-optic Fabry-Perot cantilever microphone with resonant photoacoustic spectroscopy, is presented for trace gas detection. A fiber-optic cantilever microphone with high sensitivity at the frequency near 1.4 kHz is designed to match with a first-order longitudinal resonant photoacoustic cell, whose resonant frequency is 1402 Hz. For sensitivity improvement, an erbium-doped fiber amplified near-infrared laser, with the central wavelength of 1532.83 nm and the maximum output power of 1 W, is used as the light source for acoustic excitation. The trace acetylene detection experiment demonstrates that, with the wavelength modulation spectrum and second-harmonic detection methods, the gas detection limit is achieved to be 80 ppt, which is at least one order of magnitude improvement compared with other photoacoustic acetylene sensors reported so far.

A novel CMOS spectrometer based on wavelength absorption

This article reports on a novel spectrometer without dispersing elements fabricated in standard CMOS technology. The spectrum detection principle is based on the wavelength absorption mechanism in silicon. A finite element model confirms the excess holes’ detection principle as a function of depth where moving holes’ trajectory is deviated under the Lorentz force towards a set of collectors. In the case of high excess carrier concentration, experimental results confirm the theoretical analysis that wavelength becomes indistinguishable because the Auger recombination mechanism is dominant, which should be avoided to realize a spectrometer. For the low excess carrier concentration case, the concentration profile is determined by the incident irradiance and the wavelength and can be additive since the Shockley-Reed-Hall recombination mechanism prevails, where the excess carrier life time is constant, and hence suitable for wavelength discrimination. In order to realize a spectrometer, a light spectrum detection method is developed, which requires a linear equation set where coefficients of the matrix coming from the measurement of the current density as a function of the irradiance for different wavelengths and magnetic fields. The proposed miniature and integrated spectrometer with a pixel array can be used as a spectral imager.

スキャッタードパイロットの周期性を利用したISDB-Tのスペクトル検出法の検討(デジタル放送技術および一般)

スキャッタードパイロットの周期性を利用したISDB-Tのスペクトル検出法の検討(デジタル放送技術および一般)

7-5 スキャッタードパイロットの周期性に基づくISDB-Tのスペクトルの検出法の検討(第7部門放送方式)

7-5 スキャッタードパイロットの周期性に基づくISDB-Tのスペクトルの検出法の検討(第7部門放送方式)

The Anomaly Mixed Spectrum Signals Detection Based on ICA and KNN

Independent Component Analysis is very effective for blind source separation of mixed signals, except detecting abnormal signal, presented an anomaly mixed spectrum detection method based on K Nearest Neighbor classification, using Independent Component Analysis to separate the mixed signal under Gaussian noise, and classifying the mixed signal based on training sample set, so as to find the abnormal signal mixed in the original signal. Simulation results showed that this method could not only separated signals effectively, but also improved the classification accuracy.

Collaborative Spectrum Sensing Algorithm Based on Exponential Entropy in Cognitive Radio Networks

Traditional detectors for spectrum sensing in cognitive radio networks always become disabled when noise uncertainty is severe. Shannon entropy-based detection methods have aroused widespread attention in recent years due to the characteristics of effective anti-noise uncertainty. However, in existing entropy-based sensing schemes, the uniform quantization method cannot guarantee the maximum entropy distribution when primary users do not exist, and cannot effectively distinguish between two hypotheses, which severely limits the promotion of detection performance. Moreover, the Shannon entropy-based sensing schemes are prone to misconvergence occurring when estimating entropy values, thus causing failure detection, which leads to system detection inefficiency and resource waste. These are the two major serious defects in Shannon entropy-based detectors, which restrict the performance improvement. In this paper, a novel non-uniform quantized exponential entropy-based (NQEE) detector is proposed for local sensing to deal with the problems of maximum entropy distribution and detection failure. To further improve the reliability of the detection, a collaborative spectrum sensing algorithm based on an NQEE detector with multiple fusion rules is presented. Simulation results verify that the detection performance of the improved local entropy-based detector is superior to the existing Shannon entropy-based detectors and is proved to be robust to noise power uncertainty. In addition, the novel collaborative detection algorithm outperforms the traditional collaborative spectrum detection method to a great degree.