35 results about "Generalised likelihood ratio test" patented technology

The invention discloses a method for estimating the safety communication rate of a near-earth satellite by using an SDFS cooperating with a GLRT. The method comprises the following steps: constructing a communication system model between the near-earth satellite and a ground terminal; analyzing the signal and channel characteristics, and obtaining the distance between the nodes, the channel gain and the received signal distribution; under the condition that the transmitting power of each monitoring party is unknown, estimating the transmitting power by adopting generalized likelihood ratio test, and independently executing the generalized likelihood ratio test based on the observation data of each monitoring party; the superior manager performs joint judgment based on a soft decision fusion scheme in combination with the generalized likelihood ratio test result of each monitoring party and a joint threshold so as to estimate the detection error probability of the monitoring party; a joint optimization strategy of the transmitting power and the number of continuous transmission time slots is constructed, and the total throughput of the system is maximized under the condition that the cooperative concealment constraint is met; according to the invention, an innovative solution is provided for reliable communication of the near-earth satellite in a complex monitoring environment.

The invention discloses a fuel cell vehicle control method integrating fault diagnosis and energy management. According to the method, vehicle key operation parameters are collected in real time, a whole vehicle operation state vector is constructed, and fuel cell health diagnosis and preliminary power action generation are carried out at the same time based on the state vector. The diagnosis module obtains a health factor and a severity index through residual calculation, particle filtering and generalized likelihood ratio test, and the action generation module outputs an intelligent agent power distribution vector. And according to the severity index, the dynamic reconstruction power, the SOC and the power change rate constraint, optimal power distribution is obtained through optimization solution, and meanwhile, a high-severity state is intervened in combination with a hierarchical fault-tolerant strategy. The method also comprises a demand power prediction and online learning module which is used for improving the robustness and the adaptive capability of the optimization strategy. According to the invention, by tightly combining the health state of the fuel cell with energy management optimization, high-energy-efficiency, long-service-life and reliable dynamic control of the fuel cell system is realized.

The invention discloses an NILM event detection method and system based on double sliding windows, and the method comprises the steps: obtaining high-frequency load data, and storing the high-frequency load data in a circular queue form, thereby obtaining a data sequence; performing data extraction on the data sequence based on self-adaptive double sliding windows, and outputting a long and short double sliding window sequence; and detecting the long and short double sliding window sequence based on a generalized likelihood ratio test method of logarithmic linearity to obtain a change point detection result. The system comprises a data arrangement module, a data extraction module and a data detection module. According to the invention, the precision of point change detection can be improved. The method can be widely applied to the field of non-intrusive load monitoring.

The invention discloses a passive detection method and device in a non-Gaussian noise environment and electronic equipment. The method comprises the following steps: constructing an M-dimensional probability density function of Rayleigh distribution of a received signal; acquiring a first probability density function of the received signal under a hypothesis H1 and a second probability density function of the received signal under a hypothesis H0; the hypothesis H1 represents the hypothesis that the received signal comprises the radiation source signal; the hypothesis H0 represents the hypothesis that the received signal does not include the radiation source signal; based on the first probability density function and the second probability density function, a generalized likelihood ratio check formula is obtained according to a generalized likelihood ratio basis; deriving the M-dimensional probability density function under the assumption of H1 to obtain maximum likelihood estimation of a product of a channel coefficient and Doppler frequency shift; substituting the maximum likelihood estimation into a generalized likelihood ratio test formula to obtain a test calculation value; and comparing the test calculation value with a threshold value to realize passive detection through a generalized likelihood ratio test algorithm based on test hypothesis under the condition of no reference signal.

The present disclosure relates to a pulse compressed radar signal detection method. The aspect of the present disclosure is to provide a radar signal detection method based on a generalized likelihood ratio test (GLRT) for detecting a pulse compressed radar signal by adequately utilizing the repeatability of pulses and being applicable even to a non-pulse compressed radar signal, thus having a relatively wide application range and securing excellent detection performance.

The invention discloses a weak cyclostationary signal detection method based on an unmanned aerial vehicle-mounted passive radar network, and the method comprises the steps: firstly building an active radar transmitting signal and unmanned aerial vehicle passive radar receiving signal model, and then building a binary hypothesis model based on cyclostationary generalized likelihood ratio detection through block cyclic matrix approximation; and constructing generalized likelihood ratio test detection statistics and obtaining local detection results of the unmanned aerial vehicles under a colored noise background by using the cyclostationary characteristic of target echoes, finally fusing the local detection results of the unmanned aerial vehicles based on a Bayesian minimum risk criterion at a fusion center, and outputting global detection judgment. And a detection process of cyclostationary generalized likelihood ratio test-Bayesian minimum risk fusion is formed. According to the method, the detection statistics are constructed by using the cyclostationary characteristics of the target echoes, the global detection accuracy and robustness can be improved under the conditions of low signal-to-noise ratio and complex colored noise, and the method is suitable for a multi-unmanned aerial vehicle cooperative passive radar detection scene.

This invention discloses a passive detection method, apparatus, and electronic device in a non-Gaussian noise environment. The method includes constructing an M-dimensional probability density function of the Rayleigh distribution of the received signal; obtaining a first probability density function of the received signal under assumption H1 and a second probability density function under assumption H0; assumption H1 represents the assumption that the received signal includes a radiating source signal; assumption H0 represents the assumption that the received signal does not include a radiating source signal; based on the first and second probability density functions, a generalized likelihood ratio (GLR) test formula is obtained according to the GLR criterion; the derivative of the M-dimensional probability density function under assumption H1 is taken to obtain a maximum likelihood estimate of the product of the channel coefficient and the Doppler frequency shift; the maximum likelihood estimate is substituted into the GLR test formula to obtain a test calculation value; and the test calculation value is compared with a threshold value to achieve passive detection in the absence of a reference signal through a GLR test algorithm based on the test assumption.

A method includes receiving a set of radar signals incident on a set of objects. The method includes selecting a GLRT detector. The method includes determining a ratio between a maximized likelihood function of a second hypothesis model and a maximized likelihood function of a first hypothesis model. The method includes determining whether a set of angles associated with the set of radar signals is available. The method includes, in response to a determination that the set of angles is available, updating a set of data associated with a set of objects. The method includes, in response to a determination that the set of angles is not available, selecting an angle estimation method, estimating the set of angles, and tracking the set of objects.

The application belongs to the technical field of equipment management, and discloses a nuclear detector fault diagnosis method, device, computer equipment and storage medium. The nuclear detector fault diagnosis method comprises the following steps: acquiring an output signal of the nuclear detector; inputting the output signal into a pre-trained deep belief network for fault identification to obtain a fault parameter corresponding to the output signal; and adopting a generalized likelihood ratio test classification model to perform fault classification on the fault parameter to obtain a fault category corresponding to the output signal, so that the fault of the nuclear detector can be quickly and accurately detected.

The present invention discloses an adaptive millimeter wave beam search method. The steps of the present invention are as follows: 1. beam search scenario, preset hierarchical beam and narrow beam search codebooks; 2. overall process of using hierarchical beam search codebooks and narrow beam search codebooks to perform adaptive beam search; 3. when using hierarchical beam search codebooks for beam search, a generalized likelihood ratio test is used to determine whether to switch the beam search codebook and whether to stop the hierarchical search of the current level and enter the next level; 4. when the hierarchical search starts a new layer of search, an angle interval to be searched is selected; 5. when the conditions for switching from the hierarchical beam search codebook to the narrow beam search codebook are met, a narrow beam search codebook is used for beam search; 6. when all beam searches are stopped, the final beamforming direction is selected. The present invention can realize the switching between the hierarchical beam codebook and the narrow beam codebook, thereby reducing the time overhead of the beam search while ensuring the accuracy of the beam alignment.

This invention discloses a method for detecting multi-channel constant-mode signals without prior electromagnetic interference, belonging to the field of radar signal processing technology. The method includes modeling multi-shot data received by a multi-channel antenna array to obtain high-dimensional multi-channel data; introducing weight vectors to project the high-dimensional multi-channel data onto a single channel for dimensionality reduction, resulting in single-channel weighted data; constructing a generalized likelihood ratio test detector without prior spatial electromagnetic interference information based on the probability density functions under two assumptions: the presence and absence of multi-channel constant-mode signals; and building an optimization model based on the generalized likelihood ratio test detector without prior spatial electromagnetic interference information. A hybrid optimization framework combining alternating optimization and alternating direction multiplier methods is used to solve the optimization model, obtaining the detection parameters and completing the multi-channel constant-mode detection.

Embodiments of the application disclose a kind of distributed communication signal detection method, device and system based on generalized likelihood ratio, wherein the method is applied to fusion center, comprising the following steps: S1: receiving the observation data transmitted by multiple receiving nodes; S2: under the assumption that radiation source does not exist and radiation source exists, respectively construct the joint conditional probability density function of all observation data of all receiving nodes within the observation time; S3: adopt NP criterion to construct the expression of generalized likelihood ratio test statistic; S4: according to maximum likelihood criterion, estimate the covariance matrix in the joint conditional probability density function under the assumption of two kinds; S5: the estimated value of the covariance matrix under the assumption of two kinds is substituted into the expression of generalized likelihood ratio test statistic, and compared with the decision threshold set according to false alarm probability, to complete distributed communication signal detection. The application can improve the detection probability of communication signal under low signal-to-noise ratio, and has good noise resistance when there is noise fluctuation in different receiving nodes.

The invention discloses an angle measurement method based on maximum likelihood estimation, and the method comprises the steps: constructing a correlation expression of an array echo signal model under the condition of employing a uniform linear array for receiving; establishing two hypotheses of GLRT (generalized likelihood ratio test), including an original hypothesis representing only noise and a standby hypothesis representing signal plus noise; respectively constructing probability density functions; constructing an expression of GLRT statistics in a mode of maximizing an angle value after maximum likelihood estimation is carried out on a probability density function under two assumptions on an unknown parameter; unknown parameters are estimated; the estimated value is substituted into the constructed expression of the GLRT statistical magnitude, and the final expression of the GLRT statistical magnitude is obtained; and obtaining a plurality of preset discrete angle values, and determining the discrete angle value which enables the final representation of the GLRT statistical magnitude to obtain a peak value as an estimated angle, thereby solving the problems of poor adaptability and insufficient precision of a traditional method in a low signal-to-noise ratio and complex electromagnetic environment.

The invention discloses a secure communication method based on STAR-RIS assistance in a communication-sensing integrated network. The method comprises the following steps: analyzing channel characteristics among a base station, an active reconfigurable intelligent surface, a user, a target, a plurality of guards and a fusion center; solving an optimal detection threshold value and a minimum detection error probability based on generalized likelihood ratio test and maximum likelihood estimation under the condition that a guarder knows the communication transmission power of the base station; solving the minimum security communication detection error probability of the fusion center under the hard decision fusion scheme and the soft decision fusion scheme based on the independent detection result of each guard; the transmitting beam forming vector of the base station, the active beam forming vector of the active reconfigurable smart surface and the number of continuous transmission time slots are jointly optimized to maximize the total secure transmission rate at the user and meet the perception performance and security performance constraints at the same time; according to the invention, the contradiction among the sensing performance, the safety performance and the safety transmission efficiency is balanced.

The invention provides a cooperative random encryption industrial internet physical layer secure transmission and detection method, and belongs to the technical field of wireless communication and information security. The multivariate data is received by I local sensors after being transmitted through a wireless channel, and the ith local sensor carries out hard decision by using a maximum likelihood criterion and encodes and converts the multivariate data into a binary signal; randomly selecting a part of data from the sent data to perform flipping encryption, and forwarding the encrypted flipping data by a plurality of relay nodes and then sending the flipping data to a fusion center; and after the data arrives at the fusion center, the fusion center firstly performs flipping decryption on the flipped data before transmission and then decodes a receiving value obtained by each transmission link, and a decoded signal obtains a system judgment metric value based on generalized likelihood ratio inspection and compares the system judgment metric value with a judgment threshold to obtain a final detection result. According to the method, the anti-eavesdropping capability and the transmission reliability are remarkably improved in the wireless sensor network with limited energy, and the method has relatively high engineering applicability and robustness.

An occupancy detector includes a radar and an occupancy analyzer. The radar is provided to output a radar background signal and a radar detection signal which are time-domain signals. The radar background signal is detected by the radar from an area when a subject is not occupying the area, and the radar detection signal is detected by the radar from the area when the presence of the subject is unknown. The occupancy analyzer is electrically connected to the radar to receive the radar background signal and the radar detection signal, and it is provided to analysis whether a subject is occupying the area based on the radar background signal and the radar detection signal through generalized likelihood ratio test (GLRT).

A method and system for sea surface target recognition based on range-time images, belonging to the field of measurement image recognition and processing, firstly maps the actually observed sea surface RT image into sea surface RT image data with amplitude following a standard Rayleigh distribution. The covariance matrix of the mapped sea clutter data is constructed. A binary hypothesis testing method is used on the mapped sea clutter data to construct a generalized likelihood ratio test statistic. The optimal estimate is substituted into the log-likelihood ratio expression to obtain the standard form of the mapped generalized likelihood ratio detector. The detector is then used to identify whether a target exists in the range cell to be detected. This invention can convert arbitrary non-Gaussian sea surface RT image data into RT image data with amplitude following a standard Rayleigh distribution, thereby achieving unified processing in complex sea clutter environments and effectively improving the accuracy of target recognition under complex non-Gaussian sea clutter backgrounds.

The application discloses a kind of artificial noise auxiliary de-cellular perception integrated beamforming methods, comprising: multiple distributed access points AP, user, eavesdropper and a perception target are laid out, and access point AP is centrally controlled by central processing unit.First, AP sends signal and receives target reflection signal, and detection probability is calculated based on generalized likelihood ratio test;With this probability as optimization goal, combined with communication demand and eavesdropping restriction, optimization problem is constructed.By analyzing the structure of the problem, it is decomposed into two sub-problems of beamforming and artificial noise joint optimization, and power allocation.The former is transformed into a convex optimization problem using semidefinite relaxation method;The latter uses bisection method to iteratively optimize power allocation, and updates the power allocation parameter optimization interval at each iteration, thereby maximizing system performance under a given total power.The application can effectively improve the detection probability and improve the perception safety under different power constraints with lower complexity.

The present invention discloses a method for optimizing the transmission power and time slots of a covert communication system under imperfect prior information. First, the scenario where malicious monitoring nodes in the covert communication network do not have prior information about the transmission power of covert users is described and modeled. Then, based on the premise of imperfect prior information, a detection framework based on the generalized likelihood ratio test is constructed, and based on this, a detection model using the observation results of the current time slot and a detection model using the observations of all past time slots are constructed. Finally, a joint optimization model for the transmission power and time slots of the covert communication system is proposed to obtain the optimized transmission power and time slots of the covert communication system. The present invention derives the optimal detection theory of malicious detection nodes in the case of unknown prior information, redesigns the transmission power and time slot strategies of the covert communication system, obtains the optimal solution, and completes the optimization of the transmission power and time slots of the covert communication system under the condition that the malicious detection nodes do not have prior information about the transmission power.

The invention belongs to the technical field of inertial navigation, and particularly provides a zero-speed detection method based on dynamic weighted generalized likelihood ratio detection, which is used for solving the problem of insufficient precision of traditional zero-speed detection and improving the precision of systems such as pedestrian inertial navigation. The method comprises the following steps: firstly, establishing a zero-speed (H0) and non-zero-speed (H1) binary hypothesis test model based on a hypothesis that sensor data obeys Gaussian distribution within a short time; deriving a generalized likelihood ratio test (GLRT) statistic of each single sensor; secondly, dynamic adjustable weight factors are introduced to different sensor shafts, the GLRT statistics of all the single sensor shafts are subjected to weighted fusion according to weights, and weighted GLRT statistics are constructed to serve as a zero-speed judgment basis; through dynamic weighted fusion, the zero-speed detection accuracy is improved, the misjudgment rate is reduced, a more reliable opportunity is provided for navigation error correction, and the navigation error under the pure inertial navigation condition is effectively relieved.

This invention relates to a network anomaly traffic detection method based on wavelet transform and generalized likelihood ratio test, belonging to the field of network security technology. This invention uses Daubechie wavelet transform on network traffic data to output a set of multiple and independent wavelet coefficients, enabling them to conform to a bivariate Cauchy-Gaussian mixture statistical model. The generalized likelihood ratio test is then used for verification, thus solving the problem that hypothesis testing cannot be applied to identify anomalies in time-series network traffic data.

The application discloses a data-driven replay attack detection method based on an equivalent space industrial control system, which comprises the following steps: acquiring input and output data of multiple time points from an industrial control system; constructing a historical data matrix and calculating a zero matrix, solving an optimal weight matrix, calculating an equivalent matrix, generating a system residual, designing a chi-square test and a generalized likelihood ratio test, and designing corresponding thresholds; introducing part of model information to enhance the detection capability of the original system; deploying the obtained detection system, acquiring the input and output of the system online, generating a residual, and using the chi-square test and the generalized likelihood ratio test to judge the system operation; when the test value is greater than the residual threshold, it is judged that the system is subjected to a replay attack, and an alarm is issued. The application can detect the replay attack based on the historical data of the equipment operation of the industrial control network through the input and output of the system without depending on the model parameters of the system, meanwhile, the application is easy to deploy and does not sacrifice the control performance of the system.

The present invention discloses a NILM event detection method and system based on double sliding windows. The method includes: obtaining high-frequency load data and storing it in the form of a circular queue to obtain a data sequence; extracting data from the data sequence based on an adaptive double sliding window to output a long and short double sliding window sequence; detecting the long and short double sliding window sequence based on a logarithmic-linear generalized likelihood ratio test method to obtain a change point detection result. The system includes: a data sorting module, a data extraction module, and a data detection module. By using the present invention, the accuracy of change point detection can be improved. The present invention can be widely applied to the field of non-intrusive load monitoring.

The application provides a moving platform distributed radar moving target rapid detection method, device and equipment. The moving platform distributed radar moving target rapid detection method comprises the following steps: a moving platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the moving platform distributed radar system model; a generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system is generated through the low-bit quantized signal model; joint estimation processing is performed on the GLRT detector by using a differential evolution (DE) method and a batch gradient descent (BGDA) method, so that state data is obtained; the state data comprises a radar cross section (RCS), a target position, a target speed, a radar position and a radar speed. In the application, the joint estimation processing is performed by using the DE method and the BGDA method, so that the accuracy and the convergence speed of the moving platform distributed radar system for target parameter estimation in a dynamically changing environment are improved.

The invention discloses an artificial noise-assisted honeycomb-removing and sensing integrated beam forming method, which comprises the following steps that: a plurality of distributed access points (APs), users, eavesdroppers and a sensing target are arranged, and the access points (APs) are controlled by a central processing unit in a centralized manner; firstly, an AP sends a signal and receives a target reflection signal, and the detection probability is calculated based on generalized likelihood ratio test; the probability is taken as an optimization target, and an optimization problem is constructed in combination with a communication demand and eavesdropping limitation. By analyzing the structure of the problem, the problem is decomposed into two sub-problems of beam forming and artificial noise joint optimization and power distribution. The former adopts a semi-definite relaxation method to convert into a convex optimization problem; and the latter iteratively optimizes power distribution by using a dichotomy, and a power distribution parameter optimization interval is updated in each iteration, so that the system performance is maximized under the given total power. According to the method, the detection probability can be effectively improved with low complexity under different power constraints, and the sensing safety is improved.

The application relates to a generalized likelihood ratio test method and device based on Bayesian inference. The method comprises the following steps: acquiring reference channel and monitoring channel signals of a transmitting source and a receiving node, and constructing a binary hypothesis test signal model of target presence / absence; modeling unknown channel response coefficients as random variables with a known prior distribution, and modeling a transmitting signal symbol sequence as a deterministic variable, and constructing a joint probability distribution function of two types of hypotheses; obtaining an edge probability function through integral edge processing, maximizing the edge probability function to obtain a deterministic variable estimation value, constructing a Bayesian generalized likelihood ratio test statistic, and comparing the Bayesian generalized likelihood ratio test statistic with a preset threshold to determine whether the target exists. By using the method, channel characteristics and signal structure prior knowledge can be fully fused, detection robustness and sensitivity in a low signal-to-noise ratio and high dynamic environment can be improved, calculation complexity can be simplified, spatial diversity gain can be efficiently utilized, and quasi-optimal detection under the constraint of a constant false alarm probability can be realized.

The application discloses a kind of based on STAR-RIS auxiliary in the security communication method of integrated network, including the following steps: the channel characteristics between base station, active reconfigurable intelligent surface, user, target, multiple guards and fusion center are analyzed;In the case where the communication transmission power of guard is unknown base station, based on generalized likelihood ratio test and maximum likelihood estimation, the optimal detection threshold and minimum detection error probability are solved;Based on the independent detection result of each guard, the minimum security communication detection error probability of fusion center under hard decision fusion scheme and soft decision fusion scheme is solved;The transmission beamforming vector of base station, the active beamforming vector of active reconfigurable intelligent surface and the number of continuous transmission time slots are jointly optimized to maximize the total security transmission rate at the user, while satisfying the sensing performance and security performance constraints;The application balances the contradiction between sensing performance, security and security transmission efficiency.

A target adaptive detection and position estimation method for a multiple-input-multiple-output system comprises the following steps: acquiring the number of transmitting array elements, the number of receiving array elements and the size of a time delay-Doppler unit of the multiple-input-multiple-output system, and constructing a time delay-Doppler unit echo vector model containing a target, dividing the leakage condition of the target energy in the time delay-Doppler unit into a plurality of scenes; modeling target adaptive detection of the multi-input multi-output system as a binary hypothesis test problem; on the basis of a two-step generalized likelihood ratio test criterion, firstly, it is assumed that a background interference covariance matrix is known, detection statistics of generalized likelihood ratio test are calculated by using an echo vector in a time delay-Doppler window to be detected, and then an estimated value of the background interference covariance matrix is calculated by using a recursive estimation method. And replacing the background interference covariance matrix in the detection statistics with the estimated value to obtain a target adaptive detection method, and estimating the position of the target in the time delay-Doppler unit through two-dimensional lattice search.

This invention provides a clutter edge detection and clutter data classification method and system. For sonar systems with multiple array elements, based on the generalized likelihood ratio test and model order selection criterion, it determines the number and location of reverberation edges in auxiliary data and assesses the uniformity of the auxiliary data. The method includes: establishing a binary hypothesis testing problem based on echo data received by the sonar system from the range cell to be detected and the auxiliary cell; estimating the number of reverberation types, the reverberation covariance matrix of the auxiliary data, and the reverberation edge locations using the model order selection criterion; and determining the uniformity of the auxiliary data using the generalized likelihood ratio test. The advantages of this invention are: direct classification of reverberation data by estimating the reverberation edge locations; adaptive estimation of the background reverberation energy of each uniform region; and adaptive estimation of the number and location of reverberation edges in the auxiliary data, making it more practical in engineering applications.

The invention relates to a generalized likelihood ratio detection method and device based on Bayesian inference. The method comprises the following steps: acquiring reference channel and monitoring channel signals of a transmitting source and a receiving node, and constructing a binary hypothesis test signal model of target existence / absence; modeling an unknown channel response coefficient as a random variable of known prior distribution, modeling a transmitting signal symbol sequence as a deterministic variable, and constructing a joint probability distribution function of two types of hypotheses; and carrying out integral marginalization processing to obtain an edge probability function, obtaining a deterministic variable estimation value after maximization, constructing a Bayesian generalized likelihood ratio test statistic, and comparing with a preset threshold to judge whether a target exists or not. By adopting the method disclosed by the invention, channel characteristics and signal structure priori knowledge can be fully fused, the detection robustness and sensitivity in a low signal-to-noise ratio and high dynamic environment are improved, the calculation complexity is simplified, the space diversity gain is efficiently utilized, and quasi-optimal detection under a constant false alarm probability constraint is realized.