59 results about "Generalized likelihood ratio" 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 relates to the technical field of robot navigation, and discloses a multi-sensor fusion navigation method and system of a robot. The method comprises the following steps: collecting point cloud data of a laser radar sensor and motion data of an IMU six-axis sensor; executing generalized likelihood ratio test and sliding window interval search on the point cloud data and the motion data to obtain a zero-speed abnormal interval, and creating a detection threshold value for the current ground environment according to the zero-speed abnormal interval; performing spatial superposition on the position coordinates of the zero-speed abnormal interval and a laser radar scanning blind area to obtain an obstacle distribution map; cleaning control parameters are adjusted based on the detection threshold value, a re-planning cleaning path is generated based on the obstacle distribution map and the cleaning control parameters, the method can actively identify the potential trapped area and re-plan the cleaning path, and the autonomous navigation ability and cleaning efficiency of the intelligent cleaning robot are improved.

The invention relates to an array IMU (Inertial Measurement Unit) fault detection and data fusion method and system based on reinforcement learning, which are characterized in that a generalized likelihood ratio detection threshold is dynamically adjusted according to a real-time environment through an Actor-Critic network, and the problems of false alarm and missing alarm caused by a fixed threshold are solved; meanwhile, the fusion weight is dynamically calculated based on the detection result, the data contribution of the fault sensor is automatically inhibited, and the fusion robustness is improved. Besides, by constructing a reward function based on a pseudo reference value, online adaptive learning under the condition of no real label is realized, the convergence time is shortened, and the requirements of real-time monitoring and high-precision fusion in a complex dynamic scene are met.

The invention relates to the technical field of calibration, and particularly discloses a compass calibration method, which comprises the following steps of: acquiring magnetic field and acceleration data in real time by using a three-axis magnetometer and an accelerometer, and establishing an error calibration model to compensate the bias, proportional error and non-orthogonal error of a sensor; constructing a Jacobian matrix by analyzing the influence of attitude orientation on the sensitivity of calibration parameters, and determining an optimal attitude by adopting a Gauss-Newton method; and calculating a parameter estimation change rate and a generalized likelihood ratio index in real time, and dynamically adjusting a batch processing window length and a forgetting factor by using fuzzy logic to adapt to the dynamic change of the magnetic field. According to the method, high-precision real-time compensation of dynamic magnetic field interference is realized, and the precision and robustness of compass calibration are improved.

The invention discloses a sea target coherent detection method based on knowledge assistance, which considers introduction of prior information of a radar sea clutter covariance matrix, adopts a two-step generalized likelihood ratio test (GLRT) criterion, constructs an adaptive coherent detection framework under a CG-LN clutter background, and improves robustness and effectiveness of sea detection radar target detection.

This invention belongs to the field of anti-spoofing and interference technology for satellite navigation and inertial navigation integrated systems. It provides a GNSS / INS tightly coupled adaptive anti-spoofing method based on generalized likelihood ratio and sequential decision-making, applicable to multi-satellite spoofing interference and micro-ramp spoofing scenarios in autonomous driving. The method includes: first, constructing a GNSS / INS tightly coupled model; second, constructing a GNSS spoofing and interference model; third, constructing a generalized likelihood ratio statistic and designing an iterative isolation strategy, improving detection accuracy through sequential hypothesis testing; and fourth, embedding the detection results into a filter and constructing an adaptive factor to achieve anomaly weighting and prediction adjustment. This method integrates spoofing detection and spoofing suppression, significantly improving the anti-spoofing capability of the navigation system.

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.

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.

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.

This invention discloses a target localization method for multiple-transmitter, multiple-receiver passive multistatic radar under interference conditions. The method includes: acquiring reference channel signals and echo signals and performing signal characterization preprocessing to obtain reference channel signal matrices and echo signal matrices; estimating channel parameters based on the reference channel signal matrices and echo signal matrices using the generalized likelihood ratio principle to construct a DPI-GRLT detector; solving the DPI-GRLT detector and calculating the detection statistics at each grid point to obtain the radar target information estimation result. This invention can maintain high localization accuracy and achieve rapid estimation and resolution of multiple targets even under weak signal conditions. As a target localization method for multiple-transmitter, multiple-receiver passive multistatic radar under interference conditions, this invention can be widely applied in the field of radar target localization technology.

This invention provides a VIO zero-velocity detection and correction method that integrates visual and inertial information. It combines information from both a camera and an IMU (Inertial Measurement Unit) to detect zero-velocity states. For the camera, in a zero-velocity state, the pixel displacement of matching points in adjacent frames determines whether the current state is zero. For the IMU, it combines triaxial acceleration and angular velocity information, applies a generalized likelihood ratio test, and determines whether the current state is zero based on whether the calculated value is less than the likelihood ratio test threshold. When both the camera and IMU information sources indicate that the vehicle is currently in a zero-velocity state, zero-velocity correction is applied: the vehicle is considered to be in a zero-velocity state, a zero-velocity observation is constructed, and applied to an extended Kalman filter for pose update. This invention can more accurately and robustly detect zero-velocity states and effectively corrects the pose estimation drift problem of VIO in a zero-velocity state.

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 multi-input multi-output passive multistatic radar target positioning method based on an interference environment, and the method comprises the steps: collecting a reference channel signal and an echo signal, and carrying out the signal representation preprocessing, and obtaining a reference channel signal matrix and an echo signal matrix; channel parameter estimation is carried out based on the reference channel signal matrix and the echo signal matrix in combination with a generalized likelihood ratio principle, and a DPI-GRLT detector is constructed; and solving the DPI-GRLT detector, and calculating detection statistics under each grid point to obtain a radar target information estimation result. According to the method, high positioning precision can still be kept under the condition of weak signals, and rapid estimation and resolution of multiple targets can be achieved. The multi-input multi-output passive multistatic radar target positioning method based on the interference environment can be widely applied to the technical field of radar target positioning.

The present invention provides a method and device for detecting variable points and their categories. The method includes: extracting a baseline from the input multi-dimensional physiological variable graph time series data to obtain a baseline sequence of the time series data of each dimension of the physiological variable graph; screening all time points in the baseline sequence to obtain candidate variable points corresponding to the time series data of each dimension of the physiological variable graph; grouping the candidate variable points according to the time gaps between the candidate variable points to obtain a plurality of candidate variable point groups; using a voting-based generalized likelihood ratio (GLR) algorithm to vote on the candidate variable points within the plurality of candidate variable point groups, and determining variable points according to the voting results; extracting variable point sequence data including variable points from the time series data of each dimension of the physiological variable graph according to the positions of the variable points; clustering the extracted variable point sequence data in the form of a tree structure split to obtain a variable point classification result. This method is beneficial to the detection of variable point positions and categories in the medical field.

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 application belongs to the field of radar target detection, and discloses a radar distributed target detection method and system when a fixed interference direction vector is mismatched. In order to solve the problem that the prior art cannot guarantee reliable detection performance when the fixed interference direction vector is mismatched, the application provides a radar distributed target detection method when the fixed interference direction vector is mismatched. First, the noise covariance matrix is estimated based on auxiliary data. Then, the amplitude vector of the fixed interference and the direction vector coordinate vector, the target coordinate vector, and the noise power mismatch amount are estimated based on the data to be measured. Further, the detection statistic is calculated based on the generalized likelihood ratio criterion, and the detection threshold is determined according to the false alarm probability. Finally, the size of the detection statistic and the detection threshold is compared. If the former is larger, the target exists; if the latter is larger, the target does not exist. The application can guarantee reliable detection performance when the fixed interference direction vector is mismatched.

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.

The present application relates to the technical field of rail transit contact rail state monitoring and fault early warning, and particularly relates to a contact rail temperature rise infrared online monitoring method and system, which comprises the following steps: firstly, mapping pixels to mileage and collecting multi-band long-wave infrared data, synchronously obtaining traction working conditions and applying phase-locked thermal excitation to generate infrared observation data in the mileage domain; then, correcting system response, calibrating radiation, and implementing phase-sensitive demodulation, and inversely obtaining an absolute temperature field according to a multi-band radiation transmission model; further inversely obtaining an equivalent line heat source according to a heat conduction equation, constructing an expected temperature rise baseline, and generating an alarm object containing a position interval and intensity by combining a generalized likelihood ratio with spatial continuity; finally, returning phase-locked and imaging parameters according to sensitivity and Fisher information. The present application realizes quantitative, locatable, low false alarm temperature rise early warning and adaptive observation.

The invention provides a non-Gaussian non-stationary signal detection method based on Riemannian manifold analysis, which belongs to the field of signal detection, and comprises the following steps: receiving a signal to be detected and a reference noise signal by a receiver, and preprocessing the signal into a covariance matrix sequence by using a Gaussian window function; a Hermitian positive definite matrix HPD manifold is constructed, Rao-Fisher measurement is selected on the HPD manifold, and the property of the HPD manifold is obtained according to the Rao-Fisher measurement; adopting a parameter estimation method on the HPD manifold to obtain the characteristics of Riemannian Gaussian distribution RGD; based on RGD characteristics on an HPD manifold, a generalized likelihood ratio detector based on RGD is constructed, the detector is applied to a covariance matrix sequence after received signal preprocessing to obtain a detection statistic, the detection statistic is compared with a detection threshold, and a detection judgment is made. The invention provides an efficient detection algorithm based on Riemannian manifold analysis aiming at the problems that weak target detection is difficult under a non-Gaussian non-stationary signal background and the performance loss of a traditional detection algorithm is large.

The present application belongs to but is not limited to the underwater detection technical field, especially relates to a kind of underwater electromagnetic detection system optimum receiver derivation method and system, comprising: S1, target echo signal feature analysis;S2, optimum receiver derivation;S3, receiver operating characteristic analysis;S4, underwater electromagnetic detection system optimum generalized likelihood ratio receiver obtains.The present application first obtains the optimum receiver model of underwater electromagnetic detection system, aiming at the existing reality problem of underwater electromagnetic detection system receiver, the present application uses generalized likelihood ratio criterion to comprehensively deduce the model of optimum receiver, and the performance of receiver is analyzed, which provides certain reference significance for underwater electromagnetic detection system receiver design.

This invention discloses a MIMO-OFDM target detection method and system considering clutter interference. Addressing the characteristics of multiple symbols and multiple carriers on an OFDM frame, this invention first combines all received symbols on a subcarrier, dividing all subcarriers into several groups. For each subcarrier and one symbol, a received signal model is established to indicate whether the target is present or absent. Then, using the generalized likelihood ratio test, the clutter covariance matrix of each subcarrier group and the maximum likelihood estimate of the target response channel are calculated. Next, based on the maximum likelihood estimate of the unknown parameters of each subcarrier group, the test statistics for all subcarriers on the OFDM frame are calculated. Finally, based on the detection threshold obtained through offline training, the presence of the target is determined by comparing it with the test statistics. This invention considers the statistical characteristics of clutter interference in MIMO-OFDM target detection, achieving a higher correct detection probability compared to traditional energy detectors, and can achieve ideal results on a hardware platform.

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.

This invention discloses a joint online monitoring method for tandem machining manufacturing systems, aiming to achieve simultaneous monitoring and intelligent early warning of equipment operating status and product quality. This method collects tool status data and workpiece dimensional and quality data at each machining stage, mines the linear relationship between tool degradation and workpiece dimensions, and constructs a system proportional risk function for the tool and a multivariate generalized likelihood ratio (MGLR) control chart for workpiece quality. Historical data is used to estimate model parameters, and based on a set system risk rate threshold and control limits from Monte Carlo simulation, the monitoring and diagnosis of tool status and workpiece quality are achieved. This method enables online joint monitoring of tool status and workpiece quality in tandem machining systems, improving equipment reliability and product consistency, reducing failure and rework rates, and ultimately achieving intelligent optimization and efficient management of the manufacturing process.

The invention provides a non-zero mean clutter neutron space signal adaptive detection method and system. The method comprises the following steps: firstly, jointly estimating a mean vector and a covariance matrix of clutters by using to-be-detected data and auxiliary data; then, on the basis of the subspace signal model, deriving a detection statistic by utilizing a generalized likelihood ratio criterion; and finally, comparing the detection statistics with a preset threshold to complete target detection. According to the method, the non-zero mean value of the clutter is explicitly estimated and compensated, so that the adaptive detector keeps a false alarm rate characteristic for a clutter covariance matrix and also has a false alarm characteristic for the non-zero mean value of the clutter in a non-zero mean value clutter environment, and the detection probability is remarkably improved; the technical problem that a traditional subspace detector fails in the environment is solved.

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.