99 results about "Correlation matching" patented technology

The invention discloses a monitoring image multi-target tracking method based on deep learning, and relates to the technical field of digital image processing, and the method comprises the following steps: S1, generating a multi-scale feature pyramid; s2, extracting causal feature vectors; s3, generating a discrete codebook index; s4, constructing a dynamic graph; s5, inputting the dynamic graph into an improved ASTGCN network, iteratively fusing space-time attention weights and Hamilton dynamics evolution characteristics between nodes through cascaded Hamilton space-time blocks, and generating a final prediction state of each historical track node; s6, solving an optimal correlation matching matrix by using a Hungary algorithm; and S7, extracting all active track information. According to the method, the limitations of identity drift caused by variable appearance characteristics and inaccurate prediction caused by lack of physical priori in a traditional multi-target tracking method are overcome, and an efficient, accurate and robust solution is provided for intelligent video monitoring.

The invention belongs to the technical field of artificial intelligence and computers, and particularly relates to a multi-modal data intelligent identification and early warning system for a low-altitude safety emergency scene. The objective of the invention is to solve the problems of difficult small low-altitude target detection and lagging emergency response in a complex urban environment. The system integrates radio frequency, optical, acoustic and meteorological multi-source sensing nodes, and target fusion tracking is realized through space-time reference synchronization and cross-modal correlation matching; threat cognition is carried out by using a deep neural network with an attention mechanism, three-dimensional risk assessment is carried out by combining Monte Carlo simulation and a dynamic weighting algorithm, and yellow, orange and red three-level early warning instructions are generated; an early warning result is pushed to a multi-department collaborative response platform through an encryption interface, and the whole process is traceable through block chain evidence storage. According to the system, the low-altitude security situation awareness precision and the emergency response efficiency are remarkably improved.

The invention discloses a satellite Internet of Things uplink signal detection method and a satellite Internet of Things uplink signal detection system, which integrate traditional signal processing and advanced spiking neural network technologies, realize high-precision frame synchronization by performing two-dimensional cross-correlation matching in a time-frequency domain, and perform low-power-consumption and high-robustness symbol judgment by using an event-driven spiking neural network. Compared with a traditional method based on FFT or matched filtering, the method has higher adaptability and detection performance in a satellite Internet of Things scene with large Doppler frequency shift, low signal-to-noise ratio and dense users.

The invention discloses a visual anti-occlusion tracking method based on AIS and video fusion. The method comprises the following steps: synchronously obtaining AIS data and corresponding video data; preprocessing the AIS data, predicting latitude and longitude coordinates of the AIS data, projecting the latitude and longitude coordinates to a video pixel plane, and generating a ship AIS track; meanwhile, identifying a ship bounding box in a video frame by using a video detection algorithm, and tracking by using a three-stage association anti-occlusion tracking algorithm; the similarity between the AIS trajectory and the video trajectory is calculated through a dynamic time warping algorithm, and the correlation matching of the AIS trajectory and the video trajectory is realized by using a Hungary matching algorithm, so that a stable and anti-shielding ship tracking trajectory fusing AIS and video information is obtained. According to the invention, the problem that pure visual tracking is easy to fail under the shielding condition is effectively solved.

The invention discloses a grid exhaustion search cross-pulse pairing method based on correlation matching, and belongs to the technical field of radio signal positioning and detection. According to the method, aiming at the problems of large cross-pulse signal pairing calculation amount and low accuracy in a multi-aircraft cooperative positioning system, efficient and accurate cross-pulse pairing is realized by establishing a cross-pulse boundary model, a symbol matching constraint mechanism and a related matching verification mechanism. The method comprises: determining upper and lower bounds of a cross-pulse according to a geometrical relationship; correcting the search range by using symbol matching; solving a candidate target solution in the limited interval by adopting grid exhaustion search; and a unique effective solution is screened through time and geometry consistency verification. The method can effectively reduce the calculation complexity, improves the cross-pulse pairing precision and uniqueness, and is suitable for a passive positioning system in a multi-target, multi-base-station and low-signal-to-noise-ratio environment. Experiments prove that the pairing time can be shortened by about 50% while high positioning precision is kept, and the method has high engineering application value.

The invention provides a dialogue data processing method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and is suitable for the fields of financial science and technology and medical health. The method comprises the steps of obtaining a target question; obtaining historical dialogue data according to the target question, wherein the historical dialogue data comprises candidate historical dialogue groups; screening the candidate historical dialogue groups according to the target question to obtain a target historical context; performing answer generation according to the target question and the target historical context to obtain a target answer; combining the target question and the target answer to obtain a target dialogue; performing correlation matching according to the target dialogue and each candidate historical dialogue group to obtain a group matching degree; and updating the candidate historical dialogue groups according to the group matching degree and the target dialogue. The dialogue data is grouped and sorted, and the dialogue data processing accuracy is improved.

The invention discloses a real-time fracture characteristic parameter estimation method based on active fault data, and belongs to the technical field of earthquake early warning. The method comprises the steps of receiving and processing strong vibration data in real time; carrying out identification and binarization processing on a fracture near-field area of the observation seismic oscillation field; constructing a general fault fracture template library; dynamically generating a specific fault fracture template library in real time; virtual stations are introduced into a station network sparse area and an edge area, and an observation blind area is complemented through weighted fusion of an actual observation value and a GMPE predicted value; performing correlation matching on the real-time strong vibration observation data and a fault fracture template, and searching an optimal template; and outputting the current fault fracture characteristic parameters based on the optimal matching template. According to the method, the existing active fault database is adopted to dynamically generate the fracture template to process the known fault fracture caused by the active fault, and the universal template is utilized to process the unknown and hidden fault fractures, so that the accuracy of real-time estimation of the seismic motion field in the great earthquake fracture process is remarkably improved.

The invention discloses a mathematical model-based chemical process simulation data management system and method, and relates to the technical field of data management. The system comprises a data feature system construction module, a dynamic mapping rule generation module, a cross-scale collaborative data processing module and a matching relation adaptive optimization module. The method comprises the following steps: constructing a multi-scale data quantization feature system and generating feature vectors; establishing a dynamic mapping mechanism and an association matching rule set, wherein the association matching rule set comprises a cross-scale collaborative weight and a working condition dynamic threshold factor; checking data in multiple dimensions, complementing missing attributes and pushing the data as required; the feature vectors and mapping rules may be iteratively optimized through deviation analysis. The problem of data and model hidden mismatching is solved, working condition fluctuation is adapted, cross-scale simulation accuracy and robustness are improved, and safe and efficient circulation of data is achieved.

This invention relates to the field of nonlinear node detection technology, and particularly to a nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting. The system includes: acquiring fundamental and second harmonic echo signals from multiple detection nodes within the detection area in the S-band to ensure a sufficient data source for the entire detection process; constructing harmonic excitation response features and generating a fingerprint database to improve the accuracy and targeting of subsequent node identification; dynamically capturing the node's current characteristic information by transmitting S-band frequency sweep detection signals and collecting echo harmonic components to extract real-time fingerprints, enhancing the timeliness of node detection; identifying matching concealed electronic nodes by performing multi-dimensional time-series correlation matching between real-time harmonic fingerprints and fingerprints in the fingerprint database, effectively filtering out irrelevant interference; and generating a topology map and marking abnormal nodes, enabling users to quickly grasp the overall situation and rapidly locate anomalies.

The application provides a compressed sensing channel estimation method and device based on a PN correlation threshold path search. The method comprises: performing correlation matching processing on an original reference signal and a PN sequence to obtain a plurality of matching values; selecting atoms satisfying a condition from a sensing matrix according to the plurality of matching values, that is, performing correlation matching processing on the original reference signal and the PN sequence by using the sharp autocorrelation characteristics of the PN sequence, and selecting atoms based on the matching values obtained by the matching processing to suppress non-ideal atoms caused by noise, so as to screen the atoms in the OMP algorithm reconstruction process, and finally obtain a reference signal without clutter. Since the method of the application is a purification method for the received original reference signal, the hardware system does not need to be processed, so the system requirement is reduced. In addition, the method of the application avoids the problem of multi-path signals entering the beam when the propagation environment is relatively complex, thereby improving the purity of the original reference signal.

The invention relates to the technical field of space target correlation and positioning, and discloses a space target correlation matching and coordinate determination method, device and equipment and a medium, and the space target correlation matching and coordinate determination method comprises the steps: predicting the tracks of a plurality of observation stars and a plurality of space targets, and determining angle measurement observation data according to the target tracks; performing coordinate transformation on the angle measurement observation data; determining a projection distance cost matrix according to the unit relative position vector obtained by coordinate conversion; according to the projection distance cost matrix, matching space targets observed by the plurality of observation stars by adopting a Hungary allocation algorithm, and determining target position coordinates according to the position coordinates of the observation stars and the unit relative position vector in a matching result; according to the method, the unit vector is obtained by predicting the trajectory of the observation star and the space target and converting the coordinates, the cost matrix-based Hungary algorithm is constructed to match the target, and the space target coordinates of the position are fitted, so that the efficiency and accuracy of determining the space target coordinates are improved.

The invention provides an unmanned aerial vehicle target positioning method and device based on cooperation of multiple observation points and a medium, and the method comprises the steps: building a dynamic and static target three-dimensional space positioning physical architecture, and building an unmanned aerial vehicle target recognition model based on bidirectional cross-dimension feature enhancement; an image-based unmanned aerial vehicle multi-target association matching algorithm is proposed to eliminate background interference and correct view angle influence, and an end-to-end deep neural network is adopted to model a mapping relationship between multiple observation points and unmanned aerial vehicle targets, so that multi-source feature fusion and positioning precision improvement are realized; according to the technology, the problems of poor recognition robustness, low positioning precision and the like caused by small target information loss, view angle transformation interference and optical sensor measurement deviation in the actual unmanned aerial vehicle target positioning process are solved, and efficient and reliable unmanned aerial vehicle target positioning technical support is provided for airspace monitoring, unmanned aerial vehicle compliance management, security patrol and other scenes.

The present application relates to the technical field of securities information processing, and discloses a securities information intelligent accurate pushing method and system based on big data, which comprises the following steps: collecting multi-dimensional data sources in a securities big data platform, separating interest feature vectors and information content feature vectors about users from the multi-dimensional data sources, and calculating the correlation matching degree of the interest feature vectors and the information content feature vectors; determining the real-time scene attributes of the users, calculating the content adaptation priority of securities information in the securities big data platform in the pushing process; obtaining historical transaction frequency, holding size and information interaction rate data of the users, calculating the demand urgency of the users about the securities information, determining the target information pushing accuracy of the users; calculating the effective pushing weight of the securities information, combining the target information pushing accuracy, performing content arrangement and pushing processing on the securities information, and obtaining the pushing result. The present application can improve the accuracy of securities information intelligent accurate pushing based on big data.

The application discloses a kind of robot environment perception systems based on multi-radar fusion, it is related to robot environment perception field.The system ingeniously fuses the radar sensor data of multiple different types, different deployment locations, realizes the high-precision, high reliability, low power consumption perception to the three-dimensional environment around robot by advanced feature extraction, correlation matching, information fusion and dynamic environment modeling etc.Technologies.System can effectively identify and track static obstacles, dynamic targets, and accurately estimate its position, velocity, attitude and size, provide reliable environmental information for the autonomous navigation, obstacle avoidance, path planning, target interaction etc.of robot.The application greatly improves the environmental perception ability of robot in various complex scenes by fusion strategy, dynamic adaptive algorithm and effective use of redundant sensor information.

The application discloses a kind of image matching's mode processing data image's interpretation method and system, including automatically segmenting actuator data, obtaining actuator displacement angle data and actuator action angle data;Displacement angle scatter diagram and action angle scatter diagram are respectively drawn;Displacement angle scatter diagram and action angle scatter diagram are respectively transformed into displacement servo curve and action servo curve;Displacement servo curve and action servo curve are grayed, obtain gray scale diagram;Gray scale diagram is binarized, and the correlation matching of normalized image is carried out, according to several normalized matching results, determine relevant peak threshold;If normalized matching result is greater than relevant peak threshold, then actuator displacement angle data and actuator action angle data registration is successful, and composite image is drawn;If less than relevant peak threshold, then registration is not successful, composite image is drawn, and troubleshooting is carried out.The man-power, time consumption when mass production is avoided, and the correctness of interpretation is guaranteed.

The invention discloses a multi-source perception data fusion method, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: performing time-space synchronization processing on initial vehicle end sensing data and initial field end sensing data to obtain target vehicle end sensing data and target field end sensing data; performing association matching on the target vehicle end sensing data and the target field end sensing data to obtain a matching result, and performing weight distribution on the target vehicle end sensing data and the target field end sensing data by adopting a confidence evaluation mode to obtain a weight distribution result; and according to the matching result and the weight distribution result, fusing the target vehicle end sensing data and the target field end sensing data to obtain a fusion result. The technical problem of how to effectively fuse multi-source sensing data so as to improve the sensing robustness and safety of the automatic driving system in a complex environment is solved.

The application relates to the technical field of data management, and particularly discloses an industrial data management method, system, medium and equipment, the method comprising the following steps: performing semantic correlation matching on key semantic elements of standardized industrial data in a target industrial field and a preset industrial knowledge graph, so as to identify an implied business relationship existing in the standardized industrial data; constructing the standardized industrial data with the business correlation into a dynamic data cluster; performing time sequence correlation degree evaluation on the dynamic data cluster, obtaining a time sequence dependency relationship between the standardized industrial data in the dynamic data cluster, and performing time sequence extrapolation on the time sequence dependency relationship to obtain a state evolution path; embedding the state evolution path into the standardized industrial data by taking a public business object identifier of the standardized industrial data in the dynamic data cluster as an aggregation key, so as to construct a dynamic data view; and the application can improve the management efficiency of industrial data.

This invention relates to the field of intelligent operation and maintenance and fault diagnosis technology for photovoltaic power plants, and particularly to a photovoltaic power generation monitoring method and system based on artificial intelligence. The method includes the following steps: parsing the time-of-use power generation data of the target photovoltaic modules, retrieving the total cumulative power generation for the entire day, performing normalization calculations for irradiance fluctuations, and constructing an actual power generation percentage sequence; performing time-domain cross-correlation matching between the actual power generation percentage sequence and a preset historical benchmark percentage sequence to determine the optimal matching time shift, and generating an aligned benchmark sequence based on the optimal matching time shift; calculating the negative distortion component sequence of the actual power generation percentage sequence relative to the aligned benchmark sequence. This invention, by normalizing power generation and using solar trajectory for spatiotemporal mapping, achieves remote inference of the physical location of interference sources and automatic classification of fault characteristics without relying on image equipment, effectively reducing false alarm rates and improving troubleshooting efficiency.

The invention provides a pollution diffusion prediction method and device based on gamma radioactivity and a wind field, and relates to the technical field of pollutant monitoring, and the method comprises the steps: carrying out the feature peak extraction, nuclide recognition and concentration calculation of a gamma radioactivity energy spectrum data set, and obtaining a nuclide concentration set; constructing a three-dimensional wind field diffusion field through the topographic data and the wind field data set; performing coupling calculation and correlation matching on the nuclide concentration set and the three-dimensional wind field diffusion field through a Gaussian diffusion model, and constructing a correlation matrix of the nuclide concentration and the wind field; obtaining a nuclide half-life period, a gamma-ray emissivity and a terrain, and performing weighted fusion on elements in the incidence matrix through the nuclide half-life period and the gamma-ray emissivity to obtain a fusion coefficient; correcting the fusion coefficient through the terrain to obtain a diffusion trend coefficient set; and constructing a radioactive pollution prediction model, and according to the diffusion trend coefficient set, performing diffusion prediction in a future time period through the radioactive pollution prediction model to obtain a pollution diffusion prediction result.

The invention provides a relay tracking method for intersection multi-target fusion, and the method comprises the steps: confirming an overlapping region, a non-overlapping region and a blind region of all thunder-vision integrated equipment, and converting a target position to a global space coordinate system; the multiple pieces of thunder-vision integrated equipment target data under the global space coordinate system are listed into an overlapping region target set and a non-overlapping region target set respectively; target matching is carried out on the targets in the overlapping region target set; updating the target information to a corresponding global tracking set according to the target matching condition of the overlapping region; and maintaining a global tracking set. According to the relay tracking method for intersection multi-target fusion, on the basis of a multi-layer multi-feature matching mechanism, information such as the multi-target multi-device sensing position, speed and category is fully utilized, the global target optimal association matching problem is converted into a minimum-cost maximum-flow model, global optimal matching is achieved, the situation that target matching is unstable is reduced, and the tracking efficiency is improved. And the trajectory tracking stability is greatly improved.

The invention relates to the technical field of image data processing, in particular to a PCB bare board blind hole defect detection method and system based on AOI, and the method comprises the steps: obtaining a to-be-detected PCB bare board blind hole image and a standard PCB bare board blind hole image; matching the blind hole image of the bare PCB to be detected with a blind hole image of a standard bare PCB by using an improved normalized cross-correlation matching algorithm to obtain a maximum normalized cross-correlation coefficient of the position of each pixel point in the blind hole image of the bare PCB to be detected, and if the maximum normalized cross-correlation coefficient is smaller than a set threshold value, determining that the blind hole image of the bare PCB to be detected is a blind hole image of the bare PCB to be detected. And if so, determining that the blind hole image of the bare PCB to be detected has defects. The method solves the problem that the existing algorithm is not high in detection accuracy when facing complex noise.

The invention discloses a chemical security data intelligent preprocessing and association matching system and method, and belongs to the technical field of chemical safety production management. The problems that in the prior art, data isomerism is high, violation type standardization is insufficient, severity and processing measure classification depend on manual judgment, table data and text description relevance is poor, and statistical report generation lags behind are solved. The method provided by the invention comprises the steps of receiving and classifying security and protection table and text data, checking data integrity, generating a unique association identifier, executing violation type standardization and severity grading, automatically classifying processing measures, establishing table and text association based on semantic matching, and generating a security and protection data statistical report. And automatic cleaning, standardization and intelligent analysis of security and protection data are realized. The data processing efficiency and the safety early warning accuracy can be remarkably improved, and the method is suitable for safety production management and risk monitoring work in high-risk industries such as chemical engineering, petroleum and pharmacy.

The invention discloses an integrated intelligent traceability method for a composite material hydrofoil frequency locking mechanism in the technical field of hydrodynamics, and the method comprises the following steps: firstly building a synchronous measurement test platform, and synchronously collecting a wake flow field image, a three-dimensional deformation field and a vibration signal in a hydrofoil frequency locking and primary cavitation state; through data calibration and alignment, gray processing and dynamic image enhancement, vortex mode characteristics, high-confidence vibration dominant frequency and structure dominant mode are extracted by combining a 3D-DIC algorithm, dynamic mode decomposition and other technologies; a frequency triple is identified through self-adaptive spectrum analysis and intelligent association matching, vibration contribution is quantized through multi-stage verification, a structured mechanism analysis report is finally automatically output, closed loop from multi-source data to intelligent diagnosis is completed, reliable support is provided for frequency locking mechanism traceability, and the method is suitable for large-scale popularization and application. And a quantitative causal chain of vortex source-deformation-vibration is automatically established through an intelligent algorithm, so that intelligent diagnosis and mechanism analysis of the frequency locking phenomenon are realized.

This application relates to the field of autonomous driving technology, specifically to a method for acquiring road structured data, an intelligent device, and a readable storage medium. It aims to solve the technical problem of how to acquire road static structured ground truth data with high generalization and high recall, along with time-series tracking results. To this end, this application acquires the observation results and ground truth annotation results of static road elements within a preset time window of the test area. It then performs correlation matching between the observation results and the ground truth annotation results to obtain the correlation matching results of the static road elements in the test area. Based on the correlation matching results, it obtains the road structured data ground truth of the test area. This enables long-term, stable temporal matching of the static road elements corresponding to the observation results and the ground truth annotation results within a preset time window. The correlation matching results effectively compensate for problems such as missed detections and unstable detection in the observation results, thereby improving the accuracy of the road structured ground truth.

This invention discloses a target tracking method and system based on a cross-correlation matching enhanced Siamese network, comprising: cropping a video sequence of the target to be tracked to obtain template images and search images for all frames; inputting the template images and search images into a constructed and trained cross-correlation matching enhanced Siamese network to extract template features and search features from the template images and search images; performing cross-correlation matching on the template features and search features to obtain cross-correlation features; encoding bounding box information in the template images to obtain bounding box encoded features; performing classification and regression calculations on the fused features of cross-correlation features and bounding box encoded features to obtain corresponding classification score maps and regression prediction maps; and obtaining the final position of the target on the video sequence frames based on the position with the largest response value in the classification score map and the offset of the regression prediction map. This invention has strong adaptability and high accuracy in tracking complex scene changes.

The application provides an environmental protection equipment control method, system, device and medium, and belongs to the technical field of environmental protection equipment control. The method sets control parameters such as a working temperature threshold, a flow regulation range and a pressure safety limit, then collects running state data of each execution unit of the controlled environmental protection equipment, extracts and encodes features of the control parameters to obtain a low-dimensional feature encoding vector, performs hierarchical filtering processing on the running state data based on feature weights to obtain a key feature aggregation vector, combines historical data to perform correlation matching analysis on the two to obtain a correlation matching encoding vector, and finally inputs the correlation matching encoding vector into a decision maker to generate a control instruction to instruct equipment operation adjustment. Through accurate data processing and correlation analysis, the method improves the precision and adaptability of environmental protection equipment control, improves equipment operation efficiency and environmental protection effect, and has strong practicality and promotional value.

The application discloses a multi-target perception fusion method in a multi-unmanned aerial vehicle search process. First, an unmanned aerial vehicle A uses a camera to search and track a target, obtains a target image position and number, and calculates a three-dimensional position of each target. If no new target appears, target information in a fusion target pool is updated; otherwise, the information component of unmanned aerial vehicle A is deleted from the fusion target pool, a target pool subset to be matched is obtained, an adjacent relationship bipartite graph is constructed, the bipartite graph is simplified, and matching is performed by taking the maximum matching number and the minimum shape gap as a correlation measure. Finally, an updated multi-target fusion result is calculated from each target information in the fusion target pool. The application can realize multi-target perception and fusion in a multi-unmanned aerial vehicle search process, retains shape features of original calculation results, uses an adjacent judgment and a shape matching strategy, and solves the problem of a high correlation matching error rate of a traditional matching fusion algorithm when a target is low-speed, gathered, and the like.

The invention discloses an indoor distribution leakage regulation and control method, device and equipment based on MDT data and a medium, and the method comprises the steps: obtaining an MDT data set of a target indoor distribution cell, and carrying out the space correlation matching of the MDT data set according to a pre-obtained relation table of the indoor distribution cell and an interest region, dividing the MDT data set into an associated MDT grid subset and an unassociated MDT grid subset; wherein the region of interest is divided according to actual map data of a target indoor distribution cell; performing indoor distribution leakage distance calculation on the unassociated MDT grid subset through space coordinate analysis, so as to divide the unassociated MDT grid subset into a plurality of leakage grid subsets located in different leakage distance intervals; and performing statistical analysis on the leakage grid subset to generate an indoor distribution leakage table so as to generate a corresponding regulation and control signal, and triggering remote regulation and control of indoor distribution system equipment. According to the invention, the accuracy of regulation and control of the indoor distribution leakage can be improved through accurate quantitative evaluation of the degree and range of the indoor distribution leakage.

The application discloses a power equipment temperature rise fault diagnosis method, system, device and medium, and comprises the following steps: acquiring temperature values of power equipment scattered points; reconstructing the temperature values of spatially dispersed sampling by a tensor block correlation matching method to obtain low-resolution three-dimensional temperature field reconstruction data; filling data of the low-resolution three-dimensional temperature field reconstruction data by a low-rank tensor super-resolution method to obtain high-resolution three-dimensional spatial temperature field data; and comparing the high-resolution three-dimensional spatial temperature field data with a differentiated threshold to perform power equipment temperature rise fault early warning according to a comparison result. The application improves the accuracy of power distribution switch cabinet temperature rise fault diagnosis.

This invention discloses an airborne magnetic anomaly detection method based on a stochastic resonance system, comprising the following steps: S1: Acquiring geomagnetic field data. The data is processed using airborne magnetic compensation technology. S2: Establishing a stochastic resonance detection system and calculating a binary hypothesis detection statistic. The binary hypothesis detection statistic is the standard deviation of the output of the stochastic resonance system. S3: Calculating a threshold and detecting the target. The threshold is calculated using the unit averaging method. The presence of a target is determined by comparing the binary hypothesis detection statistic with the threshold. A target is considered to exist when the binary hypothesis detection statistic of any subsystem in the stochastic resonance system is greater than the corresponding threshold; the measurement is considered pure noise only when both subsystem statistics are less than the threshold. This method addresses the problems of low signal-to-noise ratio and the severe influence of noise type on correlation matching detection methods during magnetic anomaly detection in complex environments.