153 results about "Aliasing" patented technology

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses an on-line brightness uniformity automatic test method and system, and relates to the technical field of brightness test, and the method comprises the following steps: obtaining a stroboscopic signal of an illumination light source in a test environment, and extracting the stroboscopic characteristic frequency of the illumination light source through a frequency domain analysis algorithm, establishing a phase tracking baseline between the frequency of the illumination light source and a sampling clock of the optical detection equipment, and constructing a phase difference mapping matrix based on the baseline; and based on the phase difference mapping matrix, constructing a self-correction model of the sampling clock of the optical detection equipment. Through phase tracking and self-correction control, optical sampling and illumination frequency are synchronized for a long time, brightness fluctuation caused by stroboscopic aliasing is eliminated, and measurement stability and consistency are improved; and through frequency domain residual suppression and brightness index re-calibration, closed-loop control from sampling to output is realized, so that the detection result is higher in precision, the judgment is more reliable, and accurate support is provided for brightness detection and quality tracing of a production line.

The invention relates to the technical field of hot melting salt storage, and discloses a photo-thermal system mirror surface micro-angle linkage adjustment method based on thermal perception, which breaks through the limitation of traditional single data monitoring and experience judgment through multi-source data fusion and triple physical constraint decoupling, and remarkably improves the fault diagnosis accuracy. And secondly, the problem of poor model timeliness caused by traditional static partitioning and long-period updating is solved through dynamic partitioning deployment and real-time contribution identification, so that the contribution degree matrix can be updated in real time along with the mirror surface state. Accurate tracking of parameters of different time scales and safe rehearsal of a control strategy are achieved through parameter hierarchical updating and digital twinborn verification, and parameter aliasing and optimization failure caused by a traditional single updating strategy are avoided.

The invention relates to the technical field of remote sensing image processing, in particular to a wetland information extraction method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining and fusing a wetland optical radar image and water level time sequence data, extracting near-infrared and back-scattering multi-source characteristic parameters to discriminate water body pixels, and extracting a water body image; and carrying out statistics on spatial connectivity aggregation to generate a water body plaque boundary, calculating an area perimeter and shape index comprehensive model to classify wetland types, and adjusting mismatching labels in combination with vegetation coverage and soil moisture to form a wetland information extraction result map. According to the method, the water body differential expression is enhanced through combined reflection and scattering characteristics, the water body and peripheral differential expression is enhanced through combined reflection and scattering characterization, boundary transition aliasing is restrained through bidirectional threshold constraint, plaque space integrity is restored and maintained through connectivity analysis, and type classification sensitivity is enhanced through morphological parameter combination. And the wetland expression accuracy and the type stability are improved through ecological attribute consistency correction.

The invention discloses a hydrostatic pressure rotary table detection method and system based on industrial vision, and relates to the technical field of machine tool precision detection. A camera-witness ring-working face-encoder unified coordinate chain is established, a zero position and a scale are anchored at a time, and five-degree-of-freedom priori is inverted; acquiring polarization / phase multiple frames under the condition of not invading an oil cavity, constructing disturbance fingerprints and mapping the disturbance fingerprints into equivalent film thickness, refractive index and scattering intensity under physical priori; learning a space-time continuous aberration compensation field and fusing with an encoder angle circular domain, peeling off eccentricity and tilting aliasing, and outputting unified angle and form and position quantification; standardized curves and indexes are formed according to ISO230-2 / 230-7, and rapid re-labeling and protection operation and maintenance triggered by a linkage threshold value are carried out. The method has the technical effects of cross-working-condition consistency, no drifting for a long time, audibility and traceability. The rotation precision is improved, the online time is shortened, and the maintenance cost and energy consumption are reduced.

The application discloses a low-altitude unmanned aerial vehicle track tracking and monitoring method based on a 5G-A integrated base station, relates to the technical field of low-altitude traffic management and communication and perception fusion, and first collects multi-source data such as integrated perception and fusion signals, environmental interference and unmanned aerial vehicle attributes, and encapsulates the data into synchronous data frames through unified timestamp and coordinate system alignment. Then, the data frames are subjected to deep fusion and anti-interference processing, noise is filtered out, and pure fusion data is generated. Further, the pure data is subjected to real-time track solving and motion trend prediction by using multi-base-station cooperative solving and prediction. Based on this, independent individual tracks are accurately stripped from complex mixed data streams by using a multi-target feature recognition and clustering separation mechanism, and the tracks are subjected to compliance verification and abnormality determination in combination with a space domain rule library. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively overcome, so that high-precision global tracking of low-altitude unmanned aerial vehicles and real-time monitoring of abnormal behaviors are realized.

The invention relates to the technical field of broadband signal synthesis and high-speed digital-to-analog conversion, in particular to an oversampling-based real-time frequency interleaving digital-to-analog conversion system aliasing suppression method and system, and aims to solve the problem of spectrum aliasing caused by non-ideal characteristics of a digital sub-band decomposition filter and an analog filter under a real-time architecture of the system. In the invention, the sub-band decomposition is completed by adopting the linear phase complementary filter bank, so that distortion introduced by a transition band of a digital filter is avoided; by restraining system parameters such as passband and stopband frequencies and local oscillation frequency of a sub-band decomposition filter, a protection band is set by an oversampling mechanism, a frequency margin is reserved for a transition band of the filter, and aliasing components are suppressed. According to the method, extraction aliasing of a digital domain and mirror image aliasing of an analog domain are avoided at the same time from the system architecture, the frequency spectrum purity of output signals is improved, the method is suitable for a real-time system for processing continuous data streams, and technical support is provided for achieving a high-performance broadband signal source.

The invention relates to the technical field of motion capture and space positioning, in particular to an acoustic motion capture method and system based on a receiving end synchronization mechanism. The core innovation of the method lies in that a system structure with a receiving end as a unified time reference is constructed, and asynchronous errors of a traditional transmitting end synchronization architecture are thoroughly eliminated. In order to solve the problems that signals of multiple transmitting nodes are difficult to judge at the same moment, multi-source signals are aliasing and the like, a receiving end synchronous unified time calibration mechanism is adopted, and a mixed signal scheduling scheme combining packet time division multiplexing and intra-group code division multiplexing is adopted. The system comprises a coordinator, an ultrasonic transmitting terminal, an acoustic receiving array and the like, the coordinator provides a global clock through an IEEE 1588v2 protocol and a PPS signal, the transmitting terminal transmits an ultrasonic signal with a unique pseudo-random noise code, the receiving array separates the signal through parallel matched filtering and records an accurate timestamp, and the acoustic receiving array receives the signal through the IEEE 1588v2 protocol and the PPS signal. And finally, high-precision positioning and attitude restoration are realized through a time difference of arrival algorithm, environmental parameter correction and an inverse kinematics model.

The invention discloses a deep sea cable fault accurate positioning method based on electromagnetic induction and transient characteristic analysis, and relates to deep sea electric power engineering. According to the technical scheme, the method comprises the steps of 1, signal excitation used for transmitting a signal source; 2, signal filtering is carried out to counteract geomagnetic background noise; 3, signal correction is carried out to solve the problems of modal aliasing and endpoint effect existing when a traditional EMD is used for processing complex signals; 4, signal feature extraction, wherein transient pulse features in the signals can be effectively enhanced; and step 5, fault positioning. The method is mainly used for non-intrusive fault detection of the power cable in the kilometer-level deep sea high-pressure environment, and high-precision fault point positioning is achieved.

Techniques for avoiding traffic blackholing problems in an Ethernet Virtual Private Network (EVPN) Data Center Interconnect (DCI) network topology that are caused by EVPN aliasing are provided. In one set of embodiments, a gateway device in the EVPN DCI network topology can determine whether an Ethernet Segment (ES) to which the gateway device is connected is an Interconnect ES (I-ES). If so, the gateway device can advertise an Auto-Discovery (AD) per ES route for the ES to one or more other network elements in the EVPN DCI network topology, without advertising any AD per EVPN Instance (EVI) routes for the ES.

The invention relates to the technical field of hydraulic engineering safety monitoring and data analysis, in particular to a dam deformation prediction method based on multi-source data dynamic fusion and a related product, and the method comprises the steps: obtaining multi-source monitoring data to construct an extended feature set; generating a weighted fusion input sequence; carrying out primary modal decomposition and secondary modal decomposition, and recombining into three types of components; obtaining a prediction result of each component; and outputting a dam deformation prediction result. According to the method, a target-oriented feature weight matrix dynamic updating mechanism is constructed, so that the problem that the actual loading state of the dam cannot be reflected by traditional static weighting is solved; by adopting a selective secondary decomposition strategy based on a modal aliasing criterion, redundant calculation of non-aliasing components is avoided while modal aliasing is effectively eliminated and high-purity characteristic components are extracted; by performing hybrid optimization on the neural network parameters, the convergence speed and population diversity are considered, the global optimality of the model parameters is ensured, and the prediction error is reduced.

The invention discloses a gyro accelerometer dynamic error modeling and calibration method considering angular motion of a base, and belongs to the field of dynamics and control. The implementation method comprises the following steps: establishing kinematics and dynamics equations of the PIGA under the triaxial angular motion of the base, and deducing a complete error model comprising a static error term, a dynamic error term and a nonlinear error term; constructing a kinetic equation including inertia tensor, a moment balance equation and servo loop influence; quantitatively analyzing the influence of the angular motion parameters of the base on PIGA output errors; based on three-axis turntable experimental design, dynamic response of each axis of the PIGA is excited through single-axis, double-axis and three-axis angular velocity input, and error model coefficients are separated and calibrated; error model parameters are calibrated by adopting a mode of combining a least square method and other data fitting algorithms; angular velocity and angular acceleration terms are separated based on direction cosine matrix transformation, and error coefficient aliasing is avoided; dynamic response data are fitted through a least square method, and all error coefficients are calibrated at a time.

The application discloses a kind of distributed array high-precision time synchronization method based on compressed sensing (CS) and secondary least square (QLS), belong to wireless communication and radar signal processing technical field.For under the restriction hardware bandwidth and strong multipath environment, traditional cross-correlation method is limited by Rayleigh resolution and sampling grid, there is the problem of serious peak aliasing, insufficient synchronization accuracy, the application proposes CS-QLS joint decoupling strategy.The method constructs frequency domain observation matrix and high-density time delay over-complete dictionary by transmitting sparse multi-tone probe signal, reconstructs sparse channel impulse response using CS, separates multipath aliasing and determines direct wave coarse synchronization boundary;Further combined with main path and adjacent grid point amplitude information, QLS is used to fit continuous domain peak position, and grid mismatch error is corrected.The method breaks through the limitation of physical bandwidth and sampling grid, and realizes nanosecond-level high-precision time synchronization in complex non-line-of-sight environment with very low computational complexity.

The invention belongs to the technical field of synthetic aperture radars, and discloses a high mountain and valley region-oriented SAR corner reflector arrangement method, which realizes accurate positioning and installation angle optimization of an SAR corner reflector by introducing an MDDG model and an R index evaluation index and combining GNSS high-precision positioning and R-D equation iterative optimization, and improves the positioning accuracy of the SAR corner reflector. High spatial precision and long-term stability of InSAR monitoring are ensured; according to the method, after the SAR corner reflector is installed, the installation angle is optimized through RCS time sequence analysis and UAV image; according to the method, the installation stability, the signal reflection quality and the observation precision of the SAR reflector can be improved, the arrangement and positioning of the reflector can be optimized especially under complex terrain and environment conditions, and the problems of signal attenuation, shielding, positioning errors, signal aliasing and the like of the reflector in the prior art are solved.

The invention provides a mental disease classification method based on a two-stage multi-atlas neural network, and belongs to the technical field of medical information intelligent diagnosis. The technical problems that early-stage features are excessively mixed due to multi-atlas information interaction in the prior art, unique disease-related specific characterization of each atlas can be diluted, and potential noise in cross-atlas connection can be possibly amplified are solved. The method comprises the following steps: firstly, on the basis of fMRI data, constructing a functional connection matrix by using various brain maps, and constructing cross-map edges through spatial proximity to obtain a joint map; then, adopting a two-stage graph neural network alternate propagation mechanism: only starting a graph inner edge to extract stable features in an odd number layer, starting a cross-graph edge in an even number layer, and realizing multi-graph information fusion in combination with an action mechanism; and finally, realizing mental disease prediction through graph-level pooling and a classifier. According to the method, premature aliasing of map features can be effectively avoided, and the rationality and robustness of cross-map information interaction are enhanced.

The application provides a hydropower station reservoir flow prediction and scheduling optimization method, which separates different scale hydrological characteristics through multi-time scale processing of multi-source data, extracts reservoir inflow characteristics by cooperating with gray correlation analysis, avoids time structure aliasing, and makes the hydrological law clearer; the prediction interval is corrected by the deviation of the observed value of the reservoir inflow, the prediction result is dynamically adjusted with the actual hydrology, the error of the initial prediction is made up, the prediction accuracy is twice improved, and the problem of inaccurate reservoir inflow prediction is solved. In addition, the corrected prediction interval is used as an uncertainty set, a double-layer robust optimization model of safety margin constraint is combined with a confidence parameter adjustment, scheduling safety and benefit are balanced; different risk level strategies are generated by weighting and combining scheduling decision variables through a risk preference parameter, and the scientificity and stability of reservoir scheduling are improved.

The invention discloses a power system oscillation parameter identification method based on improved TVF-EMD, and the method comprises the steps: carrying out the preprocessing of an original PMU data stream, calculating a local cut-off frequency LCF through B-spline approximation, solving a signal local mean value through B-spline approximation, completing the time-varying filtering empirical mode decomposition, namely TVF-EMD decomposition, screening an effective IMF based on a correlation coefficient after the verification of a stop criterion, and carrying out the recognition of the oscillation parameter of a power system. Extracting an instantaneous frequency and a damping ratio through the TKEO; the method combines TVF-EMD with TKEO, can accurately estimate the modal parameters of the power system under the normal operation condition, including frequency and damping ratio, has robustness for the instantaneous mode characteristics in the power system under the condition of low calculation complexity, reduces the problems of modal aliasing and discontinuity easily occurring in the traditional EMD method, and improves the reliability of the power system. The method provides better performance in the aspects of predefined parameters and time-varying cut-off frequency, and is suitable for analysis of non-stationary signals.

The invention relates to the technical field of biomedical radar signal processing and artificial intelligence crossing, in particular to a physical sign recognition method based on an FMCW millimeter wave radar segmented scanning algorithm, and the method comprises the steps: dividing a monitoring space range of an FMCW millimeter wave radar into a plurality of continuous and non-overlapped sub-regions; body movement, respiration and heart rate feature vectors are extracted from each sub-region, feature weights are calculated through an entropy weight method, the optimal detection sub-region of each type of physiological signals is screened out in combination with a weighted fuzzy comprehensive evaluation method, and finally parameter calculation is completed in the respective optimal region. According to the method, the problems of loss of weak signals in a non-peak region, mutual interference of body movement and respiration / heart rate signals, difficulty in adapting to spatial distribution differences of people with different heights / body types, insufficient signal aliasing recognition precision and the like caused by traditional global single-region detection are effectively solved, and high-precision, partitioned and robust detection of the body movement, respiration and heart rate signals is realized.

The present application relates to the technical field of bioinformatics and artificial intelligence, in particular to a virtual cell construction method and system, the method comprising: taking single cell gene expression matrix and perturbation condition data as input data; constructing an encoding network based on a structural causal model to obtain latent representation, and constructing a perturbation variable according to the perturbation condition data, modeling the latent representation and the perturbation variable to obtain decoupled latent representation; constructing a continuous time evolution path from an initial distribution to a target distribution based on a flow matching model, and determining state changes according to the decoupled latent representation; numerically solving the continuous time evolution path to output virtual cell expression data. The present application is used to solve the problems of causal aliasing, insufficient distribution out-of-distribution generalization ability, unstable generation process and difficulty in counterfactual reasoning in the existing single cell perturbation prediction method.

This invention relates to a method and apparatus for designing seismic data acquisition trace spacing. The method includes: determining different first apparent velocities of effective waves and different second apparent velocities of interfering waves based on raw single-shot data; determining a first frequency corresponding to a first preset minimum energy value for each effective wave and a second frequency corresponding to a second preset minimum energy value for each interfering wave; determining a first candidate maximum wavenumber for each effective wave that does not produce spatial aliasing based on the first frequency and the first apparent velocity, and determining a second candidate maximum wavenumber for each interfering wave that does not produce spatial aliasing based on the second frequency and the second apparent velocity; determining a target maximum wavenumber from the first and second candidate maximum wavenumbers, and then determining the seismic data acquisition trace spacing for the target work area; and designing a trace spacing that can balance exploration costs and technical requirements using old single-shot data, saving costs and fully utilizing the value of old data.

The invention discloses an eddy current detection-based steel-cored aluminum strand breakage defect identification method and equipment, and relates to the technical field of steel-cored aluminum strand breakage defect identification, and the eddy current detection-based steel-cored aluminum strand breakage defect identification method comprises the following steps: installing an annular vector coil array probe formed by a plurality of excitation coils on a to-be-detected steel-cored aluminum strand, and controlling the driving parameters of each excitation coil to identify the breakage defect of the steel-cored aluminum strand; constructing a vectorized excitation magnetic field on the surface of the steel-cored aluminum strand; based on the vectorized excitation magnetic field, a multilayer selective penetration strategy is constructed, and excitation mode sets used for detecting peripheral areas of the surface layer aluminum strand, the inner layer aluminum strand and the steel core are sequentially generated; collecting eddy current signals in each excitation mode and performing layered feature extraction to form corresponding layered feature vectors; and performing fusion discrimination based on the hierarchical feature vectors, and generating an identification result of the broken strand defect of the steel-cored aluminum strand. According to the invention, the problems of excitation magnetic field distortion, signal aliasing and insufficient deep defect identification sensitivity in a multi-layer stranded structure in traditional eddy current detection are solved.

This invention discloses a Sigma-Delta analog-to-digital converter (ADC) with aliasing suppression function. The Sigma-Delta ADC includes a preset loop filter module, a sampling module, a quantizer, and a preset feedback DAC module. The preset loop filter module, sampling module, and quantizer are connected in series. The preset loop filter module includes a chopper. The preset feedback DAC module is connected between the loop filter module and the quantizer to suppress aliasing introduced by the chopper and maintain the effective signal sampled by the sampling module within the corresponding frequency band. All poles of the transfer function of the preset feedback DAC module are located at the aliasing points introduced by the chopper, thus achieving the function of suppressing aliasing.

This invention discloses a lithology identification method based on hybrid mode decomposition and fuzzy entropy-PSO optimization of drilling signals, belonging to the field of integrated deep earth space exploration and drilling geophysical exploration technology. The method first acquires real-time seismic monitoring signals during drilling; then, it uses an empirical and variational hybrid mode decomposition (EVMD) method to decompose the original signal, removing strong noise disturbances; finally, it employs a particle swarm optimization (PSO) algorithm combined with adaptive optimization using fuzzy entropy as the objective function to obtain the optimal parameter combination [K, α] of EVMD, and decomposes it to obtain the optimal modal data and construct a multi-dimensional feature vector, completing the real-time identification and judgment of strata lithology and rock mass structural characteristics. This invention effectively solves the technical problems of traditional methods, such as difficulty in separating strong noise, fixed parameters that cannot be adaptively adapted, mode aliasing, and poor real-time performance. It achieves extremely high lithology identification accuracy and is suitable for deep earth space development, deep resource exploration, and intelligent construction of underground engineering projects.

The invention belongs to the technical field of material ultrasonic detection, and relates to a composite board interface defect classification and identification method and system based on echo feature fusion, and the method comprises the steps: extracting ultrasonic echo multi-dimensional acoustic features; calculating a local acoustic feature aliasing degree; combining the acoustic impedance value, the local acoustic feature aliasing degree and the sample scarcity degree to generate a dynamic acoustic cost weight; reconstructing a random forest model, and constructing a cost-sensitive voting factor by using an information gain index and a cost weight; inputting the multi-dimensional acoustic features of the to-be-detected composite board, and executing weighted summation to obtain a classification result; according to the method, the feature aliasing degree can be evaluated more accurately, the sample scarcity and acoustic characteristics are comprehensively considered, the importance of high-risk defect features in decision making is ensured, the misjudgment problem is effectively solved, and the accuracy and reliability of composite board interface defect classification and recognition are improved.

This invention discloses a target detection method applicable to underwater acoustic and optical images. The method first constructs and differentially preprocesses underwater acoustic and optical labeled datasets, then builds an end-to-end target detection model consisting of a backbone network, encoder, and decoder. A residual network is used to extract multi-scale features. The encoder includes a same-scale feature interaction module and a cross-scale feature fusion module. The same-scale feature interaction module suppresses extreme value activation and enhances feature interaction stability through dynamic hyperbolic tangent modulation, and uses wavelet domain sampling to preserve high-frequency details and reduce sampling aliasing. After model training and evaluation using training, validation, and test sets, the input image to be detected outputs the target category, bounding box, and corresponding confidence score. This invention achieves compatible detection of two types of underwater images, improves the accuracy and robustness of target detection in complex underwater environments, and combines real-time performance with generalization ability, making it suitable for scenarios such as marine resource surveys and underwater security.

The invention belongs to the technical field of radar signal processing, and relates to an adaptive GMM radar signal pre-sorting method and system based on data field initialization. The method comprises the steps of pulse feature standardization, pulse data field construction, pulse cluster number recognition and potential center extraction, Gaussian mixture model establishment and initialization, Gaussian mixture model solving and clustering result output. The method has relatively high universality and expansion capability; high-precision and high-robustness intelligent pre-sorting of radar signals is realized, the problems of unstable clustering and easy falling into local optimum caused by random initialization are effectively avoided, and the stability and convergence efficiency of parameter estimation are improved; the accuracy of radar signal clustering and pre-sorting in a multi-target aliasing scene is effectively improved; the adaptive capacity of the model under different densities and complex background conditions is improved; the adaptive capacity of the model to a high-dimensional, dense and parameter overlapping data structure is enhanced, and the robustness of radar pulse pre-sorting is enhanced.

The application provides a Feature-level SMOTE-based complex equipment unbalanced fault diagnosis method, which maps samples to an embedded space and uses MLP to perform fault diagnosis on the samples in the embedded space. The Feature-level SMOTE-based complex equipment unbalanced fault diagnosis method provided by the application has a feature-level data enhancement mechanism, so that the generated fault samples and normal samples have a smaller degree of aliasing, which is more conducive to normal and fault diagnosis; the GRU in the model can capture complex relationships in multi-dimensional monitoring data and better represent the characteristics of original samples; the DSGRU can resist the influence of noise when performing space mapping, and effectively solves the noise problem of engine monitoring data.

The invention provides a multi-station radar passive time difference positioning accurate pulse matching method and system based on signal correlation, and aims at realizing accurate matching of different signal pulses in a complex battlefield situation and improving the accuracy and anti-noise capability of passive positioning. The method belongs to the technical field of passive time difference positioning. According to the method, signal waveform cross-correlation analysis is introduced, the matching reliability of pulse pairs is dynamically verified through time domain waveform similarity calculation of signals received by the main station and the auxiliary station, and the pulse matching method which does not need to carry out signal sorting firstly under the dense pulse condition is innovatively provided. Specifically, when parameters such as time difference and frequency difference of two paths of pulses fall into a preset threshold range, cross-correlation operation is further performed on the two paths of signals, and a cross-correlation function peak value and corresponding time delay are extracted to eliminate false correlation caused by noise interference or asynchronous signal aliasing. The invention provides a solution with robustness and practicability for a passive positioning system in the fields of electronic reconnaissance and the like.

The invention discloses a corn yield high-temperature loss prediction method based on machine learning, particularly relates to the technical field of high-temperature loss prediction, and realizes refined, dynamic and spatialized prediction of corn yield high-temperature loss by constructing a machine learning prediction system based on high-temperature-drought composite coupling characteristics. The method effectively captures the interaction of high temperature and drought stress under an unsteady climate condition, quantifies the composite stress characteristics through the joint analysis of a high temperature stress function and a drought response matrix, reduces the misjudgment caused by characteristic aliasing through nonlinear orthogonal decomposition and mutual information analysis, and improves the reliability of the system. According to the method, a deep learning model is trained, network parameters are optimized and prediction deviation is dynamically corrected by using a composite stress confusion distortion index constraint deep learning model, stable and robust prediction of the yield loss under an extreme climate condition is realized, and meanwhile, through feature contribution degree analysis and spatial grid mapping, the spatial and temporal distribution of the yield loss under a composite stress condition can be visually displayed.