1916 results about "Covariance matrix" patented technology

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention discloses a knowledge cross validation question-answering method and system for reducing hallusion of a large language model, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: generating results through multiple times of sampling: when a user puts forward a question, controlling the large model to perform multiple times of sampling, and generating a specified number of results; calculating hidden state related indexes: extracting the hidden state of the last token of the middle layer of the large model corresponding to the result, and calculating covariance matrixes and answer discrete feature values of the hidden states; mLP model prediction: inputting the discrete feature value of the answer and the length of the answer into a multilayer perceptron MLP, and outputting a hallucination-free probability; querying and summarizing a knowledge graph; and calculating a final illusion-free score and outputting a result. According to the method, the answer quality and credibility of a large language model can be remarkably improved, and the method is particularly suitable for application scenes with extremely high requirements on the accuracy of single-mode text generation contents.

The invention discloses a self-adaptive battery life calibration method for cloud-side cooperation, and belongs to the crossing field of an energy storage system and cloud-side cooperation calculation. According to the invention, a cloud-edge double-layer collaborative framework is provided; an edge end estimates the health state and the residual life of a battery in real time through a recursive least square extended Kalman filtering model; the error observer calculates a prediction error based on a sliding window, and a dynamic threshold triggers an uploading mechanism; the edge end adopts an auto-encoder to compress original time sequence features into abstract vectors, and the abstract vectors and error statistics are uploaded together; the cloud performs incremental learning by using a deep sequential network, and only finely adjusts tail level parameters of which the gradient sensitivity exceeds a threshold value to generate a correction value; and the correction value is compressed and issued to an edge end, local model parameters are updated through weighted fusion, and a covariance matrix is adjusted. The method realizes high-precision life prediction and dynamic calibration, remarkably reduces the communication load, and is suitable for electric vehicles, power grid energy storage and other scenes.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention relates to the technical field of dam safety monitoring, in particular to a method and system for improving dam GNSS deformation monitoring precision through base station and observation station combined network adjustment. The method comprises the following steps: arranging base stations and monitoring points to form a GNSS monitoring network; gNSS original observation data are collected in real time and preprocessed; constructing a joint network adjustment model, and taking base station coordinates as constraints and monitoring point coordinates as to-be-estimated parameters; performing baseline resolving based on the double-difference carrier phase observed quantity to obtain a baseline vector and a covariance matrix thereof; carrying out overall adjustment on the baseline vector by adopting a robust estimation method, and solving an optimal coordinate estimated value and precision information of the monitoring point; carrying out deformation analysis on the basis of the adjusted coordinate time sequence, and extracting tendency, periodicity and abnormal deformation; and outputting a deformation monitoring result, and carrying out visual display and early warning. According to the method, the precision and reliability of dam deformation monitoring are effectively improved through combination of network adjustment and robust estimation.

The invention provides a slope displacement monitoring method and system based on reinforcement learning and enhanced Kalman filtering, and the method comprises the steps: carrying out the preprocessing of displacement data collected by Beidou, and carrying out the abnormal value elimination, missing value interpolation and time consistency inspection; establishing a Kalman filtering model containing displacement and speed state vectors, and initializing a process noise covariance matrix Q and an observation noise covariance matrix R as initial filtering parameters; q and R matrixes are dynamically optimized through a PPO reinforcement learning algorithm, and parameter self-adaptive adjustment is achieved; carrying out displacement trend analysis on the filtered output data, marking abnormal trend data by adopting a statistical test and trend inflection point recognition algorithm, and feeding back a root-mean-square error of the abnormal trend data to a PPO algorithm to carry out parameter readjustment; data stage changes are analyzed based on a sliding window technology, independent experience playback buffer areas are set for data in different stages in PPO, and associated updating of filtering parameters is achieved. According to the invention, the precision and reliability of slope displacement monitoring are improved.

The invention discloses a blood filter early warning method based on multi-parameter coupling and a blood filter, and aims to solve the problems of lagging and high false alarm rate of the existing single parameter threshold alarm. The method comprises the following steps: acquiring time sequence data of at least two monitoring parameters such as transmembrane pressure and venous pressure in real time; constructing a risk state vector based on the sequential data; calculating a risk covariance matrix of the vector in a set time window so as to quantify a collaborative relationship of variation trends among parameters; performing matching degree comparison on the matrix and a pre-stored standard fault feature matrix; and generating and outputting early warning information at least indicating one equipment abnormal mode based on the comparison result. By analyzing a multi-parameter dynamic cooperative relationship instead of a single parameter absolute value, early warning and intelligent preliminary diagnosis of filter blood coagulation, pipeline abnormity and other faults are realized, and the timeliness, accuracy and clinical decision support capability of alarm are remarkably improved.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention relates to the technical field of electrical digital data processing, and discloses a lightweight-based multi-modal data enhancement method, which comprises the following steps: acquiring a multi-modal feature tensor; extracting a vector orthogonal projection scalar and determining the novelty; updating the global covariance matrix when the novelty is greater than a redundancy threshold, and maintaining the global covariance matrix in a register state when the novelty is not greater than the redundancy threshold; extracting a main diagonal variance component to determine a differential modulation coefficient; calculating a second-order moment manifold projection operator according to the global covariance matrix, and calibrating the operator by using a differential modulation coefficient; the calibrated operator is used for carrying out orthogonal projection on random noise to generate a structured disturbance vector, the structured disturbance vector is superposed to a multi-modal feature tensor to output enhanced features, a novelty judgment mechanism is used for restraining statistical deviation caused by steady-state redundant data, computing resource occupation is reduced, and it is ensured that semantic alignment between modals is maintained in enhanced feature distribution.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses an on-orbit spacecraft attitude estimation method and system based on ISAR image feature selection, and belongs to the field of aerospace control systems. The method comprises the following steps: acquiring an ISAR image of a spacecraft, and acquiring a complex linear structure set and three-dimensional feature points of an on-orbit spacecraft; then, according to the obtained three-dimensional-two-dimensional projection model, the CRLB of each reference structure in the complex linear structure set is deduced to carry out attitude estimation error analysis; calculating the trace of the CRLB covariance matrix of each reference structure to select an optimal feature structure, correcting the scattering point trace of the optimal feature structure by using polynomial fitting, and switching the reference structures as a new optimal feature structure according to the scattering point loss rate and a preset sequence; and optimizing the spacecraft attitude angle solving function by using a particle swarm and LM hybrid algorithm to obtain attitude angle parameters. The target with high-precision target attitude real-time estimation can be completed aiming at the problems that high-order frequency change in a dynamic environment is difficult to capture and resolution and noise suppression are contradictory due to a fixed window time-frequency analysis method.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention discloses a digital human rendering method based on Gaussian splashing and multi-scale characteristic field distillation, and belongs to the field of three-dimensional human body digital reconstruction. According to the method, feature extraction is carried out through a Vision Transformer encoder, based on an SMPL model, through a cross-modal parameter estimation module and dynamic human body modeling of three-dimensional Gaussian splashing and semantic feature rendering, three-dimensional Gaussian is projected to a two-dimensional image plane to calculate a covariance matrix and color mixing, and after a rendered color image and an initial feature field image are output, a three-dimensional image is obtained. A student feature map is obtained through a convolution acceleration module, a teacher feature map is obtained after feature extraction is carried out through a two-dimensional basic model, the constructed model is trained, and optimization training is completed through comprehensive total loss function calculation; according to the method, the problems of fuzzy semantics, detail missing, low rendering efficiency and inaccurate human body-scene separation in the existing method are effectively solved, so that more efficient, fine and robust monocular or multi-view human body three-dimensional reconstruction is realized.

The invention discloses a heat pump system state anomaly detection method based on a depth auto-encoder, and the method comprises the steps: collecting compressor data, and carrying out the standardization processing to construct a multi-dimensional time sequence; a spatial-temporal feature extraction depth auto-encoder with a thermodynamic coupling attention mechanism is constructed, coupling attention is utilized to calculate physical parameter coupling strength weights to extract spatial features, time features are extracted in combination with a long and short-term memory network, and normal state data are reconstructed and predicted through a decoder after fusion; residual vectors of predicted normal state data and original data are calculated, and a weighted mahalanobis distance is calculated by using a covariance matrix to generate an abnormal score; and constructing a sliding probability distribution model based on historical normal data, calculating a current score occurrence probability, and comparing the current score occurrence probability with a preset threshold to output an anomaly detection result. According to the method, a multi-physical parameter space coupling relationship and a time evolution rule are captured through a thermodynamic coupling attention mechanism, and the anomaly detection accuracy and robustness are improved.

A method comprising receiving a first input bit stream for a first parametrically coded input audio signal, the first input bit stream including data representing a first input core audio signal and a first set including at least one spatial parameter relating to the first parametrically coded input audio signal. A first covariance matrix of the first parametrically coded audio signal is determined based on the spatial parameter(s) of the first set. A modified set including at least one spatial parameter is determined based on the determined first covariance matrix, wherein the modified set is different from the first set. An output core audio signal is determined, which is based on, or constituted by, the first input core audio signal. An output bit stream for a parametrically coded output audio signal is generated, the output bit stream including data representing the output core audio signal and the modified set.

The invention relates to the technical field of network security detection, and discloses a malicious network traffic detection and analysis method based on artificial intelligence. The method comprises the following steps: acquiring network flow data through preset equipment, and constructing a network flow characteristic representation containing a time sequence statistical characteristic and a protocol attribute characteristic; determining a multi-level associated entity of each traffic fragment in combination with the network security knowledge graph, and aggregating the features to generate aggregated network traffic features; performing malicious probability evaluation on the aggregation features, and determining target malicious traffic by means of the maximum response value of the thermodynamic map; determining a similar malicious traffic mode based on the aggregation feature similarity; generating a detection prompt text in combination with the target malicious traffic and the similar mode, and inputting a preset model to output a detection result; and adjusting model noise covariance matrix parameter optimization detection according to the flow dynamic index. The method can comprehensively capture traffic characteristics, mine associated information, improve the accuracy and adaptability of malicious traffic detection, and effectively cope with malicious attacks in a complex network environment.

The invention belongs to the technical field of intelligent early warning, and particularly relates to an intelligent early warning method for a DMF waste liquid purification and recovery control platform, and the method comprises the steps: carrying out the principal component analysis of long-period historical data, and constructing a principal component transformation matrix of a static reference model; for a moment to be diagnosed, calculating a reconstruction value by using the static reference model to obtain a residual vector, carrying out eigenvalue decomposition on a covariance matrix of a residual matrix of a sliding time window, calculating a drift coherence index according to the distribution of drift eigenvalues, modulating a drift principal component vector of the sliding time window by combining the residual vector, and carrying out diagnosis on the moment to be diagnosed; and obtaining a drift compensation vector, superposing the drift compensation vector with a reconstruction value of a real-time data vector at a to-be-diagnosed moment to obtain an adaptive reconstruction value at the to-be-diagnosed moment, calculating a reconstruction error, comparing the reconstruction error with a fault alarm threshold, judging whether a fault exists at the to-be-diagnosed moment, and performing early warning. According to the invention, the early warning accuracy and robustness are improved.

The invention provides an improved robust Kalman filtering integrated navigation method and system based on chi-square detection, and belongs to the technical field of navigation, and the method comprises the following steps: constructing a GNSS / INS loose integrated navigation Kalman filtering basic framework; before combined navigation Kalman filtering measurement updating, calculating an innovation chi-square test amount, recording the number of available satellites of each epoch, and calculating a satellite observation quality index according to the number of the available satellites; sage-Husa robust Kalman filtering based on IAE windowing is improved, a weighting factor is determined through an innovation chi-square test amount and is used for adjusting the weight of current epoch innovation in a window, and the window length is dynamically adjusted according to the change of a satellite observation quality index; a robust factor obtained through calculation of improved Sage-Husa robust Kalman filtering based on IAE windowing acts on an observation noise covariance matrix, so that the observation noise covariance matrix is expanded, and the weight of observation information with gross error in data fusion is reduced.

The invention relates to the technical field of mobile energy storage charging piles, and discloses a fault detection method and system for a mobile energy storage charging pile, and the method comprises the steps: collecting a voltage and current original sequence during an operation period, and executing the Kalman filtering to obtain a smooth electrical parameter; adaptively adjusting a process noise covariance matrix by using numerical dispersion to obtain a stable internal resistance value sequence; extracting a fluctuation trend and matching a working condition mode to filter periodic disturbance to obtain a pure internal resistance offset sequence; extracting a health degradation rate slope, establishing a dynamic health reference range by using kernel density estimation, and judging an abnormal migration event according to the dynamic health reference range; the offset severity is calculated through severity probability weighted fusion, the fault early warning level is obtained, and an event trigger counter reset mechanism is driven to judge the overall health state. The method can solve the problem of low fault detection accuracy in the prior art.

The invention belongs to the technical field of data processing, and particularly relates to a wind disc fault monitoring method and system, and the method comprises the steps: collecting wind disc operation data and environment parameters in real time, constructing an environment response sensitivity tensor, calculating and eliminating environment-induced supply and return water temperature difference deviation, and obtaining a de-environmental operation feature vector. And in combination with the static hydraulic topology and the dynamic behavior difference, generating a dynamic weight between the wind discs, constructing a self-evolution covariance matrix, and realizing dynamic optimization calculation of the Mahalanobis distance. And fault type discrimination is carried out based on the spatial topological relation of the abnormal wind disc, monomer blockage and system-level blockage are accurately distinguished, and intelligent early warning is realized. The method can effectively inhibit the environment disturbance false alarm, and improves the fault positioning accuracy.

The invention belongs to the technical field of wireless communication, and discloses a CNN-Transform direction estimation method based on covariance-unitary matrix input, and the method comprises the following steps: generating received signal sample data of different incident angles; calculating a covariance matrix for a received signal sample, performing singular value decomposition, extracting real parts and imaginary parts of the covariance matrix and a unitary matrix, and constructing a four-channel two-dimensional real number tensor as input; extracting local spatial features and coherence structures through CNN; serializing the feature map and adding a position code; a Transform encoder module is input, and global spatial dependence is modeled; carrying out average pooling and full-connection classification on the output to realize direction angle prediction; and training is carried out by adopting cross entropy loss, an AdamW optimizer and a cosine annealing learning rate scheduler. According to the method, local and global features are fused, and the method has high precision, strong robustness and excellent generalization ability in complex environments of low signal-to-noise ratio, multipath interference and the like.

The invention discloses a three-dimensional radiation field construction method and system, and the method comprises the steps: firstly obtaining an actual three-dimensional coordinate parameter set and an actual radiation dose rate parameter set of a to-be-measured scene, constructing a standard covariance matrix according to the parameters, selecting a to-be-predicted point, generating an actual covariance vector for the to-be-predicted point, and carrying out the calculation of the actual covariance vector. Generating predicted radiation dose rate parameters based on the standard covariance matrix and the actual covariance vector, and collecting the predicted radiation dose rate parameters of the plurality of points to be predicted into a predicted radiation dose rate parameter set, and finally, generating a three-dimensional radiation field according to the actual three-dimensional coordinate parameter set, the actual radiation dose rate parameter set and the predicted radiation dose rate parameter set. According to the method, the correlation between the trajectory points is represented through the covariance, and the conversion relation between the to-be-predicted point and each trajectory point is established through the conversion relation between the actual covariance vector and the standard covariance matrix, so that the radiation dose rate of the predicted point is calculated, the radiation dose rate of more point positions is obtained, and the precision of the three-dimensional radiation field is improved.

The invention belongs to the crossing field of information and ocean science and technology, and discloses a DOA estimation method for a UUV broadside parallel co-prime array. According to the method, firstly, a virtual extension array is generated by utilizing an array auto-covariance and cross-covariance matrix, then, Toeplitz completion is carried out on'holes' in the virtual array through trace norm regularization and nuclear norm regularization constraints, a complete covariance structure is recovered, and finally, an extension matrix is constructed, and rotation invariance of a signal subspace of the extension matrix is utilized, so that the covariance structure of the virtual array is obtained. The pitch angle and the azimuth angle of the target are jointly solved, automatic angle pairing is achieved, and the effectiveness and the reliability of the method are verified through simulation results. According to the method, aiming at the application challenges that the UUV broadside array space is limited and the underwater environment is complex, all virtual array elements of the broadside parallel co-prime array are fully utilized, the array freedom degree and estimation precision are remarkably improved, the target detection capacity of the UUV is effectively enhanced, and the method has high practical engineering application value.

The invention provides a lithium ion battery charge state estimation method and device, equipment and a medium, and the method comprises the steps: obtaining a battery end voltage measurement value and a platform interval query table collected at a current moment when a charge state estimation request for a to-be-estimated battery is received, and determining a prior charge state estimation value; determining a platform interval attribute result corresponding to the priori state-of-charge estimation value; adjusting an observation noise covariance matrix based on the platform interval attribute result, the real-time slope of the open-circuit voltage-state-of-charge relation curve at the priori state-of-charge estimation value and the battery working current change rate; and updating the Kalman gain based on the adjusted observation noise covariance matrix, correcting the priori state-of-charge estimated value by using the updated Kalman gain to obtain the target state-of-charge estimated value at the current moment, and obtaining the target state-of-charge estimated value at the current moment through platform interval identification and a multi-parameter adaptive adjustment mechanism. And the estimation precision and stability of the state of charge of the lithium ion battery are improved.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The embodiment of the invention provides an underwater scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering and related equipment, and belongs to the technical field of computer vision. The method comprises the following steps: performing feature point analysis on each real two-dimensional image to obtain a sparse point cloud set; a three-dimensional Gaussian ellipsoid of each point cloud in the sparse point cloud set is initialized to represent a three-dimensional underwater scene, and the three-dimensional Gaussian ellipsoid is used for representing Gaussian distribution of the position, the covariance matrix, the color and the opacity of the point cloud; selecting a plurality of three-dimensional Gaussian ellipsoids located in a corresponding camera view cone based on the target camera pose, and performing two-dimensional projection rendering according to the plurality of selected three-dimensional Gaussian ellipsoids to obtain a rendered two-dimensional image; and updating the three-dimensional Gaussian ellipsoids in the point cloud space according to the rendered two-dimensional image and the real two-dimensional image with the same target camera pose to obtain a plurality of three-dimensional Gaussian ellipsoids accurately representing the underwater scene. According to the invention, the accuracy and efficiency of underwater scene three-dimensional reconstruction can be improved.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

The invention relates to the technical field of wireless communication, and discloses an information source number detection method and system based on a non-circular signal. The method comprises the following steps: calculating a compensation sample covariance matrix of a received signal, and obtaining a sample canonical correlation coefficient through Takagi decomposition of the compensation sample covariance matrix; constructing a marginal likelihood function of the maternal typical correlation coefficient; taking a sample canonical correlation coefficient as an estimated value of a corresponding matrix, and establishing an estimated statistic by applying a minimum description length criterion; and obtaining the number of the parent typical correlation coefficients when the estimation statistic is minimized, and taking the number as an estimation value of the actual number of the non-circular signals. According to the method, the characteristic that the non-circular signal compensation covariance is not zero is fully utilized, the marginal likelihood function with the minimum redundancy parameters is adopted to construct the statistics, high-precision estimation of the number of the non-circular signals can be achieved, and necessary guarantee is provided for practical application scenes such as direction of arrival estimation and wave velocity formation.

The invention relates to the field of underwater acoustic detection, and discloses a high-resolution target orientation estimation method and device based on adjacent grid re-optimization, and the method comprises the steps: carrying out the expansion of an array manifold into a virtual array manifold, taking the virtual array manifold as a covariance matrix of an over-complete dictionary construction receiving signal, and carrying out the iterative updating of a noise signal component and a signal component; re-optimizing the adjacent grids to update the dictionary, and performing orientation estimation again in the re-divided intervals; and obtaining a direction-of-arrival estimation result through spectrum peak search. By applying the method, the orientation estimation performance of the underwater vehicle can be improved, and the DOA estimation performance with higher robustness and higher estimation precision can be obtained on a small array.