81 results about "Nonlinear filtering" patented technology

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention provides an accurate calculation method and system for the axial pretightening force of an ultrasonic bolt, and the method combines a high-order statistic theory with a nonlinear filtering technology, and solves the problem that the measurement precision of a conventional method is insufficient under the conditions of strong noise and nonlinearity. Particle filtering and Kalman filtering are combined, so that the problem that a traditional Kalman filtering method easily causes sensitivity of an initial value of filtering divergence is solved; a double-Gaussian attenuation model is adopted to fit an echo envelope, the problem that a single Gaussian or single index model cannot adapt to envelope distortion caused by stress change is solved, and a nonlinear state space model is established to adapt to nonlinear echo signal characteristics under dynamic stress; gaussian noise is effectively suppressed by using a third-order cumulant cross-correlation algorithm, the time difference resolution in a low signal-to-noise ratio environment is remarkably improved, and the Gaussian noise and asymmetric interference are effectively suppressed.

The invention discloses a battery internal short circuit detection method and device which are applied to a battery pack comprising a plurality of single batteries, and the method comprises the steps: obtaining the current information of the battery pack and the voltage information of each single battery; constructing observed quantity representing monomer difference, and estimating a first difference parameter representing monomer voltage source characteristic difference and a second difference parameter representing monomer impedance characteristic difference on line by adopting nonlinear filtering to obtain a corresponding time sequence; segmenting the time sequence according to a preset operation period, and performing weighted convergence according to weights related to electric quantity changes to obtain corresponding characteristic parameters; and constructing a first statistical discrimination quantity and a second statistical discrimination quantity which reflect extreme value deviation and fluctuation deviation based on the periodic characteristic parameters, performing joint discrimination with a threshold value, and determining an internal short circuit when abnormal monomers are consistent and meet a continuous periodic criterion. According to the method, through difference observation, filtering estimation, periodic weighting and double-discriminant-quantity combined judgment, the discriminant stability and reliability under working condition fluctuation and noise interference are improved.

The invention belongs to the technical field of aviation flight training, particularly relates to a flight training trajectory generation method fusing an attention mechanism, and solves the problems that in the prior art, diversified, personalized and high-quality flight training trajectories are difficult to generate, and the high-quality training requirement cannot be met. According to the method, flight training multi-source data is obtained and subjected to preprocessing and dynamic weight fusion to generate fusion data, an encoder in a prediction model processes a single track fragment in the fusion data through a random feature attention mechanism, real-time aerodynamic parameters are fused into a key matrix, an attention score is adjusted through an aerodynamic consistency coefficient, and then submerged space representation is obtained; the decoder generates an initial prediction flight training trajectory sequence in combination with the submerged space representation and the noise vector; and finally, suppressing high-frequency noise by using speed self-adaptive cut-off frequency and weighted sinc filtering, and carrying out nonlinear filtering to obtain a final predicted flight training trajectory sequence. According to the method, diversified, personalized and high-quality flight training trajectories are generated, and the high-quality training requirement is met.

The embodiment of the invention provides a signal processing method and device, a model training method and device, equipment and a storage medium. The method comprises the steps of obtaining a mixed sound signal and a reference sound signal; filtering a linear echo component in the mixed sound signal based on the reference sound signal to obtain a first filtered signal; obtaining an output signal through a pre-trained nonlinear filtering model and the first filtering signal, and sending the output signal to the far-end terminal equipment; wherein the output signal filters out a nonlinear echo component of a target energy quantity level relative to the first filtering signal, the target energy quantity level is smaller than a preset energy level, and the preset energy level is determined based on the energy level of the human voice component in the first filtering signal. The nonlinear component in the sample filtering signal is filtered based on the nonlinear filtering model, and meanwhile, the filtering amount of the nonlinear component is controlled, so that excessive suppression of human voice components is avoided, and the voice communication quality in a complex environment is improved.

The invention relates to the technical field of vehicle-mounted equipment, and discloses a high-vibration environment-oriented vehicle-mounted graphic server active anti-seismic system, which comprises the following steps of: firstly, acquiring a real-time vibration data flow of a vehicle-mounted graphic server, constructing an energy spectrum, and analyzing a resonance modal cluster to generate a damping regulation and control path; analyzing a dominant resonance mode, and identifying a critical oscillation node to calculate an inertia compensation coefficient; nonlinear filtering is carried out on the vibration spectrum based on the coefficient to obtain an anti-interference frequency band set, a servo driving instruction set is generated through topological mapping, a dynamic stiffness parameter is inquired, and a parameter distribution vector is marked; then, the vector is loaded to an active memory controller, an energy dissipation state is monitored, a gain threshold value combination is adaptively adjusted, and an anti-seismic robustness index is calculated; and finally, generating vibration characteristic lines, extracting steady-state, transient and chaotic components, and constructing a multi-cascade active anti-seismic mechanism. According to the method, the anti-seismic stability of vehicle-mounted graphic calculation can be guaranteed.

A signal processing method, a model training method, an apparatus, a device, and a storage medium. The signal processing method comprises: acquiring a mixed sound signal and a reference sound signal (S101); on the basis of the reference sound signal, filtering a linear echo component in the mixed sound signal to obtain a first filtered signal (S102); and obtaining an output signal by means of a pre-trained nonlinear filtering model and the first filtered signal, and sending the output signal to a remote terminal device, wherein for the output signal, a nonlinear echo component of a target energy level is filtered out relative to the first filtered signal, the target energy level is less than a preset energy level, and the preset energy level is determined on the basis of the energy level of a human voice component in the first filtered signal (S103).A nonlinear component in a sample filtered signal is filtered out on the basis of a nonlinear filtering model, and the amount of the nonlinear component filtered out is controlled, avoiding over-suppression of the human voice component, and improving the voice communication quality in complex environments.

The invention provides a high-precision deformation monitoring method and system based on tight combination of Beidou and IMU, and a medium, and relates to the field of geological disaster monitoring and engineering structure safety monitoring and early warning. The method comprises the following steps: constructing a three-dimensional displacement increment between Beidou carrier TDCP epochs, collecting triaxial acceleration and angular velocity data by using an IMU, carrying out tight combination modeling on calculation results of the two, constructing a nonlinear filtering equation taking the displacement increment as an observed quantity, realizing complementation of low-frequency high precision and high-frequency anti-interference through weighted fusion, and obtaining a monitored displacement quantity. Through cooperation of displacement change calculation between Beidou epochs and IMU vibration signals, complementation of low-frequency high precision and high-frequency anti-interference is achieved, the real-time performance of calculating collapse and slip displacement is improved, the displacement magnitude estimation error is reduced, the data utilization rate and deformation monitoring precision are improved, seamless switching of dynamic and static monitoring modes is achieved at the same time, and the real-time performance of the system is improved. And the robustness in a complex environment is enhanced.

The invention relates to the technical field of digital twinning and system health state prediction, in particular to a steering system model construction method based on digital twinning. Comprising the steps of 1, constructing a high-fidelity physical mechanism model representing the dynamic characteristics of the steering system; acquiring a working condition data flow of the steering system in real time; constructing a state observer; resolving a residual signal through a state observer; step 2, constructing an augmented state vector; performing recursive estimation on the augmented state vector by adopting a nonlinear filtering algorithm; 3, injecting the online identification parameter sequence into the high-fidelity physical mechanism model in real time to realize adaptive updating of the physical mechanism model; collecting historical data of the online identification parameter sequence; training a degradation prediction model by using historical data; and predicting and outputting a degradation trend prediction time domain of the steering system. According to the method, the fidelity and credibility of the model are remarkably improved, and a solid foundation is laid for subsequent analysis and prediction.

The application discloses a kind of based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, it is related to chromatic aberration detection technical field, including multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end, the multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end pass through on-board bus communication protocol and carry out two-way data interaction between, and each end is electrically connected with central logic processing matrix.This based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, the present application is aimed at atmospheric data and inertial navigation data this kind of multi-source heterogeneous data, establishes the unified fusion framework based on unscented kalman filter, and introduces multiple fading factor matrix, solves the problem of insufficient tracking ability of traditional method under strong nonlinear and large maneuvering flight conditions, significantly improves the precision of attitude estimation.

The application belongs to the technical field of target tracking and fusion, and relates to a nonlinear filtering distributed target tracking method based on variational inference. Measurement noise parameters of a conventional nonlinear filtering distributed algorithm are assumed to be known, and the application extends the nonlinear filtering distributed algorithm based on variational inference to a single-target tracking scene under unknown measurement noise parameters. Under the distributed algorithm, weight likelihood parameters of each sensor to the target state tend to be consistent, so that the algorithm has strong robustness, small calculation amount, high flexibility and timeliness, and in combination with the variational inference method, the algorithm can achieve high-precision target tracking in a distributed multi-sensor scene under unknown measurement noise variance. The algorithm has close tracking precision to a distributed particle filtering algorithm with known measurement noise variance, and enhances practicability.

The invention discloses a measurement regulation and control method and system of an atomization tester, and aims to solve the problem that a Peltier temperature control system is easy to generate overshoot and low-frequency oscillation at a critical point. The method comprises the following steps: S1, setting a target temperature and a precision tolerance interval; s2, rapidly cooling to a dynamic switching temperature; s3, switching to a precise approaching mode, and driving a Peltier by adopting a frequency self-adaptive micro-current pulse sequence based on the residual heat to realize overshoot-free temperature positioning; and S4, when the temperature meets the steady-state conditions of the mean value, the standard deviation and the trend slope, switching to a steady-state holding mode, performing spectral analysis on the temperature sequence to distinguish high-frequency disturbance and low-frequency drift, and only applying intelligent micro-compensation subjected to nonlinear filtering to the low-frequency drift. According to the invention, through multi-stage strategy matching, pulse control based on energy prediction and intelligent disturbance identification, rapid, accurate and stable control of the temperature of the condensation plate is realized, and the reproducibility of a test result is significantly improved.

The invention relates to the technical field of crossing of medical instruments and biomedical engineering, and discloses a respiratory nerve muscle electrical stimulation intensity self-adaptive control method and device based on EIT and electronic equipment. The method comprises the following steps: constructing and standardizing a multi-frequency space-time EIT tensor; carrying out block Hankel tensorization expansion on time-frequency domain information; the tensor is decomposed through CPD, and breathing signals and artifacts are identified through fuzzy logic; performing nonlinear filtering and signal reconstruction; a delta Z image is generated through multi-frequency fusion, features are extracted, and adaptive adjustment of NMES parameters is driven. According to the method, the problems of poor artifact separation and signal distortion of the existing PCA method are solved, the signal-to-noise ratio of the EIT signal is improved, and respiratory nerve muscle function monitoring and electrical stimulation control can be assisted.

The invention relates to the technical field of image processing, in particular to a pancreatic operation key anatomical region segmentation method based on a CT image, and the method comprises the following steps: obtaining a CT arterial phase image and a CT venous phase image; preprocessing the CT arterial phase image and the CT venous phase image in a window width and window level adjustment and nonlinear filtering processing mode; and through combination of a TotalSegmentor segmentation model, an nnUNet segmentation framework and a region growing algorithm, key blood vessels such as a pancreatic duodenal artery disjunction region and a PV-SMV axis are segmented from the preprocessed CT vein phase image and the preprocessed CT vein phase image, and peripheral adipose tissues are obtained. According to the method, key blood vessels such as a pancreatic duodenal artery dissection region and a PV-SMV axis are automatically segmented by combining an nnNet segmentation framework to optimize a TotalSegmentor technology, and a surrounding fat gap is obtained, so that the problem of subjective deviation caused by traditional manual ROI sketching is solved, key anatomical regions such as a GDA region and a PV-SMV region are accurately positioned, and the accuracy of the segmentation of the pancreatic duodenal artery dissection region is improved. And a more stable space frame which better meets the surgical requirements is provided for image omics feature extraction.

The application discloses a method, device and equipment for applying an improved nonlinear filtering algorithm to a UAV, and relates to the technical field of UAV application. The method comprises the following steps: based on each sensor arranged on the upper portion of the UAV, collecting GNSS data and IMU data in real time and synchronously according to a set sampling frequency and inputting the GNSS data and the IMU data into the improved nonlinear filtering algorithm; performing preliminary fusion on the GNSS data and the IMU data through an unscented Kalman filtering algorithm, and combining UT transformation and an adaptive mechanism to obtain a preliminary state estimation value of the UAV; inputting the preliminary state estimation value into a cubature Kalman filtering algorithm, updating and predicting the state of the UAV based on a cubature rule, and obtaining final positioning and attitude estimation results and applying the final positioning and attitude estimation results to the UAV. The application can greatly improve the positioning and attitude estimation precision of the UAV.

The application provides a kind of nonlinear filtering method of pulse eddy current detection signal, belong to nondestructive testing technical field, can at least partially solve the problem that magnetic field decay voltage signal is greatly influenced by noise in pulse eddy current detection, traditional smoothing filter is easy to introduce signal distortion.The method is used for the pulse eddy current detection signal receiving processing of metal structure with cladding layer, comprising: collecting magnetic field decay voltage signal in pulse eddy current detection process;Nonlinear compression transformation is carried out on the time axis corresponding to the magnetic field decay voltage signal;Smooth filtering time window of preset length is set on the time axis after nonlinear compression, and the corresponding conventional time interval is determined;The magnetic field decay voltage signal falling into the conventional time interval is carried out smoothing filter processing, and the filtered pulse eddy current detection signal is obtained.The application makes the filtering process match the time characteristics of the magnetic field decay signal, improves the stability of signal processing, and is beneficial to subsequent wall thickness or state evaluation.

The present application integrates the idea of separating noise and true value into the PLKF framework, and proposes an unmanned aerial vehicle target positioning method based on two-stage unbiased pseudo-linear Kalman filtering (2S-UPLKF). The angle estimation based on EKF in the first stage can effectively decouple noise interference when the unmanned aerial vehicle is tracking at a long distance, providing a robust initial state for the system. The second stage constructs a noise-true value separation mechanism to eliminate the correlation between the observation matrix and the noise in the pseudo-linear equation from the principle level. This method not only inherits the advantage of efficient calculation of pseudo-linear filtering, but also realizes the active suppression of bias in the dynamic tracking process through two-stage collaborative optimization. Theoretical analysis and simulation experiments show that, compared with other nonlinear filtering algorithms, it can still effectively suppress error fluctuations under extreme conditions such as strong nonlinear observation, large angle noise and long distance observation, and has better tracking performance.

The invention relates to the field of online detection and analysis of the running state of an integrated circuit, and discloses an online detection system and method for the running state of the integrated circuit, and the method comprises the steps: obtaining current, voltage and temperature parameters from a plurality of functional substructure layers in a chip, forming a multi-channel original signal flow, and constructing a local load dynamic mapping relation; under the constraint of a local load dynamic mapping relation, performing nonlinear filtering and wavelet domain decomposition on the multichannel original signal flow to form a module-level short-time abnormal feature set; a self-adaptive anomaly judgment mechanism is constructed by combining a module historical operation curve and load response characteristics, the anomaly confidence of each transient event is calculated under a judgment mechanism bundle, and a module-level real-time anomaly distribution result is generated; a local risk propagation relation is formed by combining the logic dependency relation and the power consumption coupling relation between the modules; and generating a corresponding dynamic intervention strategy in combination with the overall load evolution characteristic and the historical fault mode. The method has the advantage of improving the monitoring precision and reliability.

The invention discloses a method, device and equipment for applying an improved nonlinear filtering algorithm to an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle application, and the method comprises the steps: synchronously collecting GNSS data and IMU data in real time according to a set sampling frequency based on each sensor disposed on the unmanned aerial vehicle, and inputting the GNSS data and IMU data to the improved nonlinear filtering algorithm; carrying out preliminary fusion on the GNSS data and the IMU data through an unscented Kalman filtering algorithm, and obtaining a preliminary state estimation value of the unmanned aerial vehicle in combination with UT conversion and an adaptive mechanism; and inputting the initial estimation value into a volume Kalman filtering algorithm, updating and predicting the state of the unmanned aerial vehicle based on volume rule calculation, obtaining a final positioning and attitude estimation result, and applying the final positioning and attitude estimation result to the unmanned aerial vehicle. According to the invention, the precision of unmanned aerial vehicle positioning and attitude estimation can be greatly improved.

The present invention discloses a method for multi-node collaborative navigation of a flexible lander under limited navigation information, belonging to the field of deep space exploration technology. The present invention is implemented as follows: based on the current altitude and the associated observation information between nodes, an indirect observation equation for navigation landmarks is constructed based on homography transformation or epipolar geometry to build a transmission path for observation information between nodes; the Fisher information matrix is ​​used to analyze node observability, and multi-node information interaction conditions are established with node observability and the flexible lander altitude as indicators; the information interaction conditions are used to determine the information transmission direction between nodes that makes the positions of all nodes observable and the minimum transmitted observation information in the corresponding information transmission direction; a single node uses the observation information obtained by the node itself and the observation information transmitted by other nodes to estimate the node state through a nonlinear filtering algorithm, and all nodes synchronously perform state estimation according to the single node state estimation method to achieve multi-node collaborative navigation under limited navigation information.

The present application relates to the technical field of oil and gas exploration, and particularly relates to a method and system for determining nonlinear isochronous stratigraphic slices. The method comprises the following steps: obtaining original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performing adaptive nonlinear filtering processing on the preprocessed seismic data to obtain adaptive nonlinear filtering data; performing nonlinear seismic attribute extraction on the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; obtaining geological data and inversion target data; constructing an initial geological model according to the geological data to obtain the initial geological model; and performing residual error calculation according to the inversion target data and the initial geological model to obtain a residual error calculation result. The present application can better handle nonlinear geological structure complexity, improve the accuracy and reliability of isochronous stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development.

This invention discloses a secure Tobit filtering method based on a three-wheel Ackerman steering model. The method includes the following steps: 1. Establishing a three-wheel Ackerman steering model with spoofing attacks and censoring measurements; 2. Designing a secure Tobit filter for the three-wheel Ackerman steering model; 3. Calculating the upper bound of the prediction error covariance matrix of the three-wheel Ackerman steering model at time h; 4. Calculating the gain matrix K of the three-wheel Ackerman steering model at time h+1. h+1 5. K h+1 Substitute into step two to obtain the filtering judgment at time h+1. If h+1 < Y, then proceed to step six; step six, through K... h+1 The upper bound of the filter error covariance matrix of the three-wheel Ackerman steering model at time h+1 is calculated, and h = h+1 is set. The process is repeated until h+1 = Y. This invention solves the problem that existing secure Tobit filtering methods cannot simultaneously handle censoring measurements and spoofing injection attacks in nonlinear filtering.

The invention discloses a time-varying optimization formation control method for networked robots with a layered architecture, and the method comprises the steps: collecting the pose and kinetic parameter information of the networked robots, carrying out the equation conversion based on kinematics and kinetic models, and constructing a chain control form capable of describing the state change; track correction is realized by setting a cost function and optimizing a signal generator; a nonlinear filtering function and a virtual controller are introduced to suppress position and speed errors; a radial basis function neural network is adopted to carry out approximation on an unknown nonlinear dynamic state, an adaptive control law is constructed in combination with error feedback, and time-varying optimization of the networked robot formation is realized. According to the method, the convergence speed, stability and robustness of the system can be effectively improved in dynamic and uncertain environments, it is guaranteed that the robot group achieves accurate formation control in complex tasks, and the method has high engineering application value.

The invention particularly relates to a sea clutter pulse interference suppression method for cascaded Hammerstein robust filtering, and the method comprises the steps: receiving an echo signal, and carrying out the preprocessing of the echo signal, so as to obtain a direct signal and a sea clutter signal, which are separated from each other; a cascaded Hammerstein filter is used to carry out multi-stage filtering processing on the separation signal, and a non-linear mapping output signal is obtained; wherein the cascaded Hammerstein filter comprises a first nonlinear filtering module, a second linear filtering module and a third nonlinear filtering module which are arranged in a cascaded manner; and carrying out filtering processing on the nonlinear mapping output signal through a robust filter to obtain an output signal after interference suppression. According to the method, nonlinear distortion and non-Gaussian pulse interference can be effectively suppressed in a complex marine environment, and the anti-interference capability, the signal fidelity and the adaptivity of a radar and a communication system are improved.

The invention provides a non-linear filtering method for a pulsed eddy current detection signal, belongs to the technical field of nondestructive testing, and can at least partially solve the problems that a magnetic field attenuation voltage signal is greatly influenced by noise in the pulsed eddy current detection and signal distortion is easily introduced by traditional smooth filtering. The method is used for receiving and processing a pulsed eddy current detection signal of a metal structural member with a coating layer, and comprises the following steps: collecting a magnetic field attenuation voltage signal in a pulsed eddy current detection process; performing nonlinear compression transformation on a time axis corresponding to the magnetic field attenuation voltage signal; setting a smoothing filtering time window with a preset length on the time axis after nonlinear compression, and determining a corresponding conventional time interval; and performing smooth filtering processing on the magnetic field attenuation voltage signal falling into the conventional time interval to obtain a filtered pulsed eddy current detection signal. According to the invention, the filtering process is matched with the time characteristic of the magnetic field attenuation signal, the signal processing stability is improved, and the subsequent wall thickness or state evaluation is facilitated.

The embodiment of the present invention discloses a robust state estimation method for grid voltage sag, which relates to the technical field of power quality monitoring of power systems and can improve the robustness and accuracy of grid voltage sag state estimation. The present invention includes: after establishing a voltage sag disturbance signal model for state estimation, collecting voltage sag monitoring waveform data and inputting it into an improved nonlinear filtering system; using the improved nonlinear filtering system, performing dynamic phasor measurement of the voltage sag parameters of each monitoring point; establishing a structural risk-minimized robust state estimation model for voltage sag, using the instantaneous voltage phasor of each monitoring point as the basic quantity measurement for state estimation, and simultaneously using the instantaneous voltage phasor of non-monitoring points as the state quantity; using the structural risk-minimized robust state estimation model for voltage sag, adopting a quasi-real-time incremental support vector machine algorithm driven by a hybrid model and data, to obtain a robust state estimation result for distribution network voltage sag.

The embodiment of the invention provides a satellite receiver signal processing method and device, and the method comprises the steps: carrying out the demodulation processing of a received signal, and obtaining a baseband signal; performing correlation calculation processing on the baseband signal and a local correlation sequence to obtain a correlation value; constructing a signal space based on the correlation value; performing linear filtering processing on the correlation value of the signal space to obtain a linearly filtered signal space; based on the signal space after linear filtering, the maximum correlation value under the same Doppler frequency and different code phase delays in the first dimension is searched, the sum of the distances between the maximum correlation value and the correlation values under the same Doppler frequency and other code phase delays is calculated, and the maximum distance is selected from the sum of the distances corresponding to the Doppler frequencies; performing nonlinear filtering processing on the correlation value of the Doppler frequency corresponding to the maximum distance to obtain a correlation value after nonlinear filtering; and determining whether a target signal exists based on the correlation value after nonlinear filtering. The performance of capturing the target signal can be improved.

The invention discloses an improved MPC adaptive time delay compensation method based on unscented Kalman filtering, belongs to the technical field of structural anti-seismic tests, and is used for solving the problems of time delay, multi-actuator coupling and noise interference in a multi-axis real-time mixing test. The method comprises the steps that system parameters and UKF initial values are calibrated in an off-line mode; seismic excitation is input into the numerical value substructure, and the expected displacement of the actuator is obtained through coordinate transformation; uKF optimal state estimation is used as an initial condition, and MPC rolling optimization is adopted to solve a control instruction; loading the physical substructure and acquiring actually measured displacement and counter force; inputting measured data into the UKF for nonlinear filtering, and outputting updated state estimation; and finally, state estimation and actually measured counter force are fed back to the numerical substructure, the expected displacement is updated, the steps are repeated, and closed-loop self-adaptive compensation is formed. The precision and robustness of multi-axis loading control can be effectively improved, and the method is suitable for complex structure anti-seismic tests in the field of civil engineering.

The invention discloses an initial alignment method for a movable base of an underwater vehicle based on multi-navigation-system Kalman filtering, and the method comprises the steps: constructing N navigation systems which are uniformly distributed in the azimuth, enabling the included angles between the x axes of the N navigation systems and the x axis of an n system to be gradually increased, and splitting the three-dimensional attitude misalignment angle of an STEKF algorithm model on each navigation system; independently operating the STEKF algorithm on each navigation system to obtain an initial course angle estimated value of the carrier in each navigation system; and according to the initial course angle estimated value in each navigation system, obtaining an optimal navigation system by using a preset screening rule, and obtaining a final alignment result corresponding to the optimal navigation system. According to the method, a plurality of navigation systems covering all-directional angles are constructed, a large azimuth misalignment angle problem is converted into a small misalignment angle estimation problem which can be accurately processed by a linear model, and an optimal result is selected from a parallel filter based on three-dimensional attitude misalignment angle splitting; and the problem that the estimation precision of state transformation Kalman filtering and nonlinear filtering is reduced under the condition of a large misalignment angle can be solved.

The application relates to the technical field of ecological protection and restoration, and discloses a multi-star lily and competitive species interaction regulation and population natural renewal restoration method and system, which comprises the following steps: based on multi-period sample plot monitoring data, a multi-star lily-competitive species dynamic model containing non-integer time lag and nonlinear density index is established; nonlinear filtering is adopted to perform state online estimation on noise-containing monitoring data, and state estimation values and uncertainty measurements thereof are output; according to the state estimation values and the uncertainty measurements thereof, rolling optimization regulation and decision-making are performed through scene analysis and weight construction based on gradient difference; the natural renewal restoration potential is evaluated, and the grade of auxiliary measures is determined; and the restoration scheme is dynamically adjusted in a monitoring-estimation-decision-implementation-remonitoring closed loop. The application can be used for competitive regulation and ecological restoration of a multi-star lily degraded population in a karst mountain meadow, and the stability of the multi-star lily degraded population restoration is improved.