86 results about "Nonlinear filter" patented technology

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention discloses a fish multi-target tracking method based on nonlinear motion modeling and category constraint, and the method comprises the following steps: constructing a basic fish target detection model which is used for generating a fish target detection frame; constructing a target tracking data set in an MOT format for target tracking and a target identification data set for appearance feature extraction, and constructing a basic fish appearance feature re-identification model for extracting appearance features of a fish target; and constructing a target tracking model associated with a nonlinear filter based on detection confidence weighting and category gating. A confidence-weighted extended Kalman filtering algorithm is provided, and the algorithm dynamically adjusts weight distribution of a detection result in a track updating process by analyzing a confidence score of a target detection frame in real time. According to the category gating mechanism, a category similarity penalty term is introduced into a cost matrix of data association, and by establishing an association barrier of category perception, it is ensured that detection frames and estimation frames of different categories are not wrongly associated to the same identity.

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 relates to the technical field of underwater vehicle inertial navigation, and particularly discloses an INS / DVL tight coupling combined navigation method, device and system of an underwater vehicle, and the method comprises the following steps: constructing an INS / DVL tight coupling combined model of an underwater robot; constructing a mahalanobis distance according to the error covariance of the combined model; determining a navigation state filter of the combined model according to a comparison result of the mahalanobis distance and a preset complex environment error judgment threshold value; if the environment is a complex environment, executing a robust state filter based on local consistency measurement; if the environment is a non-complex environment, executing a basic nonlinear filter; and determining navigation parameters of the underwater robot according to the filtering state output result. According to the INS / DVL tight coupling integrated navigation method for the underwater vehicle, the problem that noise parameters are not matched with actual conditions due to environmental changes can be solved on the premise of avoiding parameter excessive compensation in a low-complexity environment and reducing computing power waste.

The invention discloses an underwater polarization imaging device and method based on interference-free coded aperture correlation holography, and belongs to the field of computational optical imaging. According to the technical scheme, on the basis of an interference-free coded aperture correlation holographic system, two light paths are utilized, a target object hologram and a point spread function are synchronously collected through cross-polarization double light paths (0 degree and 90 degrees), after target information light is separated out through an underwater polarization imaging model, low-frequency background noise is filtered out through a nonlinear filtering reconstruction algorithm, and the target object hologram and the point spread function are obtained. And the phase-coded high-frequency object structure information is reserved, and the global estimation precision is optimized. According to the method, a complex optical path of traditional polarization imaging is simplified, the influence of local noise on polarization parameters is avoided, the image definition and the signal-to-noise ratio in a turbid underwater environment can be effectively improved, and the method is suitable for high-scattering scenes such as marine resource exploration, underwater robot navigation and military underwater target detection.

The invention provides an OBD message analysis and attitude rendering method and system based on a vehicle dynamics model, and belongs to the field of vehicle electronics and control. The method comprises the steps that a message is analyzed through an OBD interface, and vehicle operation parameters are obtained; establishing a unified time base and carrying out resampling and synchronous processing on the data of each channel; constructing a fifteen-degree-of-freedom vehicle dynamics model comprising a vehicle body six-degree-of-freedom pose, a vehicle body angular velocity, a four-wheel rotation angular velocity and unsprung mass vertical displacement for state prediction; designing an observation function to map a model state to an OBD observation space, and adopting an observability enhancement strategy; predicting and observing data are fused by using a nonlinear filter, and abnormal observation gating is carried out based on a mahalanobis distance and an amplitude threshold value; compensating system time delay and adaptively adjusting observation noise; and performing three-dimensional real-time rendering on the attitude information. According to the method, high-precision and high-robustness vehicle attitude estimation can be realized only by depending on the OBD message, and the system cost is effectively reduced.

The invention discloses an unmanned ship safety control method and system based on an adaptive neural network observer, and relates to the technical field of unmanned ship control, and the method comprises the steps: building a second-order dynamic model of an unmanned ship through a kinetic equation, and obtaining the actual position and speed state of the unmanned ship; designing an adaptive observer based on a neural network by using a neural network approximate nonlinear dynamic state so as to estimate a first-order system position state and a second-order system speed state under DoS attack and external interference; determining an actual and estimated position tracking error of the unmanned ship according to a preset expected trajectory in combination with the actual and estimated position states, and constructing a virtual controller; designing a nonlinear filter according to the virtual control signal to obtain a trajectory tracking signal of a second-order speed system, and constructing an adaptive controller in combination with the estimated speed state; based on a virtual controller and an adaptive controller, expected trajectory tracking of the unmanned ship under DoS attack and disturbance is realized.

In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and / or a statistical filter.

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.

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 discloses a vehicle-mounted Bluetooth call echo cancellation method and device, a storage medium and an electronic terminal, and the method comprises the steps: obtaining a first call voice signal, and carrying out the preprocessing of the first call voice signal, so as to obtain a to-be-detected signal, and the to-be-detected signal comprises a reference signal and a target signal; judging whether the call state of the to-be-detected signal is double-talk based on a double-talk detection method; if the call state is a double-talk state, performing first echo cancellation processing on the to-be-detected signal through a combination of an adaptive filtering algorithm and a nonlinear filtering algorithm to obtain a preliminary residual signal, then detecting the preliminary residual signal through a voice energy detection method, and judging whether residual echo exists in the preliminary residual signal; and if the residual echo exists in the preliminary residual signal, performing second echo cancellation processing on the residual signal to obtain a second call voice signal. According to the method, the limitation of a traditional single filtering method in a complex acoustic environment is overcome, the phenomenon of'sound absorption 'is effectively avoided, and the call definition is greatly improved.

A device for optically imaging a scene has at least one optical detector and an optical arrangement arranged in front of the optical detector, by means of which the scene can be imaged in a focal plane of the optical arrangement. The detector surface of the optical detector is arranged outside the focal plane and the optical arrangement has an amplitude or phase mask which enables improved algorithmic reconstruction of a sharp image of the scene from a blurred image acquired by the detector. In an alternative, the device additionally has an optically nonlinear filter arranged in the region of the focal plane which, when subjected to incident optical radiation with an intensity above a threshold intensity, changes its optical properties such that it blocks or at least attenuates this radiation.This device creates an optical sensor system that has an improved protective effect against interference radiation, in particular incident laser radiation, while maintaining almost unchanged size.

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.

A noise reduction system for actively compensating background noise in a passenger transport area of a vehicle. The noise reduction system includes a nonlinearity filter unit having a model of a non-linear transfer function of the sound generator, wherein the nonlinearity filter unit is configured to receive the anti-noise signal and to generate a filtered anti-noise signal by applying a non-linear filter function on the anti-noise signal, which is based on the model of the non-linear transfer function in that the non-linear response of the sound generator is at least partially corrected when driven by the filtered anti-noise signal. Wherein the nonlinearity filter unit is further configured to output the filtered anti-noise signal to a sound generator.

The application provides an example in the technical field of signal processing, and provides an audio device, an echo cancellation method and an apparatus. The audio device comprises a loudspeaker, a microphone, and a processor connected with the microphone and the loudspeaker respectively. The processor is configured to: acquire an audio signal frame received by the microphone; perform echo cancellation processing on the audio signal frame; if a nonlinear filter suppression coefficient used in the echo cancellation processing is less than a coefficient threshold value, and a current call state is detected as a double-talk state, then add 1 to a count value; if the count value is greater than or equal to a count threshold value, then reduce the volume of the loudspeaker; if the count value is less than the count threshold value, then perform the acquisition of the audio signal frame received by the microphone; if the nonlinear filter suppression coefficient is greater than or equal to the coefficient threshold value, or the current call state is detected as a non-double-talk state, then clear the count value, and perform the acquisition of the audio signal frame received by the microphone. The application can effectively eliminate echo.

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 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 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 robust relative navigation method for aircraft based on a hybrid distribution under non-Gaussian noise, belonging to the technical field of computation, estimation, or counting. To address the problem of filter divergence caused by non-stationary heavy-tail noise in time-varying environments during relative navigation, this invention introduces a Dirichlet random mixture vector fusion of Gaussian, Student's t, and multivariate K-distributions, proposing a Gaussian-Student's t-multivariate K-distribution modeling of measurement likelihood. Then, by minimizing the KLD of the true posterior probability density function and the approximate posterior probability density function using variational Bayesian techniques, the approximate posterior estimates of the aircraft's relative motion state and filter parameters are obtained, yielding the target's state information relative to the aircraft and solving for relative position and velocity. Finally, a nonlinear filter based on the Gaussian-Student's t-multivariate K-distribution is derived to improve relative navigation accuracy for angle-only relative navigation of aircraft in time-varying environments.

The invention discloses an under-actuated ship finite time formation control method based on a historical data triggering mechanism, and the method comprises the steps: constructing a ship formation virtual control law through introducing a power nonlinear feedback term according to a formation tracking error, and introducing a first-order nonlinear filter to carry out the filtering processing of the virtual control law, performing approximation processing on unknown function items in the error dynamic equation by adopting a radial basis function neural network to obtain an optimized error dynamic equation; and constructing an information guidance dynamic event triggering mechanism based on historical triggering data, combining a formation cooperative control law and a parameter updating law, and realizing under-actuated ship finite time formation control based on the historical data triggering mechanism. The method solves the problems that the dynamic balance between the control performance and the communication efficiency is difficult to realize and the finite time quick response requirement of the time-sensitive task cannot be met due to the fact that the data weight dynamic adjustment characteristic of the historical triggering moment is not fully fused in the existing method.

This invention belongs to the field of robot control technology, specifically relating to a global tracking control method for flexible joint robots based on obstacle functions. The method includes: S1: constructing and reconstructing the dynamic model of the flexible joint robot to adapt it to a reverse-engineering control framework; S2: designing a nonlinear filter that integrates obstacle functions, and using the obstacle functions to limit the filtering error within a preset range; S3: based on the nonlinear filter, recursively designing a controller using a reverse-engineering method, constructing an energy function, and performing stability analysis to ensure that all signals in the closed-loop system are globally consistent and bounded. This invention enables the control method to significantly improve tracking accuracy and system robustness while maintaining low computational complexity. It is suitable for robot systems with compliant actuation characteristics and has good engineering application value.

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.

This invention belongs to the field of measurement and control, and relates to a fixed-time preset performance control method, device, computer equipment, and storage medium for a macro-micro composite motion platform. The method includes: constructing a dynamic system model of the macro-micro composite motion platform; transforming the dynamic system model into a state-space equation suitable for a backstepping recursive control framework; designing a fixed-time preset performance function to constrain the tracking error signal within a preset range within a fixed time; constructing a nonlinear filter to simplify the control design process and establishing a switching function to resolve coupling terms in the system; designing an adaptive fixed-time preset performance tracking control algorithm and proving the stability of the closed-loop system based on Lyapunov stability theory. By designing the controller using an adaptive backstepping recursive control framework, the control design process is simplified, and the computational load is reduced; the tracking error signal of the closed-loop system converges to the preset range within a fixed time, improving the transient performance and control accuracy of the system.

The present invention relates to the field of underwater vehicle inertial navigation technology, and specifically discloses an INS / DVL tightly coupled integrated navigation method, device, and system for underwater vehicles, comprising: constructing an INS / DVL tightly coupled integrated navigation model for the underwater robot; constructing a Mahalanobis distance based on the error covariance of the integrated model; determining a navigation state filter for the integrated model based on a comparison result of the Mahalanobis distance with a preset complex environment error determination threshold; executing a robust state filter based on a local consistency metric if the environment is complex; executing a basic nonlinear filter if the environment is non-complex; and determining the navigation parameters of the underwater robot based on the output of the filtered state. The INS / DVL tightly coupled integrated navigation method for underwater vehicles provided by the present invention can solve the problem of mismatch between noise parameters and actual conditions caused by environmental changes while avoiding parameter overcompensation in low-complexity environments and reducing computing power waste.

The present invention discloses a method and system for optical fiber fault detection and separation based on a two-step neural network. The method comprises acquiring optical fiber source data; performing B-spline approximation on the optical fiber source data to obtain a probability density function of light intensity data; performing static modeling on the probability density function and calculating weights at each moment; using a neural network to approximate the weights at each moment and establishing a nonlinear model between the input light intensity data and the weights; constructing a nonlinear filter for fault detection and a nonlinear observer for fault diagnosis based on the nonlinear model; and inputting the optical fiber source data into the nonlinear filter and the nonlinear observer to obtain the fault size. The present application detects fault intrusion through the nonlinear filter of the fault detection method, and then diagnoses different fault information through the nonlinear observer of the fault diagnosis method to obtain the fault size, thereby achieving the purpose of fault separation.