62 results about "Nonlinear filtering" patented technology

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 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 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 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 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.

The present application belongs to the technical field of image processing, and particularly relates to a kind of image sharpness evaluation methods for long focal length visible light imaging system. The present application first adopts median filter algorithm to pre-process image, and the algorithm is a typical representative of nonlinear filtering algorithm, which can eliminate isolated noise points, and can effectively protect edge contour information while removing random noise. This feature is very consistent with the high demand of edge information for sharpness function. Then, the pre-processed image is decomposed by NSST (non-subsampled shearlet transform), to obtain low-frequency components and high-frequency subbands, and a robust image sharpness evaluation method is constructed by combining direction consistency and contrast normalization.

The invention provides a method, a device and a system for audio signal processing. The present implementation more specifically relates to speech enhancement techniques that combine statistical signal processing with neural network inference. In some aspects, a speech enhancement system may include a linear filter, a deep neural network (DNN), and a non-linear post filter. The linear filter and the non-linear post filter are configured to suppress noise in an audio signal using statistical signal processing techniques. More specifically, the linear filter de-noising an input audio signal based on a temporal correlation between consecutive frames of the audio signal. The DNN inferes a speech signal and a noise signal (respectively representing speech and noise components of the audio signal) based on the de-noised audio signal. The non-linear post filter suppresses residual noise in the speech signal based on one or more Gaussian mixture models (GMMs) associated with the speech signal and the noise signal.

This invention provides a biomimetic lateral line communication system based on propeller wake field perception, belonging to the field of underwater communication technology. Based on the lateral line perception mechanism of fish, it utilizes the flow field disturbance signal generated by the interaction between the propeller and the flow field as the communication signal to achieve network communication for underwater robots. The flow field disturbance of the main propeller is converted into a carrier signal to increase the transmission distance and intensity, while the disturbance of the modulation propeller is converted into a modulation signal for information transmission. A neural network model and a nonlinear filtering model are used to achieve signal modulation and demodulation, exhibiting good environmental adaptability and improving the working efficiency and accuracy of the detection system. The beneficial effects of this invention are: the signal source used for biomimetic navigation perception is the flow field medium fluctuation signal, which can provide a novel approach and practical detection sensing means for underwater covert detection and guidance tracking technology.

The invention relates to a self-adaptive tracking method and system for a maneuvering magnetic target, and belongs to the technical field of target tracking and magnetic detection. The method comprises the following steps: establishing a target physical model and a motion model, and determining a state variable of a tracked magnetic target according to the models; constructing a state space model, and obtaining a motion state equation and a nonlinear observation equation of the magnetic target according to the determined state variable; constructing an IMM-AKKF-FNN model, comprising the following steps: a maneuvering decision-making layer adopts an IMM framework, runs a plurality of filters for different motion modes in parallel, and performs probability weighting according to the likelihood of each model so as to adaptively cope with macroscopic motion mode switching of a target; the nonlinear filtering layer adopts AKKF as a core state estimator in each model of the IMM framework; the noise sensing layer is provided with a pre-trained FNN for each AKKF. According to the invention, the tracking precision and robustness can be improved.

This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques that combine statistical signal processing with neural network inferencing. In some aspects, a speech enhancement system may include a linear filter, a deep neural network (DNN), and a nonlinear post-filter. The linear filter and the nonlinear post-filter are configured to suppress noise in audio signals using statistical signal processing techniques. More specifically, the linear filter denoises an input audio signal based on a temporal correlation between successive frames of the audio signal. The DNN infers a speech signal and a noise signal (representing a speech component and a noise component, respectively, of the audio signal) based on the denoised audio signal. The nonlinear post-filter suppresses residual noise in the speech signal based on one or more Gaussian mixture models (GMM) associated with the speech signal and the noise signal.

The application discloses a coordinate system transformation fusion filtering tracking method and system for a dual-base station radar, and relates to the technical field of radar target tracking. The method comprises the following steps: firstly, establishing state and observation equations, and determining an initial state of a target based on prior information in a Cartesian coordinate system. Then, a target motion state is obtained through one-step prediction, sigma points are generated by using U transformation, and a mean value and a covariance of state space prediction are calculated and constructed. Next, measurement data and prediction data are fused by using a Kalman filter to obtain optimal state estimation and a covariance matrix. Finally, the updated state estimation is converted back to the Cartesian coordinate system based on U transformation, and the process is repeated until the tracking is completed. The application can avoid the nonlinear filtering problem in the updating process, and improves the robustness and precision of tracking.

The embodiment of the invention provides a voice endpoint detection method, a storage medium and a program product. The method comprises the steps that a voice signal to be detected is acquired, framing and windowing processing is carried out on the voice signal, a framing and windowing signal is obtained, and the voice signal comprises noise; first nonlinear filtering processing and second nonlinear filtering processing are carried out on the framing windowing signal, a first enhanced signal and a second enhanced signal are obtained, and a delay parameter corresponding to the first nonlinear filtering processing is smaller than a delay parameter corresponding to the second nonlinear filtering processing; performing cepstrum domain feature extraction processing and fusion processing on the first enhanced signal and the second enhanced signal to obtain fusion features; and according to the fusion feature, the voice signal is detected to obtain a detection result, and the accuracy and stability of voice endpoint detection are improved.

The invention discloses a wind power variable pitch intelligent control method and controller based on DRL-PID fusion nonlinear filtering, and relates to the field of wind power control, and the method comprises the steps: collecting the real-time wind speed of a wind turbine generator and a generator rotating speed signal through a wind speed sensor and a rotating speed sensor, obtaining a blade load estimation value according to a load estimator, and constructing a control input state set; performing joint estimation processing on the signals in the input state set by applying an extended Kalman filter, suppressing high-frequency noise and nonlinear disturbance, and outputting standardized state sensing data; the multi-dimensional operation data is collected and optimized, the noise and nonlinear disturbance influence is effectively reduced, the state sensing accuracy is improved, the control parameters are dynamically adjusted by means of autonomous learning, the control instruction is made to accurately adapt to the real-time working condition, and the flexibility and adaptability of variable pitch control are enhanced.

The application discloses an acoustic echo cancellation method based on nonlinear filtering, and steps are as follows: step 1, signal acquisition and adaptive Hammerstein filter model construction; step 2, solution of a target function is calculated; step 3, setting adaptive Hammerstein filter parameter initial value; step 4, updating parameters; step 5, echo signal cancellation; step 6, iteration until the end of conversation. The application considers different echo environments, uses an adaptive algorithm to estimate the parameters of the system, has a faster convergence speed, can adjust the filter parameters under different echo environments, makes the filter quickly obtain a better filtering effect, and adapts to an environment with higher real-time requirement; the application uses Euclidean projection to update the adaptive filter parameters, can effectively eliminate the echo in a communication system, and has a better echo cancellation effect.