105results about "Digital adaptive filters" patented technology

The invention discloses a sliding bearing friction wear prediction method based on fluid mechanics, and relates to the technical field of mechanical state monitoring, and the method comprises the steps: collecting single-point temperature, local pressure, vibration time domain signals and a bearing pedestal inclination angle through a sensor, and generating sensor data; performing field reconstruction based on a fluid mechanics conservation equation on the sensor data to obtain multi-field data; performing spatial alignment on the multi-field data, inputting the multi-field data into a long short-term memory (LSTM) network, and generating wear state characteristics; training a sparse correlation vector machine regression model RVM by using the historical wear data, establishing a nonlinear mapping relationship between the wear state characteristics and the wear depth, taking the wear state characteristics as the input of the vector machine regression model RVM, and outputting the predicted wear depth; sensor data and the predicted wear depth are fused in real time through Kalman filtering, and when prediction deviation exceeds a covariance threshold value, vector machine regression model RVM parameters are updated.

The invention relates to the technical field of anti-electromagnetic interference, in particular to an anti-electromagnetic interference method for a flywheel energy storage system of a data center, which comprises the following steps: acquiring change rate data of the rotating speed and the angular speed of a flywheel, judging the attribution of a disturbance interval, recording change characteristics, extracting an interference identifier, comparing the interference identifier with impedance change, and generating a switching node. Frequency response and reflection coefficient characteristics of the filtering unit are detected, a coupling channel generation label is identified, and output fluctuation is evaluated to generate an anti-interference capability state label. According to the method, the surface resistivity of the shielding layer is dynamically switched according to the comparison result of the specific interference triggering identification bit and the signal link impedance change state in each disturbance stage through classification judgment and correlation marking of inertial disturbance, so that the effect of suppressing electromagnetic interference in real time is optimized; the influence degree of electromagnetic interference can be dynamically evaluated in the system operation process, and the adaptability of the energy storage system to the electromagnetic interference is judged and adjusted in real time.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention discloses a first-order ECM-based parameter and SOC joint estimation method and system, and the method comprises the steps: constructing a first-order RC equivalent circuit model, and constructing a state-space equation of a lithium ion battery based on the first-order RC equivalent circuit model; corresponding open-circuit voltage OCV values under different SOCs are collected according to an HPPC experiment, and the OCV values are fitted to obtain an OCV-SOC relation curve; constructing a state-space equation based on model parameters, performing parameter identification on the model by using a Kalman filtering algorithm optimized based on a suburb wolf algorithm, and calculating a terminal voltage value through an identification result to verify the identification precision; updating a state-space equation related to the SOC based on a parameter identification result; and on the basis of the state-space equation, rapid convergence from the SOC estimated value to the SOC actual value is realized by using a self-adaptive Kalman filtering algorithm based on an innovation matrix. The method is high in estimation precision.

The invention provides a multi-parameter adjustable wireless transmission signal simulation system and method, and relates to the technical field of wireless communication. The system comprises a main control module, a signal source module, a signal processing module, a combiner module, a digital pre-distortion module and an adaptive adjustment module. Carrier and modulation signals are generated through the control signal source module, the carrier signals and the modulation signals are processed through the signal processing module, direct signals and multipath signals are output, nonlinear distortion of the signals is compensated through the digital pre-distortion module, combined signals are synthesized through the combining module, and characteristics of the combined signals are collected through the self-adaptive adjustment module. Dynamically optimizing multipath parameters by adopting an RLS (Recursive Least Squares) algorithm, and updating a pre-distortion coefficient; and the machine learning prediction module is used for predicting channel characteristic parameters and adjusting the parameters in real time. The method solves the problems of redundancy of discrete devices, low parameter adjustment precision and signal distortion, and is suitable for dynamic channel simulation and complex scene modeling.

The invention relates to the technical field of aero-engines, and discloses an aero-gas turbine engine complete machine stability margin evaluation method, which comprises the following steps of: analyzing physical characteristics and working principles of all parts of a gas turbine engine to construct an engine complete machine model, and constructing an engine experiment table to carry out an experiment; judging whether the compressor enters a surge state and recording experimental data, carrying out numerical simulation based on the constructed engine complete machine model and recording simulation data, comparing the simulation data with the experimental data, verifying the accuracy of the model, if so, constructing a surge boundary, and if not, optimizing the model. The method comprises the following steps: judging whether an engine has a stall hysteresis phenomenon or not in real time, generating a normal operation signal or a stall hysteresis signal, if the stall hysteresis signal is generated, updating a surge boundary, calculating a stability margin value of the engine based on the surge boundary, and evaluating the stability margin of the whole engine.

The invention relates to the technical field of data processing, in particular to a multi-channel controllable power converter for realizing filtering processing and a filtering processing method, and the method comprises the steps: obtaining the signal delay time and voltage signal data of the multi-channel controllable power converter in a current monitoring time period and a historical monitoring time period; according to peak value distribution and frequency distribution of the voltage signal data in each historical monitoring time period, analyzing a noise interference degree, and combining the noise interference degree and the difference of a pulse width parameter change trend to obtain a synchronization characteristic index; according to the signal delay time of the current monitoring time period and the historical monitoring time period, combining the synchronization characteristic index to obtain a filtering weight; and decomposing the voltage signal data in the current monitoring time period according to the filtering weight, and filtering in combination with Gaussian characteristics of decomposed signal components to obtain filtered voltage signal data. The filtering processing result has a good processing effect, and the stability of power supply output is ensured.

The invention relates to the technical field of digital control switching power supplies, and particularly discloses a switching power supply dynamic response adjusting system based on digital control, which comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, a self-adaptive control law adjusting module, a pulse width modulation driving module and a stability monitoring module. On-line identification of key parameters of a system is realized by constructing a nonlinear state space model and combining an extended Kalman filtering algorithm, dynamic compensation is carried out for non-minimum phase characteristics such as a right half plane zero point, and a self-adaptive control law adjustment module optimizes a control strategy in real time according to an updated model. A Lyapunov stability criterion and a predictive control mechanism are introduced, the robustness and response speed of the system are improved, and a stability monitoring module identifies an abnormal state through sliding window variance analysis and peak detection and triggers a fault-tolerant mechanism to guarantee safe operation of the system.

The invention relates to the technical field of electric energy metering monitoring, in particular to an electric energy metering online monitoring method based on adaptive filtering and a deep convolutional neural network. According to the technical scheme, the method comprises the following steps: based on monitoring of correlation between electrical parameters and equipment characteristics, carrying out preliminary correlation analysis by using an intelligent algorithm according to electrical parameters, mutual inductor characteristics and electrical topological structure information collected by an electric energy metering device, and then carrying out global optimization algorithm processing. Noise interference in data is effectively removed through the adaptive filtering technology, the data quality is improved, a reliable data basis is provided for subsequent analysis and model training, the accuracy of a monitoring result is improved, high-precision description of errors of a measuring instrument is achieved, the precision of online monitoring of electric energy measurement is remarkably improved, and the method is suitable for popularization and application. Potential problems of the measuring instrument can be found in time, electric power transaction disputes and power grid operation risks caused by measuring errors are reduced, and stable operation and economic operation of an electric power system are guaranteed.

The invention relates to the technical field of multi-sensor fusion positioning, and discloses an outdoor robot multi-sensor fusion method. Comprising the following steps that robot operation data are obtained through multiple sensors in the running process of a robot; taking the preprocessed operation data as measurement input of an unscented Kalman filter state estimation process, and obtaining first position data after fusion processing; learning the noise characteristics of each sensor by using a long-short-term memory network of a sliding window, and compensating the position deviation of the robot to obtain second position data; performing time synchronization processing on the first position data and the second position data, and inputting the first position data and the second position data into an improved double-layer multi-model adaptive estimation framework; the improved double-layer multi-model adaptive estimation framework adopts a double-layer structure; and obtaining final real-time position data. The method can inhibit noise and drift of a single sensor, reduces error accumulation caused by factors such as environment shielding, improves positioning precision, robustness and continuity, and is suitable for scenes such as outdoor driving robots.

The invention relates to the technical field of physiological signal processing, and discloses an anti-interference filtering method and system for a multi-modal physiological signal acquisition device, a terminal and a storage medium, and the method comprises the steps: carrying out the preprocessing of an acquired multi-modal physiological signal; modal recognition and feature extraction are carried out on the preprocessed multi-modal physiological signals; selecting a corresponding filter according to a modal identification result, and dynamically adjusting parameters of the filter; adjusting a filtering path and a filtering sequence based on a cascade filtering structure and a dynamic switching mechanism; adaptive interference estimation and offset are carried out based on crosstalk processing and a reference channel compensation mechanism; and performing parallel filtering according to the extracted features, comparing evaluation parameters of each filtering result based on an output result selection mechanism, and selecting and outputting a final signal according to a comparison result. According to the invention, effective suppression of multi-source interference such as power supply interference, motion artifacts, crosstalk and high-frequency noise is realized, and the real-time performance and stability of the filtering effect are improved.

A digital signal processing (DSP)-based serializer-deserializer (SERDES) includes a first filter configured to mitigate inter-symbol interference (ISI) attributed to dispersion associated with a long-reach transmission medium. The SERDES includes a second filter configured to shape the ISI. The SERDES includes also includes a third filter coupled in parallel with the second filter and configured to reduce ISI attributed to reflections associated to both near-zero delays and long delays.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention discloses a pollution source positioning method, device and equipment based on multi-source data fusion and a storage medium, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining environment and meteorological data through a sensor network, and obtaining filtered environment data and synchronized meteorological data through adaptive filtering and time synchronization processing. Then, pollution source information estimation is carried out by combining the data and using an improved Kriging interpolation algorithm and adaptive Kalman filtering, and an initial state vector is generated; the position of the pollution source is predicted based on the vector, a candidate coordinate set is formed, the position of the target pollution source is optimally selected through maximum likelihood estimation, the problems of data transmission delay and meteorological disturbance of a traditional method are solved, and the accuracy and reliability of real-time positioning of the VOCs pollution source in the industrial park are improved.

The invention discloses a spherical simplex robust unscented Kalman filter and a PWM (Pulse Width Modulation) rectifier control method and device, which are characterized in that (2n + 1) Sigma points in standard unscented Kalman filtering are replaced by (n + 2) simplex vertexes which are symmetrically distributed on a unit hyper-sphere, so that the calculation complexity is remarkably reduced. A covariance matrix and a state quantity are dynamically adjusted in combination with a corrected observation noise covariance matrix and an adaptive factor adjustment mechanism, so that the estimation precision of the filter on the lumped disturbance quantity is remarkably improved, and the robustness and the tracking performance of the system on non-Gaussian noise and abnormal observation values are enhanced. And estimating the lumped disturbance quantity of the system in real time through spherical simplex robust unscented Kalman filtering, introducing the lumped disturbance quantity into a cost function for optimization solution, and selecting an optimal voltage vector to control the on-off state of the rectifier. According to the invention, the total harmonic distortion rate of the output current and the voltage fluctuation of the direct-current bus are obviously reduced under the steady-state condition, and higher response speed and stronger anti-interference capability can be shown under the dynamic working condition.

The invention discloses a lithium battery internal temperature estimation method based on thermal parameter identification of AWPSO and IKF, and aims to solve the problems that thermal parameter identification is susceptible to noise interference and the temperature estimation precision is insufficient in the prior art, the method comprises the following steps: firstly, collecting battery voltage, current and surface temperature data, and constructing a lumped parameter thermal model; an adaptive weighted particle swarm optimization (AWPSO) algorithm is adopted to dynamically adjust the inertia weight, and thermal resistance and thermal capacity parameters are identified by taking the error minimization of the model calculation temperature and the actually measured temperature as a target; secondly, based on a thermal model of identification parameters, the internal temperature is estimated in real time through an incremental Kalman filter (IKF) algorithm, a residual feedback mechanism is introduced into the algorithm to dynamically update noise covariance, and the state prediction and measurement updating process is optimized. According to the method, the robustness of thermal parameter identification and the accuracy of internal temperature estimation are remarkably improved, and an effective technical support is provided for safety monitoring and service life management of the lithium battery.

The embodiment of the invention provides a high-speed signal receiving method and system of a protocol signal processing module, and relates to the field of high-speed signal receiving, and the method comprises the steps: obtaining an initial signal; spectral distribution characteristics of the high-speed signal are determined based on the initial signal. And determining an interference frequency band range based on the spectrum distribution characteristics, and judging the dynamic change trend of the interference signal based on the interference frequency band range. If the power value of the interference signal in the dynamic change trend in the preset time exceeds the power threshold range, adaptive updating calculation is carried out on the filtering parameters in the interference frequency band range, and a first filtering parameter set is obtained; and applying the first filtering parameter set to an adaptive filtering processing module to perform interference suppression operation on the high-speed signal to obtain a suppressed first high-speed signal. Therefore, according to the method provided by the invention, when the power value of the interference signal in the dynamic change trend in the preset time exceeds the power threshold range, the filtering parameter can be adaptively updated, and the interference suppression effect is improved.

The invention relates to the technical field of hydraulic support group fault self-sensing scheme design, in particular to a hydraulic support group fault self-sensing method and system. Multi-source sensor data are collected in real time, a spatial relation matrix representing the mechanical coupling strength of the support is constructed, and group-level fault collaborative diagnosis is achieved in combination with a graph neural network; an edge end lightweight model is innovatively adopted to carry out dynamic noise filtering and time-frequency domain feature extraction (FFT frequency domain analysis), and the cloud verifies a diagnosis result through digital twinborn verification and increments the model; and positioning an abnormal source based on fault propagation traceability, and generating a maintenance strategy by matching historical cases with a knowledge graph. The technical bottlenecks of a traditional method in the aspects of group coupling effect modeling, real-time response, maintenance decision making and the like are broken through, the fault positioning precision and the system reliability are remarkably improved, and a core technical support is provided for intelligent guarantee of a fully mechanized coal mining face.

The invention discloses a fusion filtering method, system, equipment and medium for power transformer winding state monitoring, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: obtaining an original state monitoring signal of a transformer winding; performing multi-scale decomposition on the original state monitoring signal by adopting self-adaptive wavelet transform; an improved particle filter is embedded, and a suggested distribution function of relative entropy optimization and a dynamic weight updating mechanism are combined; the filtered sub-signals are reconstructed through inverse wavelet transformation; weight parameters are adjusted in a self-adaptive mode; and outputting the key state parameters for transformer winding health assessment. Under the complex electromagnetic environment and the dynamic load fluctuation working condition, the signal-to-noise ratio of the state data of the transformer winding can be remarkably improved, the comprehensive accuracy rate of state evaluation is improved, and reliable data support is provided for health management and preventive maintenance of the transformer winding. And the operation reliability of the transformer and the intelligent level of state evaluation can be obviously improved.

The invention provides an FPGA-based millimeter-wave radar data parallel pipeline filtering method, device and equipment and a storage medium, and the method comprises the steps: receiving echo data of a millimeter-wave radar in real time, and alternately writing the continuous echo data into a first buffer region and a second buffer region through a double-buffer structure; the method comprises the following steps: receiving full frame data through a three-line queue, and dividing a currently processed scanning line in the three-line queue into n blocks; performing parallel filtering on the jth sampling point in any block and the front scanning line and the rear scanning line at the corresponding position, and generating filtered data; and calculating a self-adaptive threshold based on the statistical characteristics of each block, comparing the self-adaptive threshold with the filtered data, suppressing noise signals lower than the self-adaptive threshold according to a comparison result, and selecting a result with the highest energy as final output by comparing signal energy of corresponding positions of the three lines. The problem that resource occupation is too high when echo signals of the millimeter wave radar are processed on an existing FPGA is solved.

The invention relates to the technical field of data filtering, and provides a sliding window robust adaptive cubature Kalman filtering method and device and a medium, and the method comprises the steps: obtaining an observation vector of a sensor for a tracked target at a current moment; estimating a current state and an error covariance matrix by using a volume Kalman filtering algorithm based on the observation vector and the previous moment information; performing reverse smoothing on an estimation result at each moment in the sliding window by using a volume Kalman smoother to obtain a state smooth vector and a corresponding matrix; and finally, determining a current noise covariance matrix by variational Bayesian inference based on the information, wherein the current noise covariance matrix is used for correcting a prediction error covariance matrix at the next moment. According to the embodiment of the invention, the sliding window mechanism and the volume Kalman smoother are introduced to carry out reverse smoothing on the historical state, and the variational Bayesian inference is combined to carry out online joint estimation on the time-varying noise covariance matrix, so that the uncertainty of the model and the filtering divergence caused by noise abrupt change can be effectively inhibited, and the state estimation precision and robustness are improved.

The invention relates to the technical field of data mining analysis, in particular to a petrochemical operation state evaluation method and system based on big data, and the method comprises the following steps: collecting a sensor signal to generate standard time sequence monitoring data, constructing a high-dimensional space-time correlation map based on physical topology, extracting a global state feature vector through a map attention network, and carrying out the analysis of the global state feature vector. And calculating a feature distance and mapping to generate a petrochemical operation state evaluation result. According to the method, a high-dimensional space-time correlation graph based on physical topology and data correlation is constructed, discretely distributed sensor signals are mapped into graph structure nodes with logic connection, and hidden nonlinear coupling characteristics among multi-source parameters are deeply mined by using a graph attention network model; compared with the prior art, the method overcomes the defect that a single-point monitoring mode splits process continuity, achieves the precise quantification of the performance degradation trend of equipment under a complex working condition in combination with the dynamic evaluation logic based on the feature space distance, and effectively improves the global consistency and accuracy of petrochemical operation state evaluation.

The invention relates to the technical field of engine control and state monitoring, and discloses a diesel engine in-cylinder pressure multi-source fusion estimation method based on adaptive Kalman filtering, which comprises the following steps: acquiring working condition parameters and torque signals of a diesel engine; based on the collected working condition parameters, model prediction cylinder pressure is obtained; based on the collected torque signals, single-cylinder contribution is extracted, and torque reverse thrust cylinder pressure is obtained through reverse thrust; according to the real-time information entropy change of the model prediction cylinder pressure and the torque reverse thrust cylinder pressure, dynamically distributing a fusion weight, and generating a fusion observed quantity; forming an extended characteristic quantity based on the characteristic that the fusion observed quantity changes along with the crank angle; establishing a state transition equation and an observation equation; and outputting an in-cylinder state prediction and estimation result after fusion optimization. According to the method, the in-cylinder pressure multi-source fusion estimation method based on adaptive Kalman filtering is adopted, fusion weights of different data sources can be dynamically adjusted in real time, and the technical effect of considering steady-state accuracy and transient response speed is achieved.

The invention discloses a self-adaptive vehicle navigation filtering method and device, and belongs to the technical field of intelligent driving. The method comprises the following steps: constructing a nonlinear uncertainty tracking system model according to the mutability of an actual vehicle tracking state, the nonlinearity of a sensor measurement equation and the non-Gaussian property of a measurement error; aiming at a nonlinear uncertainty tracking system model, combining strong tracking filtering and a maximum entropy criterion, and constructing a cost function for estimating the optimal state of the unmanned vehicle; determining a fading factor in the cost function based on the orthogonality of the measurement residual sequence; constructing a novel adaptive navigation filtering algorithm by combining an unscented Kalman filtering framework according to the cost function and the fading factor; and carrying out data processing on the unmanned vehicle tracking system according to the constructed adaptive navigation filtering algorithm. According to the method, the problems of mutability, nonlinearity, non-Gaussian property and the like in a nonlinear tracking system can be inhibited at the same time, and the tracking precision and reliability of the unmanned vehicle in a complex environment are improved.

The invention belongs to the field of food grain storage quality detection, and particularly relates to a food grain storage duration prediction method and system. The method comprises the following steps: taking biophoton radiation data of to-be-predicted grain seeds as storage duration information of each iteration, and obtaining a prediction error, a state updating equation and a state vector corresponding to Kalman gain and a time updating equation corresponding to the prediction error through a Kalman filtering algorithm of a Kalman model of the storage duration information of the grain seeds; obtaining a state vector of the current iteration; updating the current number of iterations, then obtaining a state vector of the current iteration until a prediction error meets an iteration stop condition, and obtaining a predicted grain grain storage duration according to the state vector of the current iteration and the Kalman filtering state space model containing the state vector and storage duration information; the Kalman model identifies the coefficient of the Kalman filtering state space model by using the known storage duration information of the grain seed sample and the biophoton radiation data as the state vector to determine the Kalman filtering state space model.

The invention provides an online model updating hybrid test method based on a dual adaptive UKF algorithm, and the method comprises the steps: carrying out the initialization of a to-be-recognized constitutive model parameter, determining a parameter initial value seismic oscillation according to a genetic algorithm, inputting the seismic oscillation into a step-by-step integration algorithm, and obtaining the displacement response dk of an overall structure; obtaining the boundary displacement of the test substructure based on the displacement response dk and the constitutive model parameter estimation value of the previous step, carrying out real loading according to the boundary displacement, obtaining the real counterforce and real displacement of the test substructure, and carrying out parameter estimation based on a dual adaptive UKF algorithm; and obtaining a new constitutive model parameter value according to the new constitutive model parameter value and the displacement response dk of the overall structure, obtaining the counter-force Rk of the overall structure on each dynamic degree of freedom, realizing online identification and model updating of the constitutive model parameter, and performing loop iteration to the process of ending the test, thereby improving the precision and stability of parameter identification, and improving the test efficiency. The problem that the loading boundary of the test substructure is inaccurate is effectively solved.

The invention relates to the technical field of agricultural equipment new energy driving, in particular to a rice transplanter electronic accelerator pedal signal filtering method, which comprises the following steps in each work cycle: S1, accelerator pedal signal acquisition and processing; s2, state prediction and variance prediction are carried out; s3, state updating and variance updating are carried out; and S4, outputting a filtering result. According to the method, an accelerator pedal signal is filtered by adopting a self-adaptive maximum correlation entropy Kalman filtering algorithm based on a student t kernel, and the algorithm comprises an innovative kernel bandwidth self-adaptive adjustment method of maximum correlation entropy Kalman filtering based on the student t kernel. Compared with a traditional filtering algorithm, the method has higher robustness on paddy field non-Gaussian noise, the robustness estimation capacity of the system on the opening degree of the accelerator pedal is effectively enhanced, and the control stability and precision of the electric drive system in the complex paddy field environment are improved.

In one embodiment an adaptive filter structure comprises a detector circuit (10) configured to receive an input signal (Sin), to detect a presence of a sweeping interference in the input signal, and upon detection of such sweeping interference to provide a first trigger signal (T1) to a delay circuit (20), wherein the first trigger signal (T1) comprises a first frequency indication (f1); the delay circuit (20) is configured, upon receiving the first trigger signal (T1), to provide a second trigger signal (T2) to a tracker circuit (30) after an adjustable amount of time; the tracker circuit (30) is configured, upon receiving the second trigger signal (T2), to estimate a frequency of the sweeping interference using a second frequency indication (f2), to track the estimated frequency and provide the estimated frequency as a third frequency indication (f3) to a notch filter circuit (40); and the notch filter circuit (40) is configured to substantially eliminate the sweeping interference from the input signal (Sin) using the third frequency indication (f3) and therefrom provide an output signal (Sout).

The present invention relates to the field of noise filtering technology, specifically a communication line noise filtering system and method, the system comprising: a temperature coupling module, a voltage response module, a parameter joint adjustment module, and a link verification module. In the present invention, by collecting the temperature data of the inductor and capacitor nodes and inputting them into a piecewise linear regression model, the temperature deviation gradient of the resonant frequency is calculated, the noise suppression bandwidth threshold is dynamically matched in combination with the reference table, the frequency stability under temperature fluctuations is improved, the Kalman filter processes the voltage sampling to generate a fluctuation prediction value, the interval index is extracted based on the parameter mapping table, the least squares method is used to fit and generate the real-time inductor and capacitor parameters, and the voltage transient fluctuation is adaptively realized. The noise power spectrum density is calculated by fast Fourier transform, and the parameter rollback is triggered by comparing the 3dB attenuation point with the threshold difference, thereby forming a closed-loop control to ensure that the suppression bandwidth is continuously matched with the noise frequency band, reduce the bandwidth offset to within 1.2%, and optimize the response time to shorten it to 200ms.