51results about "Digital adaptive filters" patented technology

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 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 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 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 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 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 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.

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 invention discloses an active eliminating type filtering method for cross interference of a charging module and a related device, which are used for improving the electromagnetic compatibility and the stability of a charging system and reducing the influence of the cross interference. The method comprises the following steps: monitoring a charging module output frequency spectrum, and decoupling the frequency spectrum to obtain interference frequency and interference intensity; constructing a digital twinborn model based on the interference frequency and the interference intensity; extracting working state characteristics of the target module, and analyzing by combining the working state characteristics and the expected coupling path to obtain a coupling strength coefficient; according to the digital twin model, generating an expected coupling interference signal generated by the target module; generating an anti-vector signal according to the expected coupling interference signal; injecting the anti-vector signal into a pulse width modulation carrier of the target module to generate an actual driving signal; monitoring a residual interference spectrum output by the target module, and calculating a deviation value between the residual interference spectrum and a preset threshold value; and according to the deviation value, parasitic parameters and anti-vector signals of the digital twin model are updated.

An interference cancellation circuit, an operation method of the interference cancellation circuit, and a wireless communication apparatus are provided. The interference cancellation circuit may include a kernel generation circuit configured to: generate an interference model based on a transmit signal; an adaptive filter configured to: estimate coefficients of an interference model based on an adaptive algorithm to generate an interference estimation signal, and filter the interference estimation signal from the received signal to generate an interference cancellation signal; and a parameter control (PC) circuit configured to generate a control signal for controlling at least one parameter of the adaptive algorithm based on transmission signal information of the transmission signal, and transmit the control signal to the adaptive filter.

An interference cancellation circuit may include a kernel generation circuit configured to generate an interference model based on a transmission signal, an adaptive filter configured to estimate coefficients for the interference model based on an adaptive algorithm to generate an interference estimation signal, and filter out the interference estimation signal from a received signal to generate an interference cancellation signal, and a parameter control (PC) circuit configured to generate a control signal for controlling at least one parameter of the adaptive algorithm based on transmission signal information of the transmission signal and transmit the control signal to the adaptive filter.

The invention relates to the technical field of computers, and discloses an endocrine metabolism index real-time processing method based on edge calculation. The method comprises the following steps: acquiring time sequence data of blood glucose, insulin, cortisol and thyroid stimulating hormone collected by wearable equipment; constructing a lightweight linear state space model of the diagonal dominant sparse state transition matrix; initializing a noise covariance matrix; iterative Kalman filtering based on a sparse matrix inversion algorithm is executed; the observation noise covariance is dynamically adjusted according to the residual error; and when the filtering result exceeds a clinical critical value, triggering local early warning and uploading to the cloud. According to the scheme, the single filtering delay is controlled within 80 milliseconds while the numerical stability is guaranteed, and the early warning timeliness and the system energy efficiency are improved.

In some embodiments, systems, methods, and apparatuses incorporating an hTFR-based power characterization system are provided. The system in various embodiments comprises a constant bandwidth (CB) module to generate a first group of time-frequency representation (TFR) values for a power signal acquired for a device using a first set of frequency sub-bands over a first frequency range, a constant Q (CQ) module to generate a second group of TFR values for the power signal using a second set of frequency sub-bands over a second frequency range, a TFR array generator coupled to both the CB and CQ modules to combine the first and second TFR values into an array of resultant TFR values, and a power characterization module to identify an anomaly with the device based on the array of resultant TFR values.

The invention discloses a broadband acquisition system harmonic spurious elimination method based on adaptive identification, which comprises the following steps: S1, in a pre-training process, an input signal xi (t) enters a broadband acquisition system, an output signal itself is accompanied by the generation of harmonic spurious and noise, and at the moment, the output acquisition signal is expressed as xi [n]; s2, fitting main signals of multiple frequency points and amplitudes and phases of second harmonic signals of the main signals, calculating phase deviation of the square main signals of the frequency points and the second harmonic signals, performing phase deviation adjustment by using a phase deviation half-phase period compensation technology, and designing an amplitude-frequency adjustment FIR filter, a phase-frequency adjustment frequency domain filtering module and a loss point number of delay compensation; s3, in the stray elimination process, the input signal x (t) enters a broadband acquisition system, and the broadband acquisition system outputs an acquisition signal x [n]; according to the invention, the realization difficulty of harmonic spurious elimination is reduced, and the elimination precision is improved.

The invention discloses a multi-target tracking method and system based on tandem joint probability data association and variational Bayesian filtering, electronic equipment and a medium, and the method comprises the following steps: 1, predicting each target, and calculating the interconnection probability between each measurement and the target; step 2, generating equivalent fusion measurement and equivalent covariance for each target; step 3, performing dynamic correction on the fusion measurement noise covariance of each target; 4, updating and iterating each target by using Kalman filtering, and returning to the step 3 if the number of iterations is less than the set number of iterations; otherwise, storing the optimal estimation value and the posterior error covariance of each target. According to the method, the calculation complexity and the resource consumption are remarkably reduced, and meanwhile, the multi-target state estimation precision and the system robustness are synchronously improved.

The invention discloses a self-adaptive Kalman filtering method, device and system for integrated navigation data, and belongs to the technical field of data filtering. According to the self-adaptive Kalman filtering method for the integrated navigation data, information symbols corresponding to all elements in all dimensions in observation vectors at different moments are detected, and the information symbols corresponding to all elements in all dimensions in the observation vectors at different moments are obtained; judging whether a deterministic state estimation error exists or not, and if judgment cannot be carried out through an innovation symbol, detecting the deviation amplitude of the element innovation to obtain the deterministic error; the robustness and the adaptive ability of the algorithm can be improved through multiple detection; further, the Kalman gain is adjusted based on deterministic error detection, and the purposes of increasing the convergence speed and quickly entering an unbiased estimation state are achieved.

The application discloses a kind of self-adapting vehicle navigation filtering method and device, belong to intelligent driving technical field.It includes: according to the abruptness of actual vehicle tracking state, the nonlinearity of sensor measurement equation and the non-Gaussian of measurement error, construct nonlinear uncertainty tracking system model;For nonlinear uncertainty tracking system model, combined with strong tracking filter and maximum entropy criterion, construct the cost function of unmanned vehicle optimal state estimation;Based on the orthogonality of measurement residual sequence, determine the fading factor in cost function;According to cost function and fading factor, combined with unscented kalman filter framework, construct new self-adapting navigation filtering algorithm;According to the data processing of unmanned vehicle tracking system of the self-adapting navigation filtering algorithm constructed.This application can simultaneously suppress the abruptness, nonlinearity and non-Gaussian in nonlinear tracking system and other problems, improve the tracking precision and reliability of unmanned vehicle in complex environment.

An interference cancellation circuit may include a kernel generation circuit configured to generate an interference model based on a transmission signal, an adaptive filter configured to estimate coefficients for the interference model based on an adaptive algorithm to generate an interference estimation signal, and filter out the interference estimation signal from a received signal to generate an interference cancellation signal, and a parameter control (PC) circuit configured to generate a control signal for controlling at least one parameter of the adaptive algorithm based on transmission signal information of the transmission signal and transmit the control signal to the adaptive filter.

In some embodiments, systems, methods, and apparatuses incorporating an hTFR-based power characterization system are provided. The system in various embodiments comprises a constant bandwidth (CB) module to generate a first group of time-frequency representation (TFR) values for a power signal acquired for a device using a first set of frequency sub-bands over a first frequency range, a constant Q (CQ) module to generate a second group of TFR values for the power signal using a second set of frequency sub-bands over a second frequency range, a TFR array generator coupled to both the CB and CQ modules to combine the first and second TFR values into an array of resultant TFR values, and a power characterization module to identify an anomaly with the device based on the array of resultant TFR values.

The present application relates to physiological signal processing technical field, disclose a kind of multi-modal physiological signal acquisition equipment's anti-interference filter method, system, terminal and storage medium, comprising: the multi-modal physiological signal of acquisition is preprocessed;Modal identification and feature extraction are carried out to the multi-modal physiological signal after pre-processing;According to modal identification result, corresponding filter is selected, and filter parameter is dynamically adjusted;Filtering path and order are adjusted based on cascaded filter structure and dynamic switching mechanism;Based on crosstalk processing and reference channel compensation mechanism, adaptive interference estimation and offset are carried out;According to the extracted feature, parallel filtering is carried out, and the evaluation parameter of each filtering result is compared based on output result selection mechanism, and according to the comparison result, the final signal is selected and output.The present application realizes the effective suppression of power interference, motion artifact, crosstalk and multi-source interference such as high-frequency noise, improves the real-time and stability of filtering effect.

This application discloses an active filtering method and related apparatus for eliminating cross-interference in charging modules, which improves the electromagnetic compatibility and stability of charging systems and reduces the impact of cross-interference. The application includes: monitoring the output spectrum of the charging module and decoupling the spectrum to obtain the interference frequency and intensity; constructing a digital twin model based on the interference frequency and intensity; extracting the operating state characteristics of the target module and combining these characteristics with the expected coupling path analysis to obtain the coupling strength coefficient; generating the expected coupling interference signal generated by the target module according to the digital twin model; generating an anti-vector signal based on the expected coupling interference signal; injecting the anti-vector signal into the pulse width modulation carrier of the target module to generate the actual driving signal; monitoring the residual interference spectrum output by the target module and calculating the deviation between the residual interference spectrum and a preset threshold; and updating the parasitic parameters of the digital twin model and the anti-vector signal based on the deviation.

Systems and methods for low power lattice wave filters include an input operable to receive a digital input signal having a first sample rate, a first processing branch including a first delay element operable to receive the digital input signal and output a delayed digital input signal, a second processing branch including a first summer operable to receive the digital input signal and subtract a delayed feedback signal to produce a difference signal, a second summer operable to combine the delayed digital input signal and the difference signal to produce an output signal, and wherein the second processing branch further includes a feedback path including a second delay element operable to receive the output signal and output the delayed feedback signal. In a multi-stage topology, registers are provided between each stage and clocked to reduce ripple power.

The application provides an online model updating hybrid test method based on a double adaptive UKF algorithm, which comprises the following steps: initializing to-be-identified constitutive model parameters, inputting parameter initial value seismic vibration determined according to a genetic algorithm into a step-by-step integral algorithm, and obtaining displacement response d k of the overall structure k , obtaining boundary displacement of a test substructure based on displacement response d k and constitutive model parameter estimation value of the previous step, performing real loading according to the boundary displacement and obtaining real reaction force and real displacement of the test substructure, performing parameter estimation based on a double adaptive UKF algorithm, obtaining new constitutive model parameter value, obtaining reaction force R k on each dynamic degree of freedom of the overall structure based on the new constitutive model parameter value and the displacement response d k of the overall structure, and realizing online identification and model updating of the constitutive model parameters, which are iterated and circulate until the test is completed, thereby improving the precision and stability of parameter identification and effectively solving the problem of inaccurate loading boundary of the test substructure.

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 present application relates to the unmanned technical field of open-pit mine area, and particularly relates to a method for realizing trajectory prediction of open-pit mine vehicle by combining random forest and Kalman, which comprises the following steps: obtaining current motion state information of mine vehicle by perception device; obtaining candidate reference path based on current motion state information and prior open-pit mine area high-precision map information, and obtaining path curvature of candidate reference path; processing current motion state information and path curvature based on random forest, and obtaining observation value of longitudinal velocity and yaw angle velocity of reference path; establishing state transition model and measurement model of extended Kalman filter, and taking observation value of longitudinal velocity and yaw angle velocity as measurement value of extended Kalman filter; performing state prediction and measurement update of extended Kalman filter, and finally obtaining prediction trajectory of mine vehicle at future time; the present application can improve trajectory prediction performance of open-pit mine vehicle.

The present application relates to the technical field of data processing, in particular to a kind of multi-way controllable power converter for realizing filter processing and filter processing method, comprising: obtaining the signal delay time and voltage signal data of multi-way controllable power converter in current monitoring time period and historical monitoring time period;According to the peak value distribution and frequency distribution of voltage signal data in each historical monitoring time period, analyze the noise interference degree, obtain synchronization characteristic index in combination with the difference of noise interference degree and pulse width parameter variation trend;According to the signal delay time of current monitoring time period and historical monitoring time period, obtain filter weight in combination with synchronization characteristic index;According to filter weight, the voltage signal data in current monitoring time period is decomposed, and filter is carried out in combination with the gauss characteristic of decomposed signal component, obtains the voltage signal data after filter processing.The filter processing result of the present application has good processing effect, guarantees the stability of power output.

The invention relates to the technical field of distributed optical fiber sensing, and provides a gain Kalman filtering denoising method and system for a Brillouin optical time domain reflection signal, and the method comprises the steps: initializing Kalman filter parameters which comprise a state transition matrix, a control matrix, an observation matrix, a process noise variance and a priori estimation covariance; performing prior state estimation on the Brillouin optical time domain reflection signal according to the state transition matrix, the control matrix and the control input quantity, and calculating a prior estimation covariance according to the state transition matrix and the process noise variance; dynamically adjusting the observation noise variance based on the dynamic relationship between the observation noise variance and the Kalman gain in combination with the original noise power and the Brillouin scattering light signal power; updating the Kalman gain by using the priori estimation covariance, the observation matrix and the dynamically adjusted observation noise variance, and updating the system state estimation and error covariance by using the Kalman gain and the observation variable; and outputting the filtered and denoised Brillouin optical time domain reflection signal.