43 results about "Filter gain" patented technology

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The application discloses a multi-AUV anti-communication noise mean square bounded consensus control method and system, relates to the cooperative control technical field of multi-underwater robot systems, and is based on a discrete linear dynamics model and a state measurement model of the multi-underwater robot system, designs a Kalman filter measurement base state estimator, solves filter gain and error covariance, and acquires optimal state estimation values of each robot; based on the discrete linear dynamics model and communication topology characteristics, a state estimation gain matrix and a coupling strength parameter satisfying topology constraints are solved; and based on the optimal state estimation values, the state estimation gain matrix and the coupling strength parameter, a distributed dynamic output feedback controller is designed, sufficient conditions for the multi-underwater robot system to realize mean square bounded consensus are obtained, the influence of communication packet loss and measurement noise on system consensus control is effectively inhibited, and mean square bounded state convergence of the multi-underwater robot system under a fixed undirected topology is realized.

The present application relates to unmanned vehicle safety control method and system resisting hidden false data injection attack. It includes: based on unmanned vehicle information physical system, constructing normal system not attacked and system attacked by hidden executor, constructing difference system with state difference value of two systems; obtaining steady state Kalman filter gain of normal system; constructing linear matrix inequality condition guaranteeing normal system stable and difference system state bounded based on non-escape set, and obtaining controller gain satisfying the condition; according to robust model prediction control, constructing augmented vector and prediction equation, and giving minimum maximum problem of difference system bounded and stable and converting into linear matrix inequality to obtain input signal of difference system, and finally obtaining input signal of attacked system and applying to attacked system, so that difference system is bounded and stable, and stable and safe when attacked. The present application can make the state of attacked system stable and the system safe.

The invention provides an anti-eavesdropping distributed tracking detection method for a target tracking system under low communication resources, and belongs to the technical field of distributed tracking detection. The method comprises the following steps: establishing a random dynamic model of a target tracking system under low communication resources; then designing a time-varying distributed filter under the meaning of minimum mean square error according to the dynamic model; designing a filter gain matrix of each sensor node in the system at the moment; substituting the filter gain matrix into a filter to obtain state estimation of each sensor node in the system at the moment; calculating the upper bound of the estimation error covariance of each sensor node in the system at the moment; and repeating the steps until the requirements are met. According to the method, the problem that the anti-eavesdropping distributed tracking detection of the target tracking system with a periodic scheduling mechanism and an encryption and decryption mechanism is difficult to process at the same time by the existing distributed tracking detection method is solved, so that the tracking performance of the tracking system is improved.

The application provides an abnormality detection and acousto-optic interaction method and system for multi-modal perception of process layer equipment, relates to the technical field of industrial automation, and comprises the following steps: collecting energy consumption fluctuation data and wireless communication quality data of a process layer equipment cluster, combining with equipment identification to form an original operation data set and labeling the original operation data set with equipment-associated identification to form an associated data set, adjusting and verifying initial filter gain parameters of an adaptive filter circuit to determine target filter gain parameters, filtering signals of the associated data set according to the target filter gain parameters, finally generating an equipment operation link, identifying abnormal equipment with parameters deviating from a normal range in the link, generating an equipment abnormality judgment result, determining an alarm level, and generating corresponding alarm audio signals, optical signals and ultrasonic carrier waves; and modulating characteristic parameters to the ultrasonic carrier waves to complete directional acousto-optic prompting of an abnormal area in combination with the optical signals, so that the application realizes intelligent and accurate identification, detection and acousto-optic prompting of implicit abnormalities under complex working conditions.

The application discloses a kind of laser radar synthetic wind speed and wind direction processing method based on Kalman filtering, through the data detected by laser radar is calculated by Kalman filtering algorithm, specifically for the weight of sample point and sample point set one-step prediction, system state weighted mean and covariance matrix, measurement point set one-step prediction, measurement system weighted mean and covariance matrix, Kalman filtering gain and update system state value, update covariance value calculation, with prior state input and prior covariance matrix selects sample, obtains new system state weighted mean and covariance as the parameter of Kalman filtering by the nonlinear transformation of inversion algorithm, under the premise that the mean of wind speed and wind direction inversion error at hub is zero and unchanged, reduce the standard deviation of inversion error.

The application discloses an underground space unmanned aerial vehicle positioning system, method, equipment and medium, and belongs to the technical field of unmanned aerial vehicle positioning. The system comprises a sensor data input layer, which is used for acquiring multi-source sensor data of an unmanned aerial vehicle in real time; a network feature extraction layer, which is structured as a ResNet backbone network and a bidirectional long short-term memory network; a state estimation algorithm layer, which is based on a sliding window self-adaptive extended Kalman filter, utilizes the output of the network feature extraction layer to dynamically adjust filter gain and noise covariance, and combines a sliding window to perform batch state processing and backward smoothing processing; and an external observation interface, which is used for acquiring external positioning observation data, and performs adaptive weight distribution and non-line-of-sight interference suppression based on a motion mode output by the network feature extraction layer. The application solves the technical problem of inaccurate positioning of an unmanned aerial vehicle in an underground space.

The invention discloses an online denoising method for output voltage of a microwave radiometer receiver, and belongs to the technical field of data optimization. The method comprises the following steps: firstly, acquiring a receiver voltage time sequence as observation data, and generating prediction output and error measurement thereof based on a state transition relation; then, the interference intensity and trend are described through the observation residual error and the change rate of the observation residual error, the interference intensity and trend are input into a fuzzy rule base, and increment used for adjusting the zoom coefficient of an observation noise covariance matrix is output; and realizing self-adaptive updating under the condition of ensuring matrix symmetric positive definite and adjusting amplitude and rate constraint, and determining a filtering gain by combining prediction error measurement so as to correct prediction output to obtain a de-noised voltage estimation value and posterior error measurement thereof. According to the method, the observation noise covariance is dynamically adjusted, the observation weight is reasonably distributed in the interference enhancement, sudden change and recovery stages, direct description of noise time-varying characteristics and rapid, stable and interpretable self-adaptive adjustment are achieved, and therefore the voltage output denoising effect is improved.

The invention relates to the technical field of intelligent traffic and automobile active safety, and discloses an automatic control method and system for an intelligent automobile lamp, and the method comprises the steps: collecting a road environment reflection signal through a radar sensor, and obtaining original echo data through filtering, gain adjustment and analog-to-digital conversion; doppler filtering is applied to separate velocity components, static clutters are suppressed, and a filtering frequency spectrum is obtained; identifying a rigid target cluster and rejecting a signal thereof, and performing amplitude correction to obtain a residual spectrum; performing time-frequency transformation on the residual spectrum to extract modulation features, judging a micro-Doppler effect to determine potential pedestrian signals and extracting limb motion features; and after matching with a standard gait database, predicting a pedestrian track through extended Kalman filtering, calculating an intersection point with a vehicle driving path, generating a light distribution control instruction according to a time difference or maintaining a conventional light distribution mode, and realizing automatic control of the vehicle lamp. According to the method, pedestrians in a complex environment can be accurately identified, adaptive control of the vehicle lamp is realized, and the night driving safety is improved.

The present application relates to the technical field of pointing recognition, and more particularly, to a target specimen handle pointing recognition method and system for a medical teaching robot, comprising: collecting a sequence of attitude quaternions of the handle and performing coordinate system alignment, and obtaining a specimen space position calibration table; extracting the amplitude and continuity of the jitter of each component of the quaternions in a reference collection window in a holding stage, respectively, constructing holding noise characteristic quantities of each component, and taking a holding noise reference matrix formed by each holding noise characteristic quantity as an initial value of an adaptive Kalman filter observation noise covariance matrix. The present application realizes precise matching of the filter gain and the user jitter level by constructing an individualized noise prior based on individual holding characteristics and a stability dynamic adjustment mechanism, and effectively solves the contradiction between pointing stability and response real-time when different groups of people interact.

The application discloses a kind of optimization distributed filtering method of target tracking system under work cycle scheduling strategy, the method is as follows: one, the dynamic model of target tracking system with state constraint and probability quantization is established;Two, distributed filter design is carried out to dynamic model under work cycle scheduling;Three, the upper bound of one-step prediction error covariance matrix is calculated;Four, filter gain matrix is calculated;Five, it is obtained in distributed filter by substituting, whether the total length of sensor network is reached, if, then execute six, if, then end operation;Six, the upper bound of filtering error covariance matrix is calculated;Let, execute two, until meet. The application solves the problem that existing distributed filtering method cannot simultaneously process sensor network with state constraint, probability quantization and work cycle scheduling, leading to the problem of reduced filtering performance.

This invention relates to the field of radar signal processing technology, and particularly to an MTD filter design method based on particle swarm optimization, comprising the following steps: S1: Obtaining the Doppler center frequency range and normalized half-width based on the radar type and clutter type to obtain constraints, and obtaining filter information based on the constraints and the global pilot vector matrix; S2: Designing a fitness function based on a nonlinear function and a hard-coded threshold; S3: Designing the filter based on the fitness function, and iteratively updating the filter using a main lobe width bisection method to obtain the optimal filter. This invention achieves optimized design by constructing linear and nonlinear multi-parameter fitness functions, while satisfying the requirements for stopband attenuation, null depth, and clutter bandwidth. Furthermore, it uses a bisection method for iterative optimization of the main lobe width, achieving high robustness under different clutter environments, improving the design efficiency of the MTD filter, reducing main lobe broadening, and lowering filter gain loss.

An apparatus comprises means configured to: obtain respective two or more audio signals from two or more microphones; determining a first sound source direction parameter and a first sound source energy parameter in one or more frequency bands of the two or more audio signals based on the processing of the two or more audio signals; determining a second sound source direction parameter and a second sound source energy parameter in one or more frequency bands of the two or more audio signals based on the processing of the two or more audio signals; obtaining a region defining a direction and / or range for the filter; and generating a filter to be applied to the two or more audio signals, where a filter gain / attenuation parameter is generated based on a relationship of the region to the first sound source direction parameter, the first sound source energy parameter, the second sound source direction parameter, and the second sound source energy parameter.

An apparatus including circuitry configured to: obtain two or more audio signals from respective two or more microphones; determine, in one or more frequency band of the two or more audio signals, a first sound source direction parameter and first sound source energy parameter based on processing of the two or more audio signals; determine, in the one or more frequency band of the two or more audio signals, a second sound source direction parameter and second sound source energy parameter based on processing of the two or more audio signals; obtain a region defining a direction and / or range for a filter; and generate the filter to be applied to the two or more audio signals, wherein filter gain / attenuation parameters are generated based on the region in relation to the first sound source direction parameter, the first sound source energy parameter, the second sound source direction parameter and the second sound source energy parameter.

This invention provides a distributed fault detection method and system for a sensor network switching system, belonging to the field of industrial automation control and fault diagnosis technology. The method includes: acquiring the physical structure parameters, historical test data, and sensor network communication topology of the system under various operating modes; establishing a discrete-time state-space equation set; constructing and solving linear matrix inequalities with performance indicators as the optimization objective and bounded reachability sets as constraints to obtain the optimal filter gain matrix corresponding to each operating mode; calculating the theoretical maximum boundary ellipsoid for each operating mode to obtain the safety threshold for each mode; and performing fault detection based on the filter gain matrix and the safety threshold. This invention establishes a strict instantaneous safety boundary for residual signals under bounded disturbances, achieving theoretically zero false alarms; it replaces the sliding window RMS with an instantaneous residual comparison mechanism, eliminating trailing false alarms after fault clearing, and significantly improving the real-time performance and reliability of fault detection.

The invention provides a multi-mode sensing anomaly detection and acousto-optic interaction method and system for process level equipment, and relates to the technical field of industrial automation, energy consumption fluctuation data and wireless communication quality data of a process level equipment cluster are collected, an original operation data set is formed by combining equipment identification, and equipment association identification is marked for the original operation data set; the method comprises the following steps: forming an associated data set, adjusting and verifying an initial filtering gain parameter of a self-adaptive filtering circuit, then determining a target filtering gain parameter, carrying out signal filtering on the associated data set, finally generating an equipment operation link, identifying abnormal equipment with parameters deviating from a normal range in the link, generating an equipment abnormity judgment result, and determining an alarm level. Corresponding alarm audio signals, optical signals and ultrasonic carriers are generated; the characteristic parameters are modulated to ultrasonic carrier waves, and directional acousto-optic prompting of an abnormal area is completed in combination with optical signals, so that accurate identification of implicit anomalies under complex working conditions is realized, and intelligence and precision of anomaly detection and acousto-optic prompting are realized.

According to the non-deviation and non-model filtering device and method for the servo driving system, all filter gains are determined only by three scalar parameters (k1, k2 and k3), complex matrix operation or online gain adjustment is not needed, and rapid and efficient field debugging is achieved. The method can be efficiently realized in an embedded DSP environment, the hardware cost is remarkably reduced, and the control performance of the system is averagely improved by about 27% through experimental verification.

The application provides a target tracking system anti-eavesdropping distributed tracking detection method under low communication resources, and belongs to the technical field of distributed tracking detection. The application establishes a random dynamic model of a target tracking system under low communication resources; then, a time-varying distributed filter under the least mean square error is designed according to the dynamic model; then, a filter gain matrix of each sensor node in the system at a time point is designed; the filter gain matrix is substituted into the filter to obtain the state estimation of each sensor node in the system at the time point; and the estimation error covariance upper bound of each sensor node in the system at the time point is calculated; let, and repeat until the condition is met. The application solves the problem that the existing distributed tracking detection method is difficult to simultaneously process the target tracking system anti-eavesdropping distributed tracking detection problem with a periodic scheduling mechanism and an encryption and decryption mechanism, thereby improving the tracking performance of the tracking system.

The invention discloses an adaptive sound field adjustment system and method for smart home sound equipment with environmental acoustics perception, and relates to the technical field of sound field adjustment of smart home sound equipment, and the system comprises an environmental acoustics collection module which comprises omnidirectional and directional microphones, carries out the filtering, gain adjustment and 24-32-bit ADC processing, and packages data according to a standard format; the sound field analysis module is used for constructing a sound field model in combination with the room size and the furniture sound absorption coefficient; the adaptive adjustment decision module identifies 4-6 scenes and calculates frequency compensation, volume and pointing angle parameters; the sound execution module comprises a power amplifier, a unit loudspeaker array and an adjustable steering mechanism; the user interaction module supports far-field voice and touch operation; and the intelligent learning module is used for storing user data and optimizing and adjusting the model. According to the method, room acoustic characteristics are dynamically adapted, different use scenes are accurately matched, multi-region sound field collaboration is realized, the user preference is more and more fit when the method is used, and the auditory experience and use convenience are improved.

The invention discloses an electrical parameter estimation method and system based on Kalman filtering, and the method comprises the steps: defining an electrical parameter true value as a state quantity, building a state model, and configuring process noise; defining the collected original electrical parameters as observed quantities, establishing an observation model, and configuring observation noise; the process noise and the observation noise obey probability distribution based on the first variance and the second variance; in each sampling period, predicting a current state quantity and credibility; if the deviation between the observed quantity and the predicted state quantity is greater than a preset threshold value, temporarily adjusting the second variance; and dynamically calculating a filtering gain, and updating a state estimation value in combination with the predicted state quantity and the observed quantity. According to the method, high-precision estimation of the electrical parameters is realized through construction of the state model and the observation model and a mechanism of dynamically adjusting the filtering parameters.

The invention provides a self-adaptive attitude estimation method, system and equipment and a medium. The method comprises the following steps: acquiring three-axis acceleration data output by an inertial measurement unit; determining an acceleration noise estimation value at the current moment according to the multiple pieces of three-axis acceleration data; determining an adaptive coefficient according to the acceleration noise estimation value; correcting the initial proportional item gain and / or the initial integral item gain by using the adaptive coefficient to obtain the corrected proportional item gain and / or the corrected integral item gain; and outputting an attitude estimation value at the current moment through a preset filter based on the corrected proportional item gain and / or the corrected integral item gain. The filtering gain is dynamically adjusted according to the real-time noise characteristics of the sensor, so that the attitude estimation precision is improved, and the attitude estimation method has higher robustness and adaptive capacity.

The invention provides a method and device for estimating a noise covariance matrix in a Kalman filter. Time series measurements of the state relate to a state-space model, process noise, and measurement noise. And estimating a noise covariance matrix through the time sequence measurement value, wherein the noise covariance matrix is used for estimating the state of the future time. According to the scheme, it is assumed that a Kalman filter is realized by using an initial noise covariance matrix, a target function obtained based on an innovation sequence is minimized by changing a Kalman filtering gain, and the noise covariance matrix is updated according to the updated Kalman filtering gain. The process is an updating iteration process. In the process of updating the noise covariance matrix, the updated Kalman filtering gain and the covariance matrix corresponding to the key parameters obtained in the Kalman filtering process are used for iterative updating, so that a converged noise covariance matrix is obtained when the corresponding value of the target function is minimum. In the process, actual time sequence measurement values are used instead of prior information, and the estimation method is more stable and reliable.

An apparatus includes components configured to perform the following operations: acquiring two or more corresponding audio signals from two or more microphones; determining a first sound source direction parameter and a first sound source energy parameter in one or more frequency bands of the two or more audio signals based on processing of the two or more audio signals; determining a second sound source direction parameter and a second sound source energy parameter in one or more frequency bands of the two or more audio signals based on processing of the two or more audio signals; obtaining a region defining the direction and / or range for a filter; and generating a filter to be applied to the two or more audio signals, wherein filter gain / attenuation parameters are generated based on the relationship between the region and the first sound source direction parameter, the first sound source energy parameter, the second sound source direction parameter, and the second sound source energy parameter.

The invention relates to the field of robot intelligent control design, and discloses a robot joint state estimation method and system based on adaptive Kalman filtering, and the method comprises the following steps: S1, collecting the output data of a motor encoder of a robot joint and the torque feedback data of a joint driver; s2, establishing a joint state space model based on the dynamic relationship of the robot joints; s3, setting an observation vector according to the joint input data set; s4, setting initial values of a process noise covariance and an observation noise covariance, and constructing an improved adaptive Kalman filtering model; and S5, executing state prediction and state correction according to the improved adaptive Kalman filtering model. The technical effects of improving the robot joint trajectory tracking precision, reducing control jitter and enhancing the system stability are achieved by estimating the measurement noise and the process noise in real time and dynamically adjusting the filtering gain in cooperation with a feedback compensation strategy of a joint state estimation result in a control loop.

The invention discloses a multi-sensor fusion filtering method for a 2-D nonlinear system under a measurement deletion and energy harvesting mechanism. The method comprises the following steps: firstly, establishing a Tobit model for a censored measurement value; then, under the condition that the energy of the sensor is limited, the probability that the sensor can successfully transmit the censored measurement value and the conditional expectation and conditional variance of the censored measurement value relative to the system state are calculated; on the basis, a local outlier suppression Tobit Kalman filter is constructed and is used for realizing reliable estimation of the system state under the conditions of measurement deletion and energy limitation. Furthermore, a stochastic analysis method and a 2-D mathematical induction method are utilized to deduce the upper bound of the filtering error variance, and the upper bound is minimized by optimizing the filter gain. And finally, based on the local estimation result of each sensor, a more accurate global fusion estimation value is obtained by adopting a covariance cross fusion method.

The present application relates to the technical field of signal processing, and particularly relates to a noise elimination method, device, equipment and computer readable storage medium, the method comprising: obtaining an ambient sound signal of an external environment in which an audio device is located, and determining an ambient noise level of the ambient sound signal; obtaining an input audio signal of the audio device, and determining a base gain of a noise elimination filter of the audio device based on the input audio signal; adjusting the base gain based on the ambient noise level to obtain a filter gain, wherein the filter gain and the ambient noise level are in a negative correlation relationship; and processing the input audio signal of the audio device through the filter gain to eliminate noise when the input audio signal is played. The present application makes the noise elimination method adapt to different ambient noises, so that the noise elimination method can meet the needs of different ambient noises, thereby improving the practicability of the noise elimination method.

This invention discloses a snow ablation-optimized weak-sensitivity Kalman filter method and system for UAV state estimation, belonging to the field of UAV autonomous navigation and state estimation technology. The method includes: obtaining the posterior state estimate, posterior covariance matrix, and posterior sensitivity matrix of the previous time step; obtaining the prior statistics of the current time step based on ensemble Kalman filtering with ensemble point sampling and prediction propagation; searching online for the optimal scaling factor using the snow ablation optimization algorithm and scaling the preset sensitivity weight matrix; calculating the Kalman gain based on the weighted prior sensitivity matrix; updating the posterior state estimate using measurement data and outputting the flight state estimation result. This invention, while retaining the advantages of fast analytical calculation of the weak-sensitivity ensemble Kalman filter gain, achieves online adaptive adjustment of the sensitivity weights, improving the accuracy and robustness of state estimation in dynamic flight environments, and has low computational overhead, making it suitable for UAV airborne navigation systems.

The invention relates to the field of automatic driving perception and positioning, in particular to a self-parking attitude estimation method, device and equipment and a readable storage medium, and the self-parking attitude estimation method comprises the steps: determining a wheel slip rate according to a wheel collection wheel speed at a current moment and an average wheel speed in a set time period; determining the actual wheel speed at the current moment based on the wheel slip rate and the wheel acquisition wheel speed, and determining a preliminary prediction pose by combining the angular speed; determining Kalman filtering gain according to the wheel slip rate; and determining an estimated pose of the vehicle according to the preliminary prediction pose, the observation pose and the Kalman filtering gain. The problem that a positioning algorithm cannot actively correct a pose error through slippage information due to the fact that a wheel speed signal serves as a speed observation pose in the prior art can be solved.

This invention discloses a visual inertial wheel velocity meter SLAM method based on an improved adaptive UKF in the field of visual navigation technology, including the following steps: 1) establishing the state equation for the robot SLAM system; 2) establishing the measurement model of the visual sensor and wheel velocity meter; 3) determining the unscented transformation sampling points and weights according to the state space model; 4) transferring the sampling points through a nonlinear function and updating the system state prediction and state prediction covariance matrix; 5) updating the measurement and updating the process noise and measurement noise covariance matrix using an improved Sage-Husa algorithm; 6) introducing a convergence factor to update the state estimation, filter gain, and state estimation covariance matrix. This invention uses an improved Sage-Husa adaptive Kalman filter, which optimizes the filtering effect by adaptively adjusting the parameters of the UKF algorithm, thereby improving the real-time performance and accuracy of the robot SLAM system.

This invention discloses an adaptive closed-loop control method and system for a continuous stirred tank reactor, relating to the field of continuous stirred tank reactor control technology. The method acquires a preset homogeneous evolution model; performs multi-rate sampling through target nodes to obtain monitoring feature data; uses an attack assessment model to identify random spoofing attack characteristics of the communication channel and performs cleaning and noise reduction to obtain a measured data stream; calculates the data deviation, and when it exceeds a preset evolution threshold, uses the measured data stream as trigger data; inputs the trigger data into a preset distributed filter, solves the local filter gain to generate local state estimates, and weights and fuses these estimates to obtain global state estimates; finally, it compares the estimated state with a preset target state value to generate a state deviation signal, corrects the local filter gain, and generates an execution compensation control command. This invention effectively overcomes interference from multi-rate sampling, network attacks, and bandwidth limitations, achieving high-precision and robust adaptive closed-loop control of the reactor.