71 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 invention discloses a fast-locking low-jitter sub-sampling phase-locked loop structure. The sub-sampling phase-locked loop comprises a sub-sampling Bang-Bang phase discrimination circuit, a self-adaptive variable step size digital filter circuit, a delta sigma digital-to-analog conversion circuit and an oscillator circuit. The sub-sampling Bang-Bang phase discrimination circuit generates a phase discrimination output digital control signal according to an input reference signal and a feedback oscillator signal phase error, the adaptive variable step size digital filter circuit adaptively adjusts a filtering gain according to the phase discrimination output digital control signal, and the output of the filter circuit realizes digital-analog conversion after oversampling modulation. And the output analog signal controls the voltage-controlled oscillator to realize rapid low-jitter phase locking of the output and the reference signal.

The invention relates to a self-adaptive longitudinal vehicle speed estimation method, which comprises the following steps of: establishing detailed vehicle dynamic models, including a vehicle longitudinal dynamic model and a wheel speed dynamic model; a Sage-Husa self-adaptive unscented Kalman filtering algorithm is combined with a vehicle dynamics model, a state equation and an observation equation are designed, the longitudinal vehicle speed of a vehicle serves as a state variable, and observation vectors are constructed through data collected by a wheel speed sensor, an acceleration sensor and the like; divergence calculation is introduced for detecting and correcting divergence phenomena in the filtering process; and on the basis of a self-adaptive mechanism of a Sage-Husa algorithm, the filtering gain is adjusted in real time, and the vehicle speed estimation process is optimized. The improved Sage-Husa adaptive filter can dynamically adjust the filter parameters according to the change of the system state and the difference of noise, detects transient disturbance in combination with divergence calculation, reduces the influence of the transient disturbance on longitudinal vehicle speed estimation, and effectively improves the estimation precision.

The invention relates to an unmanned vehicle safety control method and system for resisting hidden false data injection attack. The method comprises the following steps: based on an unmanned vehicle information physical system, constructing a normal system which is not attacked and a system which is attacked by a hidden actuator, and constructing a difference system according to a state difference value of the two systems; solving the steady-state Kalman filtering gain of a normal system; constructing a linear matrix inequality condition for ensuring the stability of a normal system and the state of a differential system to be bounded based on the non-escapable set, and obtaining a controller gain meeting the condition; according to robust model predictive control, an augmentation vector and a prediction equation are constructed, a difference system bounded and stable minimum and maximum problem is given and converted into a linear matrix inequality to obtain an input signal of the difference system, and finally an input signal of an attacked system is obtained and applied to the attacked system, so that the difference system is bounded and stable, and the attacked system is more stable. And the system is stable and safe when being attacked. According to the invention, the state of the attacked system is stable and the system is safe.

The invention discloses an AI-based high-fidelity audio coding and decoding algorithm adaptive to an auditory range and a compression optimization method, and the algorithm comprises an audio signal preprocessing module which is used for carrying out the preprocessing of an input audio signal, including but not limited to denoising, frequency filtering, gain adjustment, time domain / frequency domain transformation and the like, and carrying out the compression optimization of the audio signal; and the preprocessing module is used for performing feature extraction through a convolutional neural network (CNN) or an auto-encoder network so as to improve the signal-to-noise ratio of an audio signal and remove environmental noise, and generating optimized audio features. Through an AI-based auditory range adaptation algorithm, dynamic optimization can be performed according to individual auditory characteristics, so that the audio coding and decoding process is more in line with the perception characteristics of human ears, better balance is obtained between the compression ratio and the tone quality, higher audio experience is provided, and through the adaptive quantization and code rate control technology, the audio coding and decoding efficiency is improved. According to the method, on the premise of keeping the high-fidelity tone quality, the deep learning algorithm is utilized to optimize the allocation of computing resources, and the consumption of the computing resources is reduced.

The invention discloses a simulated Kalman filter circuit based on a molybdenum disulfide (MoS2) memristive transistor. Efficient fusion of sensor data is realized in the field of automatic driving. According to the circuit, the Kalman filtering gain can be accurately adjusted by adjusting the conductance value of the MoS2 memristor transistor, so that the circuit is suitable for various automatic driving scenes. The simulated Kalman filter circuit can effectively fuse a laser radar and a millimeter wave radar, can accurately estimate the coordinates of a target vehicle, and can significantly reduce power consumption while realizing a data processing speed equivalent to that of a digital filter.

The invention discloses a radiation intensity measurement method of an infrared radiation simulation source system, and belongs to the technical field of infrared radiation characteristic measurement. The method comprises the following steps: firstly, performing radiometric calibration operation on an infrared radiation simulation source system and an infrared detector; a medium-wave infrared optical system and an infrared detector are used for receiving infrared radiation signals, and continuous radiation is converted into alternating signals through modulation of an optical chopper, so that the measurement signal-to-noise ratio is increased. Collected alternating signals are processed by the filtering gain amplification circuit so as to enhance signal stability. And then dynamic denoising and state estimation are carried out on measured data by adopting a Kalman filtering algorithm, and nonlinear error compensation is carried out in combination with a medium-wave infrared radiation coefficient correction algorithm, so that a high-precision radiation intensity correction coefficient is obtained. Compared with a traditional method, the self-adaptive filtering and radiation intensity correction technology is combined, the measurement precision and the real-time performance are improved, and the method can be widely applied to the fields of infrared optical testing, target characteristic analysis and radiation measurement.

The invention provides an integrated navigation information fusion method and system considering driving state deviation, and belongs to the technical field of positioning navigation, Kalman gains generated by a first filter in past n periods are obtained, a position difference and a speed difference between an INS and a GPS in a current GPS signal interruption period are predicted by using a pre-trained LSTM, and the position difference and the speed difference between the INS and the GPS in the current GPS signal interruption period are calculated. The vehicle driving state deviation is obtained; using a second filter to update a filter gain with the predicted vehicle driving state deviation as an input; and predicting the difference between the output of the first filter and the output of the second filter by using a pre-trained TL-LSTM, and correcting the INS navigation result by taking the additionally corrected optimal estimated value as final compensation. The GPS / INS integrated navigation information fusion considering the driving state deviation combines a long short-term memory network (LSTM) and error state Kalman filtering (ESKF), and secondary correction is performed on an INS navigation result to ensure the positioning precision of the integrated navigation system in a lock losing state.

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.

To provide a device and a method for spatial audio-filtering in a spatial audio capture.SOLUTION: A device includes means configured to acquire a plurality of audio signals from a plurality of microphones, determine a first sound source direction parameter and a first sound source energy parameter in one or more frequency bands of the plurality of audio signals on the basis of processing of the plurality of audio signals, determine a second sound source direction parameter and a second sound source energy parameter in one or more frequency bands of the plurality of audio signals on the basis of processing of the plurality of audio signals, acquire an area for defining a direction and / or a range for a filter, and generate a filter to be applied to a plurality of audio signals. The device generates a filter gain / attenuation parameter on the basis of an area associated with 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.SELECTED DRAWING: Figure 1

The invention discloses a 2-D nonlinear system guaranteed probability filtering method and system under full-duplex relay network energy collection constraint, and the method comprises the steps: constructing a state space expression of a 2-D nonlinear system, designing a full-duplex relay network communication mechanism based on a coding and decoding forwarding mechanism, constructing an energy collection constraint model, and carrying out the energy collection constraint of the 2-D nonlinear system. The method comprises the four steps of constructing an augmented system and designing a filter, solving a minimum ellipsoid constraint which meets probability requirements and contains an estimation error and a corresponding optimal filter gain by using a recursive linear matrix inequality, and enabling the minimum ellipsoid constraint and the corresponding optimal filter gain to be more accurate in a long-distance wireless communication network environment by using a stochastic analysis method, a set membership filtering idea and a 2-D mathematical induction method. The filtering method provided by the invention can realize reliable estimation on the 2-D nonlinear system with a guaranteed probability under the influence of random noise.

The invention discloses a differential privacy fusion filtering method of an asynchronous sampling positioning system in a low-reliability environment. The method comprises the following steps: 1, establishing a dynamic model of an asynchronous sampling nonlinear positioning system based on sensor network communication; 2, converting the asynchronous sampling nonlinear positioning system dynamic model into a synchronous sampling nonlinear positioning system dynamic model through a zero-order holding strategy; 3, designing a differential privacy distributed fusion filter; 4, calculating one-step prediction and one-step prediction error covariance; 5, deriving a local filter gain according to the one-step prediction error covariance; 6, substituting the local filter gain and the one-step prediction into the step 3 to obtain fusion filtering; and 7, calculating a local filtering error covariance upper bound. According to the method, the problem that the privacy protection and filtering performance of the positioning system cannot be considered cooperatively in the existing filtering method is solved, and meanwhile, the influence of randomly generated nonlinearity of the sensor on the fusion filtering effect is discussed, so that the estimation precision of the problem is improved.

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 a radiation source cooperative positioning method and device, electronic equipment and storage medium, and belongs to the wireless positioning technical field. The predicted position of the radiation source is determined according to the time difference of arrival and the position information of the observation station, and the preliminary positioning of the radiation source is realized. The predicted position of the radiation source is iteratively updated according to the preset Jacobian determinant, and the single positioning information of the radiation source is obtained, so that the accuracy of the radiation source positioning is improved. The radiation source information is determined and updated according to the predicted value of the radiation source information, the time domain filtering gain, the first observation quantity and the time difference observation quantity, the continuous tracking and positioning of the radiation source are realized, and the accuracy of the radiation source positioning 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.

A 2-D system probabilistic security filtering method under relay network spoofing attack comprises the four steps of establishing a state space model of a 2-D system, establishing an amplification forwarding relay communication model with spoofing attack, designing a security filter and solving an optimal filter gain matrix. By means of a 2-D mathematical induction method and a stochastic analysis method, the designed filtering method can filter a 2-D system with a given guarantee probability in a complex communication scene of random noise, long-distance transmission and hostile attack. And more schemes are provided for the filtering problem of a complex system in practical engineering while the filtering precision is ensured.

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 utility model relates to a load ground end compensation circuit, an arbitrary waveform generator and a testing machine, the load ground end compensation circuit comprises a near-end buffer circuit, a filtering gain circuit and a driving circuit, the near-end buffer circuit receives a signal of a load ground end and outputs a buffer signal; the filtering gain circuit carries out filtering and gain processing on the received buffer signal and then transmits the signal to the driving circuit, and the driving circuit receives the signal output by the filtering gain circuit and outputs a driving signal to a tested device so that interference such as noise introduced by wiring between the near-end buffer circuit and the filtering gain circuit can be effectively inhibited. And the NSD, SFDR and THD performances of the arbitrary waveform generator are improved.

The invention discloses a nonlinear pipeline system state estimation method and system based on unscented Kalman filtering and a storage medium, relates to the field of network control, and aims to solve the problems of limited state estimation precision and high power consumption caused by signal attenuation, nonlinear characteristics and limited communication resources in the existing method. Comprising the following steps: 1, establishing an oil pipeline system dynamic model, and discretizing to obtain a state space model; 2, designing a dynamic event triggering mechanism, and constructing an amplification forwarding relay system to obtain amplification measurement signal data; step 3, setting initial values of state estimation and a covariance matrix; step 4, weighting 2n + 1 Sigma sampling points obtained by unscented transformation according to a weight coefficient to obtain one-step prediction and a one-step prediction error covariance Pj + 1j; 5, designing a filter according to a system phenomenon, calculating an estimation error, calculating an estimation error covariance upper bound matrix by using a matrix inequality, and solving a filtering gain matrix Kj + 1; and step 6, executing the step 4 and the step 5 until the total duration is reached.

The present invention discloses a method for evaluating the attitude accuracy of a GPS module / star sensor based on a hybrid chi-square detection, which specifically includes the following steps: (1) obtaining the attitude information of the GPS module and the star sensor; (2) respectively evaluating the accuracy of the attitude information of the GPS module and the star sensor by using the innovation chi-square detection, obtaining the corresponding measurement innovation test statistic, and judging whether the output data is abnormal; (3) evaluating the accuracy of the attitude combination information of the GPS module and the star sensor by using the state chi-square detection, obtaining the combined state test statistic, and judging whether the combined output data is abnormal; (4) when there is an abnormal situation in the output data in step (1) or step (2), constructing an equivalent weight factor and an equivalent covariance matrix; (5) constructing a filter gain equation and a state estimation equation, and using the Kalman filtering method to solve and obtain the final attitude result. The present invention realizes error compensation in the case of failure of the GPS module or the star sensor, and improves the robust performance of the filtering.

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.