126 results about "Nonlinear filter" patented technology

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

A data channel on an integrated circuit device includes a non-linear equalizer having as inputs digitized samples of signals on the data channel, decoding circuitry configured to determine from outputs of the non-linear equalizer a respective value of each of the signals, and adaptation circuitry configured to adapt parameters of the non-linear equalizer based on respective ones of the value. The non-linear equalizer includes a non-linear filter portion, and a front-end filter portion configured to reduce numbers of the inputs from the digitized samples. The non-linear equalizer may be a neural network equalizer, such as a multi-layer perceptron neural network equalizer, a reduced complexity multi-layer perceptron neural network equalizer, or a radial-basis function neural network equalizer. Alternatively, the non-linear equalizer may include a linear filter and a non-linear activation function, which may be a hyperbolic tangent function.

The invention discloses a fish multi-target tracking method based on nonlinear motion modeling and category constraint, and the method comprises the following steps: constructing a basic fish target detection model which is used for generating a fish target detection frame; constructing a target tracking data set in an MOT format for target tracking and a target identification data set for appearance feature extraction, and constructing a basic fish appearance feature re-identification model for extracting appearance features of a fish target; and constructing a target tracking model associated with a nonlinear filter based on detection confidence weighting and category gating. A confidence-weighted extended Kalman filtering algorithm is provided, and the algorithm dynamically adjusts weight distribution of a detection result in a track updating process by analyzing a confidence score of a target detection frame in real time. According to the category gating mechanism, a category similarity penalty term is introduced into a cost matrix of data association, and by establishing an association barrier of category perception, it is ensured that detection frames and estimation frames of different categories are not wrongly associated to the same identity.

The invention discloses a non-linear multi-agent system output feedback consistency control method, and belongs to the technical field of non-linear multi-agent system control. The method comprises the following steps: acquiring a multi-agent system comprising a leader and a plurality of followers; constructing a state observer according to the estimated value of the state variable of the follower; acquiring an auxiliary function and a dynamic surface with a nonlinear filter; on the basis of the state observer, the auxiliary function and the dynamic surface with the nonlinear filter, constructing and updating an actual controller of the multi-agent system by using a backstepping method; wherein the event triggering technology is adopted in the construction process of the actual controller and is used for controlling the updating frequency of the actual controller. According to the invention, the preset precision of the tracking error is set through the auxiliary function; eliminating boundary layer errors in each step of the backstepping method through a dynamic surface with a nonlinear filter; an unmeasurable state is observed through a state observer; the actual controller updating frequency is reduced through the event triggering technology, and resource consumption is reduced.

The invention provides an accurate calculation method and system for the axial pretightening force of an ultrasonic bolt, and the method combines a high-order statistic theory with a nonlinear filtering technology, and solves the problem that the measurement precision of a conventional method is insufficient under the conditions of strong noise and nonlinearity. Particle filtering and Kalman filtering are combined, so that the problem that a traditional Kalman filtering method easily causes sensitivity of an initial value of filtering divergence is solved; a double-Gaussian attenuation model is adopted to fit an echo envelope, the problem that a single Gaussian or single index model cannot adapt to envelope distortion caused by stress change is solved, and a nonlinear state space model is established to adapt to nonlinear echo signal characteristics under dynamic stress; gaussian noise is effectively suppressed by using a third-order cumulant cross-correlation algorithm, the time difference resolution in a low signal-to-noise ratio environment is remarkably improved, and the Gaussian noise and asymmetric interference are effectively suppressed.

The invention relates to the technical field of underwater vehicle inertial navigation, and particularly discloses an INS / DVL tight coupling combined navigation method, device and system of an underwater vehicle, and the method comprises the following steps: constructing an INS / DVL tight coupling combined model of an underwater robot; constructing a mahalanobis distance according to the error covariance of the combined model; determining a navigation state filter of the combined model according to a comparison result of the mahalanobis distance and a preset complex environment error judgment threshold value; if the environment is a complex environment, executing a robust state filter based on local consistency measurement; if the environment is a non-complex environment, executing a basic nonlinear filter; and determining navigation parameters of the underwater robot according to the filtering state output result. According to the INS / DVL tight coupling integrated navigation method for the underwater vehicle, the problem that noise parameters are not matched with actual conditions due to environmental changes can be solved on the premise of avoiding parameter excessive compensation in a low-complexity environment and reducing computing power waste.

The invention discloses a nonlinear filtering trajectory drop point prediction method based on a Runge-Kutta algorithm, and the method comprises the steps: firstly, preprocessing the trace point information of a shell, and converting a radar array plane rectangular coordinate system into an east-north-sky coordinate system; then, constructing a projectile mass point trajectory model, and if a reliable track exists, carrying out point track association on the preprocessed point track data and the reliable track based on an extended Kalman filtering algorithm; using a logic method track initiation algorithm to associate the trace point data which are not associated with the temporary track which is not prohibited so as to realize track initiation; carrying out track extrapolation on unupdated reliable tracks; performing flight path extinction on the reliable flight path meeting the extinction condition; and finally, resolving a trajectory by utilizing a Runge-Kutta method and combining a trajectory equation set, and further calculating a trajectory drop point. According to the method, target tracking filtering is accurate, trajectory extrapolation errors are reduced, the number of required measurement parameters is small, the calculation speed is high, rapid drop point prediction can be achieved, and the real-time requirement of actual engineering is met.

An apparatus and method are provided for digital predistortion of an electrical signal to pre-compensate for nonlinear distortions in a nonlinear channel (e.g., a nonlinear channel including a high-power amplifier). The digital predistortion processor includes a nonlinear filter. Filter coefficients are determined using a modified input electrical signal having multiple simultaneous tones, using an iterative process which adapts the modified input electrical signal to reduce levels of intermodulation distortions in a modified output electrical signal.

The invention discloses an aircraft robust relative navigation method based on mixed distribution under non-Gaussian noise, and belongs to the technical field of calculation, reckoning or counting. In order to solve the problem that non-stationary heavy tail noise caused by a time-varying environment in a relative navigation process of an aircraft generally causes filtering divergence, Dirichlet random mixed vectors are introduced to fuse Gaussian, student t and multivariate K distribution, so that a Gaussian-student t-multivariate K mixed distribution modeling measurement likelihood is provided; and then the KLD of the real posterior probability density function and the approximate posterior probability density function is minimized through the variational Bayesian technology to solve the relative motion state of the aircraft and approximate posterior estimation of filter parameters, the state quantity information of the target relative to the aircraft is obtained, and the relative position and speed are solved. Finally, a nonlinear filter based on Gaussian-student t-multivariate K mixed distribution is deduced, the relative navigation precision is improved, and the nonlinear filter is used for angle-measurement-only relative navigation of an aircraft in a time-varying environment.

The invention discloses an underwater polarization imaging device and method based on interference-free coded aperture correlation holography, and belongs to the field of computational optical imaging. According to the technical scheme, on the basis of an interference-free coded aperture correlation holographic system, two light paths are utilized, a target object hologram and a point spread function are synchronously collected through cross-polarization double light paths (0 degree and 90 degrees), after target information light is separated out through an underwater polarization imaging model, low-frequency background noise is filtered out through a nonlinear filtering reconstruction algorithm, and the target object hologram and the point spread function are obtained. And the phase-coded high-frequency object structure information is reserved, and the global estimation precision is optimized. According to the method, a complex optical path of traditional polarization imaging is simplified, the influence of local noise on polarization parameters is avoided, the image definition and the signal-to-noise ratio in a turbid underwater environment can be effectively improved, and the method is suitable for high-scattering scenes such as marine resource exploration, underwater robot navigation and military underwater target detection.

The invention provides an OBD message analysis and attitude rendering method and system based on a vehicle dynamics model, and belongs to the field of vehicle electronics and control. The method comprises the steps that a message is analyzed through an OBD interface, and vehicle operation parameters are obtained; establishing a unified time base and carrying out resampling and synchronous processing on the data of each channel; constructing a fifteen-degree-of-freedom vehicle dynamics model comprising a vehicle body six-degree-of-freedom pose, a vehicle body angular velocity, a four-wheel rotation angular velocity and unsprung mass vertical displacement for state prediction; designing an observation function to map a model state to an OBD observation space, and adopting an observability enhancement strategy; predicting and observing data are fused by using a nonlinear filter, and abnormal observation gating is carried out based on a mahalanobis distance and an amplitude threshold value; compensating system time delay and adaptively adjusting observation noise; and performing three-dimensional real-time rendering on the attitude information. According to the method, high-precision and high-robustness vehicle attitude estimation can be realized only by depending on the OBD message, and the system cost is effectively reduced.

The invention discloses an unmanned ship safety control method and system based on an adaptive neural network observer, and relates to the technical field of unmanned ship control, and the method comprises the steps: building a second-order dynamic model of an unmanned ship through a kinetic equation, and obtaining the actual position and speed state of the unmanned ship; designing an adaptive observer based on a neural network by using a neural network approximate nonlinear dynamic state so as to estimate a first-order system position state and a second-order system speed state under DoS attack and external interference; determining an actual and estimated position tracking error of the unmanned ship according to a preset expected trajectory in combination with the actual and estimated position states, and constructing a virtual controller; designing a nonlinear filter according to the virtual control signal to obtain a trajectory tracking signal of a second-order speed system, and constructing an adaptive controller in combination with the estimated speed state; based on a virtual controller and an adaptive controller, expected trajectory tracking of the unmanned ship under DoS attack and disturbance is realized.

Methods for video decoding and encoding, apparatuses and non-transitory computer-readable storage media thereof are provided. In one method for video decoding, a decoder may obtain a plurality of first motion compensated samples of a first channel and a plurality of second motion compensated samples of a second channel for a current inter coding block, obtain a non-linear filter, and obtain a plurality of filtered motion compensated samples of the first channel based on the non-linear filter, the first motion compensated samples of the first channel, and the second motion compensated samples of the second channel.

In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and / or a statistical filter.

The embodiment of the invention provides a signal processing method and device, a model training method and device, equipment and a storage medium. The method comprises the steps of obtaining a mixed sound signal and a reference sound signal; filtering a linear echo component in the mixed sound signal based on the reference sound signal to obtain a first filtered signal; obtaining an output signal through a pre-trained nonlinear filtering model and the first filtering signal, and sending the output signal to the far-end terminal equipment; wherein the output signal filters out a nonlinear echo component of a target energy quantity level relative to the first filtering signal, the target energy quantity level is smaller than a preset energy level, and the preset energy level is determined based on the energy level of the human voice component in the first filtering signal. The nonlinear component in the sample filtering signal is filtered based on the nonlinear filtering model, and meanwhile, the filtering amount of the nonlinear component is controlled, so that excessive suppression of human voice components is avoided, and the voice communication quality in a complex environment is improved.

The present invention discloses a hybrid active noise cancellation superposition method based on a neural network, including: W f W(n) or / and W b (n) is implemented by a linear filter FIR or IIR, and the filter parameters are iterated according to the LMS or RLMS algorithm. Since S(z) is non-linear, S’(z) can still be estimated by a neural network, and the parameters of the neural network are iteratively solved according to the BP algorithm. For the non-linear link existing in the path of the original noise propagation from the A-bit noise source to the B-bit speaker, such as the case where the original noise is too large, resulting in non-linearity of the device, if the feedforward filter can use a non-linear filter, it can better control the noise; for the non-linear link existing in the control channel from the C-bit error microphone to the B-bit speaker, such as the non-linearity of the error microphone or the non-linearity of the amplifier, if the feedback filter can use a non-linear filter, it can better control the noise.

A signal processing method, a model training method, an apparatus, a device, and a storage medium. The signal processing method comprises: acquiring a mixed sound signal and a reference sound signal (S101); on the basis of the reference sound signal, filtering a linear echo component in the mixed sound signal to obtain a first filtered signal (S102); and obtaining an output signal by means of a pre-trained nonlinear filtering model and the first filtered signal, and sending the output signal to a remote terminal device, wherein for the output signal, a nonlinear echo component of a target energy level is filtered out relative to the first filtered signal, the target energy level is less than a preset energy level, and the preset energy level is determined on the basis of the energy level of a human voice component in the first filtered signal (S103).A nonlinear component in a sample filtered signal is filtered out on the basis of a nonlinear filtering model, and the amount of the nonlinear component filtered out is controlled, avoiding over-suppression of the human voice component, and improving the voice communication quality in complex environments.

The invention discloses a vehicle-mounted Bluetooth call echo cancellation method and device, a storage medium and an electronic terminal, and the method comprises the steps: obtaining a first call voice signal, and carrying out the preprocessing of the first call voice signal, so as to obtain a to-be-detected signal, and the to-be-detected signal comprises a reference signal and a target signal; judging whether the call state of the to-be-detected signal is double-talk based on a double-talk detection method; if the call state is a double-talk state, performing first echo cancellation processing on the to-be-detected signal through a combination of an adaptive filtering algorithm and a nonlinear filtering algorithm to obtain a preliminary residual signal, then detecting the preliminary residual signal through a voice energy detection method, and judging whether residual echo exists in the preliminary residual signal; and if the residual echo exists in the preliminary residual signal, performing second echo cancellation processing on the residual signal to obtain a second call voice signal. According to the method, the limitation of a traditional single filtering method in a complex acoustic environment is overcome, the phenomenon of'sound absorption 'is effectively avoided, and the call definition is greatly improved.

A device for optically imaging a scene has at least one optical detector and an optical arrangement arranged in front of the optical detector, by means of which the scene can be imaged in a focal plane of the optical arrangement. The detector surface of the optical detector is arranged outside the focal plane and the optical arrangement has an amplitude or phase mask which enables improved algorithmic reconstruction of a sharp image of the scene from a blurred image acquired by the detector. In an alternative, the device additionally has an optically nonlinear filter arranged in the region of the focal plane which, when subjected to incident optical radiation with an intensity above a threshold intensity, changes its optical properties such that it blocks or at least attenuates this radiation.This device creates an optical sensor system that has an improved protective effect against interference radiation, in particular incident laser radiation, while maintaining almost unchanged size.

The invention provides a high-precision deformation monitoring method and system based on tight combination of Beidou and IMU, and a medium, and relates to the field of geological disaster monitoring and engineering structure safety monitoring and early warning. The method comprises the following steps: constructing a three-dimensional displacement increment between Beidou carrier TDCP epochs, collecting triaxial acceleration and angular velocity data by using an IMU, carrying out tight combination modeling on calculation results of the two, constructing a nonlinear filtering equation taking the displacement increment as an observed quantity, realizing complementation of low-frequency high precision and high-frequency anti-interference through weighted fusion, and obtaining a monitored displacement quantity. Through cooperation of displacement change calculation between Beidou epochs and IMU vibration signals, complementation of low-frequency high precision and high-frequency anti-interference is achieved, the real-time performance of calculating collapse and slip displacement is improved, the displacement magnitude estimation error is reduced, the data utilization rate and deformation monitoring precision are improved, seamless switching of dynamic and static monitoring modes is achieved at the same time, and the real-time performance of the system is improved. And the robustness in a complex environment is enhanced.

The invention relates to the technical field of digital twinning and system health state prediction, in particular to a steering system model construction method based on digital twinning. Comprising the steps of 1, constructing a high-fidelity physical mechanism model representing the dynamic characteristics of the steering system; acquiring a working condition data flow of the steering system in real time; constructing a state observer; resolving a residual signal through a state observer; step 2, constructing an augmented state vector; performing recursive estimation on the augmented state vector by adopting a nonlinear filtering algorithm; 3, injecting the online identification parameter sequence into the high-fidelity physical mechanism model in real time to realize adaptive updating of the physical mechanism model; collecting historical data of the online identification parameter sequence; training a degradation prediction model by using historical data; and predicting and outputting a degradation trend prediction time domain of the steering system. According to the method, the fidelity and credibility of the model are remarkably improved, and a solid foundation is laid for subsequent analysis and prediction.

The application discloses a kind of based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, it is related to chromatic aberration detection technical field, including multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end, the multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end pass through on-board bus communication protocol and carry out two-way data interaction between, and each end is electrically connected with central logic processing matrix.This based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, the present application is aimed at atmospheric data and inertial navigation data this kind of multi-source heterogeneous data, establishes the unified fusion framework based on unscented kalman filter, and introduces multiple fading factor matrix, solves the problem of insufficient tracking ability of traditional method under strong nonlinear and large maneuvering flight conditions, significantly improves the precision of attitude estimation.

The application belongs to the technical field of target tracking and fusion, and relates to a nonlinear filtering distributed target tracking method based on variational inference. Measurement noise parameters of a conventional nonlinear filtering distributed algorithm are assumed to be known, and the application extends the nonlinear filtering distributed algorithm based on variational inference to a single-target tracking scene under unknown measurement noise parameters. Under the distributed algorithm, weight likelihood parameters of each sensor to the target state tend to be consistent, so that the algorithm has strong robustness, small calculation amount, high flexibility and timeliness, and in combination with the variational inference method, the algorithm can achieve high-precision target tracking in a distributed multi-sensor scene under unknown measurement noise variance. The algorithm has close tracking precision to a distributed particle filtering algorithm with known measurement noise variance, and enhances practicability.

Methods, apparatus, and non-transitory computer-readable storage media for video decoding and encoding are provided. In a method for video decoding, a decoder may obtain a plurality of first motion compensation sample points of a first channel and a plurality of second motion compensation sample points of a second channel for a current inter-frame coding block, obtain a nonlinear filter, and obtaining a plurality of filtered motion compensation samples for the first channel based on the non-linear filter, the first motion compensation samples for the first channel, and the second motion compensation samples for the second channel.

A noise reduction system for actively compensating background noise in a passenger transport area of a vehicle. The noise reduction system includes a nonlinearity filter unit having a model of a non-linear transfer function of the sound generator, wherein the nonlinearity filter unit is configured to receive the anti-noise signal and to generate a filtered anti-noise signal by applying a non-linear filter function on the anti-noise signal, which is based on the model of the non-linear transfer function in that the non-linear response of the sound generator is at least partially corrected when driven by the filtered anti-noise signal. Wherein the nonlinearity filter unit is further configured to output the filtered anti-noise signal to a sound generator.

An apparatus and method are provided for digital predistortion of an electrical signal to precompensate for nonlinear distortions in a nonlinear channel (e.g., a nonlinear channel including a high-power amplifier). The digital predistortion processor includes a nonlinear filter. Filter coefficients are determined using a modified input electrical signal having multiple simultaneous tones, using an iterative process which adapts the modified input electrical signal to reduce levels of intermodulation distortions in a modified output electrical signal.

The application provides an example in the technical field of signal processing, and provides an audio device, an echo cancellation method and an apparatus. The audio device comprises a loudspeaker, a microphone, and a processor connected with the microphone and the loudspeaker respectively. The processor is configured to: acquire an audio signal frame received by the microphone; perform echo cancellation processing on the audio signal frame; if a nonlinear filter suppression coefficient used in the echo cancellation processing is less than a coefficient threshold value, and a current call state is detected as a double-talk state, then add 1 to a count value; if the count value is greater than or equal to a count threshold value, then reduce the volume of the loudspeaker; if the count value is less than the count threshold value, then perform the acquisition of the audio signal frame received by the microphone; if the nonlinear filter suppression coefficient is greater than or equal to the coefficient threshold value, or the current call state is detected as a non-double-talk state, then clear the count value, and perform the acquisition of the audio signal frame received by the microphone. The application can effectively eliminate echo.

A technique for texture filtering. A transition is made from a first mipmap corresponding to a first texture resolution to a second mipmap corresponding to a second texture resolution. The first texture resolution is lower than the second texture resolution. As compared to standard trilinear filtering, initiation of the transition is delayed by an offset (or bias), which serves to delay the initial use of the second mipmap until it has been loaded. Following initiation of the transition, first and second weightings are selected with a nonlinear filter, and the system interpolates between the first mipmap and the second mipmap by applying the weightings. During an initial portion of the transition, the nonlinear filter has a slope that is higher than that of the standard trilinear filter.

The application discloses a method, device and equipment for applying an improved nonlinear filtering algorithm to a UAV, and relates to the technical field of UAV application. The method comprises the following steps: based on each sensor arranged on the upper portion of the UAV, collecting GNSS data and IMU data in real time and synchronously according to a set sampling frequency and inputting the GNSS data and the IMU data into the improved nonlinear filtering algorithm; performing preliminary fusion on the GNSS data and the IMU data through an unscented Kalman filtering algorithm, and combining UT transformation and an adaptive mechanism to obtain a preliminary state estimation value of the UAV; inputting the preliminary state estimation value into a cubature Kalman filtering algorithm, updating and predicting the state of the UAV based on a cubature rule, and obtaining final positioning and attitude estimation results and applying the final positioning and attitude estimation results to the UAV. The application can greatly improve the positioning and attitude estimation precision of the UAV.

The present application integrates the idea of separating noise and true value into the PLKF framework, and proposes an unmanned aerial vehicle target positioning method based on two-stage unbiased pseudo-linear Kalman filtering (2S-UPLKF). The angle estimation based on EKF in the first stage can effectively decouple noise interference when the unmanned aerial vehicle is tracking at a long distance, providing a robust initial state for the system. The second stage constructs a noise-true value separation mechanism to eliminate the correlation between the observation matrix and the noise in the pseudo-linear equation from the principle level. This method not only inherits the advantage of efficient calculation of pseudo-linear filtering, but also realizes the active suppression of bias in the dynamic tracking process through two-stage collaborative optimization. Theoretical analysis and simulation experiments show that, compared with other nonlinear filtering algorithms, it can still effectively suppress error fluctuations under extreme conditions such as strong nonlinear observation, large angle noise and long distance observation, and has better tracking performance.