41 results about "Nonlinear filter" patented technology

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 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 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.

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.

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.

This invention discloses a robust relative navigation method for aircraft based on a hybrid distribution under non-Gaussian noise, belonging to the technical field of computation, estimation, or counting. To address the problem of filter divergence caused by non-stationary heavy-tail noise in time-varying environments during relative navigation, this invention introduces a Dirichlet random mixture vector fusion of Gaussian, Student's t, and multivariate K-distributions, proposing a Gaussian-Student's t-multivariate K-distribution modeling of measurement likelihood. Then, by minimizing the KLD of the true posterior probability density function and the approximate posterior probability density function using variational Bayesian techniques, the approximate posterior estimates of the aircraft's relative motion state and filter parameters are obtained, yielding the target's state information relative to the aircraft and solving for relative position and velocity. Finally, a nonlinear filter based on the Gaussian-Student's t-multivariate K-distribution is derived to improve relative navigation accuracy for angle-only relative navigation of aircraft in time-varying environments.

The invention discloses an under-actuated ship finite time formation control method based on a historical data triggering mechanism, and the method comprises the steps: constructing a ship formation virtual control law through introducing a power nonlinear feedback term according to a formation tracking error, and introducing a first-order nonlinear filter to carry out the filtering processing of the virtual control law, performing approximation processing on unknown function items in the error dynamic equation by adopting a radial basis function neural network to obtain an optimized error dynamic equation; and constructing an information guidance dynamic event triggering mechanism based on historical triggering data, combining a formation cooperative control law and a parameter updating law, and realizing under-actuated ship finite time formation control based on the historical data triggering mechanism. The method solves the problems that the dynamic balance between the control performance and the communication efficiency is difficult to realize and the finite time quick response requirement of the time-sensitive task cannot be met due to the fact that the data weight dynamic adjustment characteristic of the historical triggering moment is not fully fused in the existing method.

This invention belongs to the field of robot control technology, specifically relating to a global tracking control method for flexible joint robots based on obstacle functions. The method includes: S1: constructing and reconstructing the dynamic model of the flexible joint robot to adapt it to a reverse-engineering control framework; S2: designing a nonlinear filter that integrates obstacle functions, and using the obstacle functions to limit the filtering error within a preset range; S3: based on the nonlinear filter, recursively designing a controller using a reverse-engineering method, constructing an energy function, and performing stability analysis to ensure that all signals in the closed-loop system are globally consistent and bounded. This invention enables the control method to significantly improve tracking accuracy and system robustness while maintaining low computational complexity. It is suitable for robot systems with compliant actuation characteristics and has good engineering application value.

The invention discloses a time-varying optimization formation control method for networked robots with a layered architecture, and the method comprises the steps: collecting the pose and kinetic parameter information of the networked robots, carrying out the equation conversion based on kinematics and kinetic models, and constructing a chain control form capable of describing the state change; track correction is realized by setting a cost function and optimizing a signal generator; a nonlinear filtering function and a virtual controller are introduced to suppress position and speed errors; a radial basis function neural network is adopted to carry out approximation on an unknown nonlinear dynamic state, an adaptive control law is constructed in combination with error feedback, and time-varying optimization of the networked robot formation is realized. According to the method, the convergence speed, stability and robustness of the system can be effectively improved in dynamic and uncertain environments, it is guaranteed that the robot group achieves accurate formation control in complex tasks, and the method has high engineering application value.

This invention belongs to the field of measurement and control, and relates to a fixed-time preset performance control method, device, computer equipment, and storage medium for a macro-micro composite motion platform. The method includes: constructing a dynamic system model of the macro-micro composite motion platform; transforming the dynamic system model into a state-space equation suitable for a backstepping recursive control framework; designing a fixed-time preset performance function to constrain the tracking error signal within a preset range within a fixed time; constructing a nonlinear filter to simplify the control design process and establishing a switching function to resolve coupling terms in the system; designing an adaptive fixed-time preset performance tracking control algorithm and proving the stability of the closed-loop system based on Lyapunov stability theory. By designing the controller using an adaptive backstepping recursive control framework, the control design process is simplified, and the computational load is reduced; the tracking error signal of the closed-loop system converges to the preset range within a fixed time, improving the transient performance and control accuracy of the system.

The invention particularly relates to a sea clutter pulse interference suppression method for cascaded Hammerstein robust filtering, and the method comprises the steps: receiving an echo signal, and carrying out the preprocessing of the echo signal, so as to obtain a direct signal and a sea clutter signal, which are separated from each other; a cascaded Hammerstein filter is used to carry out multi-stage filtering processing on the separation signal, and a non-linear mapping output signal is obtained; wherein the cascaded Hammerstein filter comprises a first nonlinear filtering module, a second linear filtering module and a third nonlinear filtering module which are arranged in a cascaded manner; and carrying out filtering processing on the nonlinear mapping output signal through a robust filter to obtain an output signal after interference suppression. According to the method, nonlinear distortion and non-Gaussian pulse interference can be effectively suppressed in a complex marine environment, and the anti-interference capability, the signal fidelity and the adaptivity of a radar and a communication system are improved.

The invention discloses a novel dynamic surface funnel control method for a high-speed train with time delay and saturation, and relates to the technical field of high-speed train control, and the method comprises the steps: constructing a high-speed train longitudinal dynamic model with input time delay and input saturation; defining a displacement tracking error and a speed tracking error of the high-speed train; respectively setting funnel boundaries for the displacement tracking error and the speed tracking error based on a dynamic funnel boundary function; constructing a Lyapunov function, and constructing a virtual control law and a nonlinear filter; determining an actual control law of the high-speed train according to the high-speed train longitudinal dynamic model with input time lag and input saturation, the filtered virtual control law and the speed tracking error; the influence of input time lag on system performance is effectively solved, high-precision tracking control of the train is achieved under the non-saturation condition, meanwhile, the system can still be kept stable when saturation occurs, and therefore the control reliability and stability of the high-speed train under different working conditions are improved.

The invention relates to the technical field of light beam drift prevention, in particular to an interference accurate estimation and robust control method for a laser stable pointing system, which comprises the following steps of: processing an output signal through a zero-phase filtering technology based on the output signal of the laser stable pointing system to obtain a controlled object mathematical model; based on the controlled object mathematical model, a feedback controller is obtained through design; based on an input signal of the laser stable pointing system, a controller output quantity is obtained through a feedback controller; the controller output quantity passes through an enhanced adaptive robust controller to obtain a filtering disturbance estimated value; the enhanced adaptive robust controller comprises a Kalman filter, an adaptive robust controller and a nonlinear filter. The MR-H infinity control designed by the invention can significantly enhance the tracking performance and adaptability of the system, and the EARC provided by the invention can accurately estimate disturbance in a strong measurement noise environment. The control strategy of the invention can effectively suppress the adverse effect of strong sensor noise on the system, and significantly improve the tracking precision and anti-interference performance of the system.

The embodiment of the invention provides a satellite receiver signal processing method and device, and the method comprises the steps: carrying out the demodulation processing of a received signal, and obtaining a baseband signal; performing correlation calculation processing on the baseband signal and a local correlation sequence to obtain a correlation value; constructing a signal space based on the correlation value; performing linear filtering processing on the correlation value of the signal space to obtain a linearly filtered signal space; based on the signal space after linear filtering, the maximum correlation value under the same Doppler frequency and different code phase delays in the first dimension is searched, the sum of the distances between the maximum correlation value and the correlation values under the same Doppler frequency and other code phase delays is calculated, and the maximum distance is selected from the sum of the distances corresponding to the Doppler frequencies; performing nonlinear filtering processing on the correlation value of the Doppler frequency corresponding to the maximum distance to obtain a correlation value after nonlinear filtering; and determining whether a target signal exists based on the correlation value after nonlinear filtering. The performance of capturing the target signal can be improved.

The present application belongs to the technical field of image processing, and particularly relates to a kind of image sharpness evaluation methods for long focal length visible light imaging system. The present application first adopts median filter algorithm to pre-process image, and the algorithm is a typical representative of nonlinear filtering algorithm, which can eliminate isolated noise points, and can effectively protect edge contour information while removing random noise. This feature is very consistent with the high demand of edge information for sharpness function. Then, the pre-processed image is decomposed by NSST (non-subsampled shearlet transform), to obtain low-frequency components and high-frequency subbands, and a robust image sharpness evaluation method is constructed by combining direction consistency and contrast normalization.

This invention provides a biomimetic lateral line communication system based on propeller wake field perception, belonging to the field of underwater communication technology. Based on the lateral line perception mechanism of fish, it utilizes the flow field disturbance signal generated by the interaction between the propeller and the flow field as the communication signal to achieve network communication for underwater robots. The flow field disturbance of the main propeller is converted into a carrier signal to increase the transmission distance and intensity, while the disturbance of the modulation propeller is converted into a modulation signal for information transmission. A neural network model and a nonlinear filtering model are used to achieve signal modulation and demodulation, exhibiting good environmental adaptability and improving the working efficiency and accuracy of the detection system. The beneficial effects of this invention are: the signal source used for biomimetic navigation perception is the flow field medium fluctuation signal, which can provide a novel approach and practical detection sensing means for underwater covert detection and guidance tracking technology.

The application discloses a coordinate system transformation fusion filtering tracking method and system for a dual-base station radar, and relates to the technical field of radar target tracking. The method comprises the following steps: firstly, establishing state and observation equations, and determining an initial state of a target based on prior information in a Cartesian coordinate system. Then, a target motion state is obtained through one-step prediction, sigma points are generated by using U transformation, and a mean value and a covariance of state space prediction are calculated and constructed. Next, measurement data and prediction data are fused by using a Kalman filter to obtain optimal state estimation and a covariance matrix. Finally, the updated state estimation is converted back to the Cartesian coordinate system based on U transformation, and the process is repeated until the tracking is completed. The application can avoid the nonlinear filtering problem in the updating process, and improves the robustness and precision of tracking.

The embodiment of the invention provides a voice endpoint detection method, a storage medium and a program product. The method comprises the steps that a voice signal to be detected is acquired, framing and windowing processing is carried out on the voice signal, a framing and windowing signal is obtained, and the voice signal comprises noise; first nonlinear filtering processing and second nonlinear filtering processing are carried out on the framing windowing signal, a first enhanced signal and a second enhanced signal are obtained, and a delay parameter corresponding to the first nonlinear filtering processing is smaller than a delay parameter corresponding to the second nonlinear filtering processing; performing cepstrum domain feature extraction processing and fusion processing on the first enhanced signal and the second enhanced signal to obtain fusion features; and according to the fusion feature, the voice signal is detected to obtain a detection result, and the accuracy and stability of voice endpoint detection are improved.

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.

A real-time image denoising method based on GPU belongs to the field of image processing. The input picture is read by using opencv, and the picture pixel value is normalized to [0, 255]. The image data is transmitted from the host end to the device end, and the image data is stored in the shared memory. Each thread reads the corresponding 9 points in the shared memory in turn and stores them in the 3x3 window window. The number of salt and pepper noise points in the 3x3 window is judged, and the corresponding denoising method is used for processing. The data processed by the device end is transmitted to the host end, and the opencv is used to read and display the image after denoising. The method solves the problems of long filtering time, poor removal effect of mixed salt and pepper noise and Gaussian noise in the current nonlinear filtering field, and the processed image has the characteristics of less detail information loss, high image clarity and obvious texture characteristics. The denoised image can be processed and displayed in real time.

Decoding using rate distortion optimization including an inverse transform including transform size adaptive directional nonlinear filtering includes generating decoded block data by decoding a current block of a current frame. Decoding the current block comprises: obtaining quantized transform coefficients of the current block from the encoded bitstream; obtaining transform data of the current block from the encoded bitstream, wherein the transform data indicates a transform type and a transform size; obtaining dequantized transform block data by dequantizing the quantized transform coefficient; and obtaining decoded residual block data by inversely transforming the dequantized transform block data from the transform data. Inverse transform of the dequantized transform block data includes: obtaining intermediate decoded block data by combining prediction block data of a current block and decoded residual block data; and obtaining decoded block data by filtering the intermediate decoded block data using a transform size adaptive directional nonlinear filter.

The invention relates to the technical field of precise photoelectric detection and automatic control, and provides a tracking method and device based on inertial depth fusion and SR-CKF. According to the invention, the direct-drive torque motor is combined with the strapdown inertial stabilization platform, so that the influence of transmission errors and structural deformation is eliminated, and the stable tracking requirement of a long-distance small-pixel target is met. A compensated acceleration observation value is constructed by subtracting an original acceleration observation value from a motion interference acceleration, and an external speed is introduced, so that a photoelectric tracking system can keep a visual axis level when a carrier platform is in a strong maneuvering extreme working condition, and the condition of target loss is avoided. By adopting an SR-CKF attitude estimation algorithm, the nonlinear filtering divergence problem of a low-cost MEMS sensor in a high dynamic environment is effectively solved, and the reliability of long-time operation of a photoelectric tracking system is ensured. By constructing an annular buffer area, visual delay compensation is realized, and system oscillation caused by visual feedback lag is eliminated.

The invention discloses a multi-source information fused Mars entering integrated navigation method and system. The method comprises the following steps: acquiring multi-source measurement information of the Mars entering an aircraft in an entering stage, and performing time synchronization and coordinate conversion on the multi-source measurement information to obtain initial observation data; establishing a Mars entry dynamical model based on Mars rotation characteristics, and performing state prediction on the dynamical model by using the initial observation data to obtain a prediction state vector; constructing an observation model, associating the prediction state vector with the distance measurement and speed measurement information of the orbiter and the beacon to form a combined observed quantity, and performing estimation updating on the combined observed quantity by using a nonlinear filtering algorithm to obtain an updated state estimation result; and calculating the condition number of the observation matrix according to the updated state estimation result, and when the condition number is smaller than a preset threshold value, determining that the system is completely observable, and outputting a final navigation calculation result. The technical problem of inaccurate navigation in the Mars entering stage is solved.

The invention discloses a mining vehicle positioning method, device and equipment based on multi-sensor fusion and a medium, and belongs to the technical field of industrial vehicles. The method comprises the steps that in the running process of a mining vehicle, data collection is conducted through a UWB base station arranged on the mining vehicle, and data collection is conducted based on an IMU measuring unit of the mining vehicle; resolving to obtain UWB position information and an IMU state estimation value; the UWB position information and the IMU state estimation value serve as input, and the state estimation value is corrected through a nonlinear filtering algorithm; the weights of the UWB positioning module and the IMU are calculated according to the noise characteristics of the UWB positioning module and the IMU, weighted fusion is carried out based on the weights, and the positioning result of the mining vehicle is output. According to the technical scheme, the technical effect of high-precision, high-real-time and high-stability positioning of the mining vehicle in an underground complex environment can be achieved, and the core requirement of mine unmanned production for vehicle positioning is met.

This invention relates to the field of underwater positioning technology, specifically to an AUV cooperative positioning method based on generalized maximum correlation entropy and capacitive Kalman filtering (CKF) that can effectively improve positioning accuracy. This invention employs GMCC and introduces a generalized Gaussian density kernel function to measure the error of the nonlinear filtering algorithm using generalized correlation entropy. The generalized Gaussian kernel function has the advantages of having many parameters, flexible variation, and adaptability. It can achieve the maximum correlation entropy when the error is minimized. Because the generalized Gaussian kernel contains even-order higher moments of the errors of two variables, it can better handle heavy-tailed noise and large outliers. Experimental verification shows that the technical solution of this invention has better robustness and reliability, and can better handle problems such as outliers and heavy-tailed noise interference. Compared with the traditional CKF, this invention can improve positioning accuracy.

The invention relates to a filtering method and device based on a variational inference algorithm, electronic equipment and a storage medium, and the method comprises the steps: carrying out the sampling of an initial distribution state corresponding to the to-be-processed data of a target nonlinear filtering task, and determining a target particle distribution sample at an initial moment; performing prediction according to the target particle distribution sample at the kth prediction moment, and determining a predicted particle distribution sample at the (k + 1) th prediction moment, k being a positive integer greater than or equal to 0, and the 0th prediction moment being an initial moment; and calibrating the predicted particle distribution sample at the (k + 1) th prediction moment by using a velocity field generated by a variational inference algorithm, and determining a target particle distribution sample at the (k + 1) th prediction moment. According to the embodiment of the invention, the consumption of computing resources and time resources in the calibration process of processing the target nonlinear filtering task can be reduced, the calibration efficiency is improved, the loss of effective particle numbers in the calibration process is reduced, and the calibration precision is improved.

The application discloses a kind of water unmanned vehicle trajectory tracking method and system based on distributed fusion strategy, belong to automatic control field.First, the state estimation problem of water unmanned vehicle based on multiple radars is constructed;Then, by means of radar measurement vector, the mean and variance of random variable after transformation by nonlinear radar measurement model are approximated using unscented transformation, and a local nonlinear filter is designed under the least mean square error criterion;Further, the distributed fusion estimation value is obtained by fusing local estimates based on distributed fusion strategy, and the real-time tracking of unmanned vehicle motion trajectory is realized.The simulation results show that the application realizes the real-time tracking of water unmanned vehicle trajectory.

Extract intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds) by not only reducing steady noise (such as low-frequency-range environmental sounds or noise) but also reducing unexpected noise (such as high-frequency-range cries or speaking voices). This keeps up the intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds), while reducing the unexpected noise (such as high-frequency-range cries or speaking voices), using an improved adaptive filter unit and an improved nonlinear filter unit. Here, a reduction level of the unexpected noise (such as high-frequency-range cries or speaking voices) and a level of keeping up the intracorporeal sounds (such as low-frequency-range heart sounds or breath sounds) are quantitatively calculated.

This invention discloses a safety control method and system for unmanned surface vessels (USVs) based on an adaptive neural network observer, relating to the field of USV control technology. The method includes: constructing a second-order dynamic model of the USV using dynamic equations and obtaining its actual position and velocity state; designing an adaptive observer based on a neural network using the approximate nonlinear dynamics of a neural network to estimate the first-order system position state and the second-order system velocity state under DoS attacks and external disturbances; determining the position tracking error between the actual and estimated positions of the USV based on a preset desired trajectory and combining the actual and estimated positions, and constructing a virtual controller; designing a nonlinear filter based on the virtual control signal to obtain the trajectory tracking signal of the second-order velocity system, and then constructing an adaptive controller based on the estimated velocity state; and realizing the desired trajectory tracking of the USV under DoS attacks and disturbances based on the virtual controller and the adaptive controller.