82 results about "Process noise" patented technology

The invention relates to the technical field of data control, in particular to a digital visual control method and system for a grease production line. The method comprises the following steps: acquiring full-process physical data; performing noise filtering on the whole-process physical data to obtain whole-process noise filtering data; abnormal data detection is carried out on the whole-process noise filtering data, and abnormal data are removed to obtain a whole-process data set of the grease production line; therefore, by constructing a feature driving mechanism of physical-data fusion of the grease production line, the problems of low data utilization rate, insufficient prediction precision and control optimization lag in a traditional grease production line are solved, and the operation efficiency and decision intelligent level of the grease production line under complex working conditions are improved.

The invention discloses a multi-channel measurement time difference fusion method for Kalman filtering, and the method comprises the steps: obtaining the historical data of multi-channel measurement time differences, and obtaining a series of historical mean values; monitoring multi-path measurement time difference input, judging interruption when any path of judgment observation value is missing or invalid based on a historical mean value, performing interruption compensation, and dynamically adjusting process noise covariance according to interruption duration; based on historical data, dynamically distributing weights; and fusing the multiple paths of time difference data through Kalman filtering, and outputting a smoothed time difference measurement result. Through an interruption compensation strategy (virtual observation and weight adjustment) and sliding window smoothing, even if single-path or multi-path input is interrupted, continuous fusion metering time difference can still be output; the dynamic weight distribution mechanism makes full use of statistical correlation of multi-path time difference, and the interrupt path dynamically adjusts the weight according to real-time reliability; and dynamically updating the process noise covariance Q so as to adapt to a time-varying noise environment and realize adaptive noise processing.

The invention discloses a gas sensor multi-mode adaptive preprocessing method based on a Mama state space model, and relates to the field of gas detection, and the method comprises the following steps: synchronously collecting original signals, environmental parameters and historical time sequence data of a sensor; performing feature extraction on the three types of data to generate a sensor feature vector, an environment feature vector and a historical feature vector; the method comprises the following steps of: obtaining a final fusion feature through processing, mapping the final fusion feature to a 128-dimensional embedding space through a contrast learning encoder, inputting a three-layer residual error connection Mamba time sequence prediction network to output a zero offset, dynamically predicting process noise and observation noise through a neural network, executing adaptive Kalman filtering to output a smooth signal, and outputting a final fusion signal. Adopting a learnable piecewise linear network to carry out nonlinear correction on the filtering signal; the gas concentration value is calculated by integrating the correction signal and the zero offset, and finally a confidence score and a data quality mark are provided; according to the method, high-precision, real-time and robust pretreatment of various gas sensors in a complex environment is realized.

The embodiment of the invention discloses a time synchronization method and device based on Kalman filtering, a medium and a product. According to the method, the precision and the speed of time synchronization are improved by establishing a double-state model containing clock skew and clock drift; a dynamic suppression factor is introduced to adaptively adjust an observation noise covariance to deal with abnormal noise; distinguishing and independently adjusting the process noise covariance of the clock skew and the clock drift by adopting a block self-adaptive strategy so as to match different noise characteristics of the clock skew and the clock drift; and finally, high-precision and high-robustness time synchronization is realized through Kalman filtering.

The invention discloses a trajectory tracking method of a non-repetitive time-varying system and application thereof. The method comprises the following steps: constructing a state space model for describing dynamic characteristics of the non-repetitive time-varying system; estimating the system state of the state space model by adopting an adaptive Kalman filtering algorithm; constructing a target function of system trajectory tracking according to the system state; and performing optimization processing on the objective function of system trajectory tracking to obtain an optimization result of system trajectory tracking. Compared with the prior art, the method has the advantages that the system state and parameter change can be estimated in real time, the adaptive capacity to dynamic noise and the state estimation precision are improved by adaptively adjusting the process noise covariance and the measurement noise covariance, and trajectory tracking errors are remarkably reduced.

The invention relates to a high-dynamic time division navigation signal tracking method assisted by adaptive Kalman filtering, and belongs to the technical field of satellite navigation. Aiming at the problems of tracking delay, precision reduction and easy lock loss caused by discontinuous loop update in a high dynamic environment in the existing time division navigation signal tracking method, the method comprises the steps of carrier and code stripping, time-slot error identification, adaptive Kalman filter AKF preprocessing and loop filtering and tracking of three-order FLL-assisted four-order PLL. And full-time-slot continuous tracking is realized. The core of the method is that process noise covariance is adaptively adjusted through AKF, optimal estimation and smoothing are carried out on errors, and then processing is carried out through a post-stage loop filter. According to the method, the tracking continuity, robustness and precision in a high dynamic environment are effectively improved.

The invention discloses a self-supervised ultra-wideband positioning method based on an improved double-delay depth deterministic strategy. The method comprises the following steps: acquiring channel impulse response data CIR and performing preprocessing; an improved double-delay depth deterministic strategy model is constructed to carry out UWB ranging error correction, the improved double-delay depth deterministic strategy model is based on a double-delay depth deterministic strategy gradient model, a double-commentator network structure is designed, a spatial-temporal feature coding layer based on a self-attention mechanism is introduced, and UWB ranging error correction is carried out. Capturing a long-range dependency relationship through a dynamic weight distribution mechanism, and establishing cross-timestamp time-space association feature mapping; a target network delay updating mechanism is introduced, so that the updating frequency of a target network is lower than that of a commentator network and an actor network, and action noise generated by a strategy is regularized; and generating a pseudo tag by adopting extended Kalman filtering, and carrying out iterative optimization to obtain a positioning result. The method is particularly suitable for processing noise and multipath effect in CIR data, and high-precision UWB positioning is realized.

The invention discloses an amphibious step chariot muzzle attitude measurement method and device based on process noise adaptive filtering, and the method specifically comprises the steps: determining the installation positions of an optical fiber inertial navigation device, an MEMS inertial navigation device and a photoelectric encoder according to an amphibious step chariot body; according to roll, pitch and course angle information, provided by optical fiber inertial navigation, of a gun and pitch angle and course angle information, provided by a photoelectric encoder, of a barrel of the gun, attitude information of virtual optical fiber inertial navigation is obtained through calculation; carrying out calculation by utilizing MEMS inertial navigation to obtain self attitude information; measuring the difference between the attitude information of the MEMS inertial navigation and the attitude information of the virtual optical fiber inertial navigation; a dynamic lever arm correction method and a flexural deflection noise correction method are adopted, and Sage-Husa process noise self-adaptive Kalman filtering is combined to estimate muzzle attitude information. The system has the capacity of self-adaption to amphibious environments, has the advantages of being convenient to install, stable in performance, high in measurement precision and high in real-time performance, and can effectively cope with complex and dynamic battlefield environments.

The invention relates to the technical field of GNSS high-precision positioning calculation, and discloses a GNSS troposphere delay error real-time correction method and device and a medium, and the method comprises the steps: presetting a first process noise model and a second process noise model corresponding to different constraint values; acquiring motion state parameters of a GNSS receiver, atmospheric disturbance characteristic measurement of bypass information and a model mismatch index of a Kalman filter; when a preset dynamic motion condition is met, selecting a first process noise model; when the preset dynamic motion condition is not met, the first process noise model or the second process noise model is selected according to the atmospheric disturbance characteristic measurement and the model mismatch index, multi-source information is used for cooperative judgment, an algorithm closed loop capable of achieving autonomous recognition and avoiding carrier motion pollution and bypass information failure is constructed, and the algorithm closed loop is optimized. Mathematical coupling of elevation and zenith delay is avoided, and high-reliability rapid convergence can be achieved for independent users without network assistance.

The application discloses an object grabbing method based on a denoising diffusion model, and relates to the technical field of robots, and comprises the following steps: S100, scene point cloud data processing; S200, forward process noise adding network training; S300, point cloud data completion; S400, object grabbing simulation; S500, feature aggregation; S600, aggregated feature noise adding; S700, feature heat map grabbing; and S800, final grabbing posture matrix calculation. The application realizes generation of a six-degree-of-freedom diversity grabbing posture for parallel clamping jaws, and improves the success rate, accuracy and diversity of grabbing.

This invention discloses a joint estimation method and system for state and characteristic parameters based on maneuver intensity perception. It primarily addresses the problems of filter divergence and low parameter estimation accuracy caused by sudden changes in target maneuvering when estimating parameters for hypersonic targets in existing technologies. The implementation scheme is as follows: receiving observation data and calculating innovation; introducing a forgetting factor to adaptively update the process noise covariance, and using the innovation norm to construct a gain factor to adjust the observation weights, completing the posterior state update; calculating the sliding window length based on the adaptive process noise covariance, and constructing the arrival cost using the output of the posterior state update; transforming physical constraints into a penalty function, and combining the arrival cost to establish a rolling time-domain estimation objective function and solve for the optimal estimate. This invention can perceive maneuver intensity in real time, dynamically adjust the estimation window and observation weights, significantly improving the accuracy and robustness of the joint estimation, and can be applied to guidance information extraction and trajectory prediction for near-space hypersonic gliders.

The present invention provides a noise label correction method for unsupervised domain adaptive person re-identification, which relates to the field of unsupervised domain adaptive person re-identification. The noise label correction method for unsupervised domain adaptive person re-identification uses a source domain dataset and its corresponding labels to train a source model; uses a clustering-based unsupervised domain adaptive person re-identification method to fine-tune the source model, and uses the noise label correction framework proposed in this article to correct the noise labels of the fine-tuned domain adaptation model. Compared with previous noise label correction methods, this method uses a noise label corrector to process noise labels as variables, and updates the probability label variables through back propagation, gradually correcting the noise labels while updating the variables. This method can significantly improve model performance and is a plug-and-play noise correction method.

The invention relates to the technical field of clock synchronization, in particular to a clock synchronization method based on a self-adaptive dynamic adjustment Kalman filter, which comprises the following steps of: defining a state vector containing clock skew and drift by adopting a second-order clock model, constructing a state transition and observation model, and calculating the state of the state transition and observation model; adaptively estimating a noise covariance based on a historical residual sliding window; measuring and predicting residual errors are recorded in each round of filtering, and the prediction residual errors are quantized; the residual error is stored in a sliding window, sample variance is calculated to obtain noise covariance initial estimation, and meanwhile, a simplified model of the proportional relation between clock skew and drift noise covariance is assumed; according to a covariance matching principle, using maximum likelihood estimation to calculate observation and process noise covariance from a sliding window residual error, that is, measuring a residual error square mean value as an observation noise covariance, presenting the process noise covariance in a matrix form, and accurately estimating noise characteristics; and performing exponential weighted moving average updating on the noise covariance by using a smoothing factor, performing Kalman filtering prediction and updating iteration to obtain clock skew and drifting optimal estimation, correcting a local clock according to the clock skew and drifting optimal estimation, and performing loop iteration to realize high-precision clock synchronization. According to the method, the noise covariance can be estimated and adjusted in real time, higher synchronization precision, higher convergence speed and higher robustness can be realized in a complex environment, and the problem that high-precision and stable clock synchronization is difficult to guarantee in a non-stable and high-dynamic wireless environment by the existing method is solved.

This invention relates to an adaptive Kalman noise estimation method and system based on data fusion. The method includes using multiple sensors to track a dynamic target and acquiring observations from each sensor; performing a first data fusion based on the observations from the first two sensors to obtain first fused data; performing a second data fusion using the first fused data and the observations from the next sensor to obtain second fused data; and so on, completing the data fusion of observations from multiple sensors to obtain fused observations from multiple sensors; and obtaining estimates of the process noise covariance matrix and measurement noise covariance based on the fused observations from multiple sensors. This invention improves the accuracy of measurement data by fusing measurement sequences obtained from multiple sensors, and solves the filtering problem of unknown second moments in the noise statistical characteristics of the Kalman model.

The invention discloses an automobile rear main reducer assembly noise suppression method and system, and belongs to the technical field of automobile part noise control. Comprising the steps that noise data and vibration data are processed through a trained recurrent neural network, and a noise prediction signal at the next moment is acquired; the recurrent neural network is deployed in the FPGA chip to accelerate the convolution operation of the weight matrix; acquiring real-time working condition data of the automobile, constructing a real-time feature vector representing noise and working conditions according to the real-time working condition data and the noise prediction signal, performing fuzzy reasoning by using a fuzzy rule base according to the real-time feature vector, and generating a noise control strategy so as to perform noise suppression through corresponding noise reverse sound waves; constructing and dynamically updating a fuzzy rule base by using an improved fuzzy C-means clustering method; noise reverse sound waves are generated by a sound system including no less than two sound field generating units. The stability of the noise reduction result can be improved, and the problem that the noise control effect of an existing rear main reducer assembly is not ideal is solved.

The embodiment of the invention discloses a GNSS receiver clock modeling method based on power-law noise, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: obtaining phase difference data between a local clock and a reference clock, and obtaining a diffusion coefficient of clock noise through extraction based on the phase difference data; establishing a clock state space equation containing a clock offset parameter and a frequency offset parameter, and calculating a process noise covariance matrix of the clock state space equation based on the diffusion coefficient; and embedding the two-dimensional clock random state model and the process noise covariance matrix into a process noise embedding filtering estimation algorithm, and calculating to obtain a clock offset parameter, a frequency offset parameter and a three-dimensional coordinate parameter of the receiver. According to the method, estimation noise of receiver frequency deviation can be remarkably suppressed, and the short-term stability of the elevation direction in GNSS dynamic positioning is effectively improved. The method and the device are suitable for the GNSS terminal adopting a low-cost clock so as to realize high-precision time synchronization and positioning.

The present invention provides a particle filter-based fixed-time predictive sliding mode control method for hypersonic aircraft. The method adds process noise to the input of a hypersonic aircraft system and measurement noise to the output of the hypersonic aircraft system, constructs a disturbed system state equation, and uses a particle filter to obtain the filtered state of the disturbed system. Then, in the undisturbed system state, the system discrete state space equation is selected as a prediction model to predict the sliding surface, and a fixed-time convergence law is designed. A fixed-time predictive sliding mode convergence law is obtained using the fixed-time convergence law. Finally, the filtered state of the disturbed system replaces the undisturbed system state to obtain a fixed-time predictive sliding mode controller. The present invention solves the problem of poor control performance of predictive sliding mode control under non-Gaussian random disturbances, thereby achieving rapid and stable control of hypersonic aircraft under random disturbances.

The application discloses a Kalman filtering multi-path measurement time difference fusion method, comprising the following steps: acquiring historical data of multi-path measurement time difference and obtaining a series of historical mean values; monitoring multi-path measurement time difference input, determining as interruption when determining observation value missing or invalid for any one path based on the historical mean values, performing interruption compensation, and dynamically adjusting process noise covariance according to interruption duration; dynamically allocating weights based on historical data; fusing multi-path time difference data through Kalman filtering and outputting smoothed time difference measurement results. Through interruption compensation strategy (virtual observation, weight adjustment) and sliding window smoothing, even if single-path or multi-path input is interrupted, continuous fusion measurement time difference can still be output; the dynamic weight allocation mechanism fully utilizes the statistical correlation of multi-path time difference, and dynamically adjusts the weight according to real-time reliability; the process noise covariance Q is dynamically updated to adapt to the time-varying noise environment, and adaptive noise processing is realized.

The invention relates to the technical field of loading control, and discloses an LQG control method and system for an integral configuration link, and the method comprises the steps: obtaining an actual measurement output vector, an expected output vector and a system state observation vector of a loading control system in a current control period, and constructing an integral type performance index functional; obtaining an augmented state space model and an augmented state estimation vector according to an original state space equation, a process noise covariance matrix, a measurement noise covariance matrix and a system state observation vector of the loading control system; based on the augmented state space model and the integral type performance index functional, establishing an extended Riccati differential equation to obtain an optimal feedback gain matrix; and performing linear feedback operation on the augmented state estimation vector based on the optimal feedback gain matrix, generating a control instruction of a loading control system, and realizing zero-static-error optimal tracking, independent and accurate setting of multi-channel integral intensity, adaptive optimization of controller parameters and robust stable operation under uncertainty.

The invention provides a bidirectional clock synchronization method based on an LQG control strategy, and relates to the technical field of wireless communication. Firstly, a state equation of master and slave clock nodes is constructed, clock skew of the master and slave clock nodes is defined, and a process noise covariance matrix of the master and slave clock nodes is established. And obtaining a corresponding timestamp based on bidirectional communication of the master clock node and the slave clock node. And constructing an observation equation of the master and slave clock nodes based on the timestamps. Based on the observation equation, the process noise covariance matrix and the state equation, state prediction and state updating of the master and slave clock nodes are carried out, and the optimal estimation value of the state of the master and slave clock nodes is obtained. And calculating a control value of an LQR controller based on the optimal estimated values of the states of the master and slave clock nodes, and adjusting the frequency of the slave clock nodes based on the control value to realize synchronization of the master clock nodes and the slave clock nodes. According to the clock synchronization method provided by the invention, the precision and robustness of clock synchronization are improved, and the synchronization requirement of a wireless communication network is met.

Embodiments of the present application disclose a Kalman filter-based time synchronization method and device applied to a distributed system, a medium and a product. In the method, a double-state model containing clock offset and clock drift is established to improve the precision and speed of time synchronization; a dynamic inhibition factor is introduced to adaptively adjust the observation noise covariance to cope with abnormal noise; a block adaptive strategy is adopted to distinguish and independently adjust the process noise covariance of the clock offset and the clock drift to match their different noise characteristics; and finally, high-precision and high-robustness time synchronization is achieved through Kalman filtering.

The invention relates to the technical field of sound effect data restoration, in particular to a game sound effect data restoration method based on artificial intelligence, which comprises the following steps: firstly, expanding original game sound effect data through a generative adversarial network integrated with a dynamic path selection mechanism so as to solve the problem of sample singleness caused by a fixed structure of a traditional generative adversarial network; further, a self-encoder based on quantum state sparse constraint is adopted to carry out multi-level repair on the damaged sound effect, noise and spectrum distortion are processed through a self-adaptive noise suppression module and a dynamic tuning feature feedback module respectively, and therefore the defects that a traditional self-encoder is insufficient in reconstruction precision and poor in detail capturing capacity are overcome; and finally, performing weighted fusion on the outputs of the plurality of restoration stages to generate a high-fidelity, natural and coherent final sound effect, thereby effectively solving the problems of poor adaptability to spectral characteristics and sound quality distortion after restoration in the prior art.

The present invention provides an adaptive unscented Kalman filtering method based on sequential state difference, including: establishing a state model of an adaptive navigation system based on sequential state difference; establishing a measurement model of an adaptive navigation system based on sequential state difference; constructing a mutation noise detection based on state chi-square test to determine whether to adaptively estimate the process noise covariance; if a mutated process noise is detected, adaptively estimating the process noise covariance; if no mutated process noise is detected, keeping the process noise covariance unchanged. The state chi-square test is used to detect the mutation of the process noise in real time. When a state noise mutation is detected, based on the dynamic model, this method is applied to the state estimation of a Mars spacecraft. This method can effectively suppress the estimation error and solve the divergence problem.

This invention relates to the field of rail transit technology and discloses a rail corrugation detection method based on anomaly detection and dynamic adaptation. The method includes: acquiring the axle box acceleration vibration signal of the rail; performing state prediction using a Kalman filter model to obtain a priori state estimate and calculating the measurement residual; dynamically adjusting the process noise covariance matrix of the Kalman filter model based on the measurement residual; determining a dynamic threshold based on the measurement residual; comparing the measurement residual with the dynamic threshold to determine whether the measurement residual is an outlier; if so, robustly correcting the priori state estimate to obtain a posterior state estimate. This invention effectively solves the problem that traditional fixed-parameter models cannot accommodate different track conditions, maintaining optimal filtering performance on various track grades and avoiding common problems in traditional methods such as filter divergence or tracking hysteresis.

The embodiment of the invention provides a state estimation method and device for autonomous navigation of a spacecraft and a storage medium, and the method comprises the steps: constructing an uncertainty nonlinear system for autonomous navigation of the spacecraft during the operation of the spacecraft, designing the gain of a state estimation error through employing a robust extended Kalman filtering algorithm, and guaranteeing the upper bound of the state estimation error; a quasi-consistency robust extended Kalman filtering algorithm is adopted to ensure that the actual covariance matrix of the state estimation error is equal to or smaller than the covariance matrix of the state estimation error generated by a filter, and an uncertainty nonlinear system with quasi-consistency is obtained; and solving the uncertainty nonlinear system with quasi-consistency to obtain state parameters of the spacecraft. The state estimation error upper bound is ensured, and the estimation precision of the filter can be improved; the uncertainty and the robustness stability of process noise and measurement noise can be ensured, and the state of the spacecraft can be accurately obtained.

The invention provides a radar track Kalman filtering method and system for gating cycle unit parameter optimization. The method comprises the following steps: step 1, constructing a Kalman-gating cycle unit track filtering calculation framework; step 2, collecting radar target track measurement data and corresponding true value sensor data to construct a training data set, training a Kalman-gated cycle unit in a framework, optimizing Kalman filtering process noise covariance estimation, observing noise covariance estimation, and learning gated cycle unit network parameters of real-time optimization gain; and step 3, based on an optimization learning result in the step 2, carrying out track filtering on radar real-time measurement information through a Kalman-gating cycle unit track filtering calculation framework. According to the invention, the accuracy and robustness of radar track state estimation are improved.

The invention discloses a self-adaptive rotary table space attitude estimation method and device based on multiple coded discs, and belongs to the field of attitude estimation. The method comprises the following steps: aiming at rotary table space attitude estimation at each moment, obtaining a rotary table azimuth angle measurement value fed back by an absolute code disc at the current moment and a rotary table pitch angular velocity measurement value fed back by an incremental code disc so as to construct a space attitude measurement matrix at the current moment; fusing measurement values fed back by the absolute code disc and the incremental code disc through a Kalman filtering algorithm to obtain a rotary table space attitude estimation value at the current moment; and constructing an adaptive method based on residual errors, and dynamically adjusting a process noise covariance matrix and an observation noise covariance matrix to cope with time-varying noise and model errors. According to the scheme, the advantages of the absolute code disc sensor and the incremental code disc sensor are combined, and the covariance weight is adjusted in real time based on the residual error, so that the uncertainty of system noise is balanced, the accuracy is high, and the safety is high.

The present invention discloses a filtering method based on probabilistic data association in a clutter environment where the observation noise is unknown. The method belongs to the field of adaptive filtering and addresses the problem that the observation noise in a clutter environment is difficult to estimate. A filtering method based on a probabilistic data association algorithm is proposed. The purpose is to solve the problem that the observation noise in a clutter environment is difficult to estimate and the accurate position of the target is difficult to determine. The core technology includes using a probabilistic data association algorithm to track the target in the clutter environment to obtain the target position, and using the obtained new information for observation noise calculation and filter update. Simulation results show that the invented method has good estimation performance in a clutter environment where the observation noise is much greater than the process noise.

The present invention discloses a multi-objective neural network speech processing method and device based on a microphone array. The method inputs multi-channel recording data and multi-channel echo data into an RLS filter for multi-channel linear echo cancellation, and then uses the frequency domain correlation between the filter's error signal and the estimated echo signal to calculate the residual echo energy of each frequency point; the acoustic features of the error signal and the estimated residual echo signal are input into a residual neural network, and multi-objective progressiveness is used to enable the residual neural network to converge quickly in a smaller network structure; the post-processing noise reduction, echo removal, and reverberation algorithm use a multi-channel Wiener filter with a mask for unified calculation, which has low complexity and avoids speech loss in the process of sequential processing of a single algorithm. Thus, the goals of speech noise reduction, echo removal, and reverberation are integrated, with the characteristics of low computational complexity, low latency, good real-time performance, and high speech quality, which meets actual usage requirements.