19 results about "Robust filter" patented technology

The invention discloses an online iterative learning control method and device based on bidirectional frequency binary search, and belongs to the technical field of tracking control, and the method comprises the steps: building a piezoelectric positioning system, and enabling the piezoelectric positioning system to have a certain low-frequency disturbance suppression capability while keeping stable through adjusting the parameters of a proportional-integral controller; constructing a control structure combining a YK parameterization method and iterative learning control; constructing a robust filter in iterative learning control by adopting a filter design method of a parallel cascade composite structure; a differential parameter constraint space is set for each filter based on a bidirectional frequency binary search method, and iterative updating of filter parameters is driven in a time domain by taking norm minimization of system errors as an optimization target, so that continuous optimization of online disturbance suppression performance is realized. According to the method, YK parameterization and an iterative learning control structure are combined, and the stability of the piezoelectric positioning system in the whole frequency domain can be ensured.

The invention provides a gross error detection method, gross error detection equipment, a readable storage medium and a product, and relates to the technical field of positioning. The method comprises the following steps: inputting a satellite observation value into an RTK filter, and obtaining a residual error output by the RTK filter at the current moment and a prediction variance covariance matrix of the residual error; determining an error in a unit weight according to the residual error and the predicted variance covariance matrix, and calculating a dynamic threshold value according to the error in the unit weight; according to a preset experience threshold value and a dynamic threshold value, performing first gross error elimination on the residual error to obtain a first residual error sample; according to the condition number of the predicted variance covariance matrix, performing second gross error elimination on the first residual error sample to obtain a second residual error sample; and performing de-correlation calculation on the second residual error sample construction, and performing adaptive robust filtering on a de-correlation residual error subset. According to the method, gross error elimination is carried out from a data layer and a model layer, and more accurate and efficient RTK gross error detection capability is achieved.

The application discloses a kind of unmanned aerial vehicle flight control method and system, for solving the protection level failure caused by navigation error non-gaussian distribution and the problem of frequent false trigger of flight control of unmanned aerial vehicle in complex electromagnetic environment of city low altitude. Method includes obtaining bottom sensor original observation value, using robust filtering algorithm to suppress multipath interference and output residual error;Non-gaussian error boundary is constructed based on set theory geometric envelope algorithm, and the rigidity quantitative index of navigation reliability is deduced by protection level;Dynamic mapping operation risk alarm limit, and introduce time anti-shake tolerance window to filter transient signal burr;When reliability is continuously broken down, trigger state machine to execute stepwise autonomous degradation. The application breaks the limitation of traditional gaussian hypothesis, and considers the absolute safety and task continuity of flight.

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 application belongs to the technical field of navigation, and proposes an adaptive robust filtering navigation method based on multivariate t distribution and Bayesian shrinkage. First, multi-source heterogeneous sensor observation data is obtained. Second, the multi-source heterogeneous sensor data is modeled based on multivariate t distribution, and the innovation of the multi-source heterogeneous sensor observation data is extracted. Then, a continuous confidence factor is constructed, the fault detection is converted into a Bayesian inference problem, and the confidence diagnosis of the observation innovation is performed based on the confidence factor. Then, the innovation discount factor is constructed based on the confidence factor, and the weighted fusion target function is constructed based on the observation fitting term and the virtual robust term, so as to obtain the equivalent innovation observation, the equivalent observation noise covariance matrix, the equivalent innovation covariance and the robust Kalman gain. Finally, the observation information weight is dynamically adjusted, the equivalent parameters are used to complete the robust filtering iteration, and the optimal navigation solution is obtained. The application can improve the navigation positioning precision and robustness in the multi-source fusion navigation process.

This invention discloses an attention-integrated multimodal federated learning method and system for risk monitoring. On the client side, an attention-integrated hybrid early fusion module dynamically injects multimodal features into globally shared latent variables through attention mechanisms and entropy-based gating mechanisms. On the server side, a plug-and-play dual-track federated aggregation strategy decouples state aggregation from model aggregation. The state track employs robust filtering based on the geometric median, while the model track supports algorithm-independent parameter updates. Between communication rounds, a neighborhood-weighted personalized reference mechanism constructs a gradient-based semantic topology to balance global consistency and local task preferences. This invention simultaneously addresses the technical challenges of modality incompleteness, data statistical heterogeneity, and Byzantine adversarial robustness in multimodal federated learning, thus providing an efficient and robust collaborative learning solution for disaster risk perception under privacy-preserving conditions.

The application particularly relates to a kind of aviation fuel regulating pump test equipment fault diagnosis methods based on parameter scheduling robust filter, by establishing linear variable parameter dynamic model, parameter scheduling robust filter is constructed, while guaranteeing system pole stable distribution, the dynamic balance of fault sensitivity and interference suppression is realized;Secondly, the theory of polytope LPV system is combined with convex optimization technology, the filter gain parameter matrix of global optimization is solved through finite-dimensional linear matrix inequality set, finally, the diagnostic framework with strong robustness is formed, and the fault recognition rate and isolation response speed under complex working conditions are improved.The application can effectively solve the diagnosis robustness of traditional method under parameter perturbation and system fault, and ensure the reliable operation of fuel regulating pump test equipment.

This invention relates to the field of traffic control systems, specifically to a checkpoint travel time estimation method based on mixed-integer optimization and spatial KNN, comprising the following steps: cleaning mapped checkpoint data, calculating initial spatiotemporal parameters, and removing anomalies based on absolute median; decomposing travel time and removing anomaly dwellers; dividing the road network according to traffic flow thresholds, estimating high-traffic road segments using mixed-integer optimization verification and Bayesian dynamic fusion, and performing parameter extrapolation based on spatial KNN topological constraints and congestion characteristics inverse solution for low-traffic road segments; fusing the results from dual-source road segments to construct a global parameter vector, and iteratively optimizing through an iterative convergence mechanism to output a high-precision global travel time field. This method utilizes absolute median to construct a robust filtering mechanism to remove extreme anomalies; it performs parameter extrapolation for low-traffic road segments, achieving high-precision estimation while ensuring physical interpretability, accurately reconstructing the dynamic operating state of the road network.

The invention relates to the field of traffic control systems, in particular to a bayonet travel time estimation method based on mixed integer optimization and space KNN, which comprises the following steps: cleaning mapping bayonet data, measuring and calculating initial space-time parameters, and eliminating anomalies based on absolute median difference; decomposing the travel time and stripping abnormal residence; road networks are divided according to flow threshold values, high-flow road sections are estimated through mixed integer optimization check and Bayesian dynamic fusion, and low-flow road sections are subjected to parameter deduction based on space KNN topological constraint and congestion feature reverse inverse solution; and fusing double-source road section results to construct a global parameter vector, and outputting a high-precision global travel time field through iterative convergence mechanism loop optimization. According to the method, an absolute median difference is utilized to construct a robust filtering mechanism to eliminate extreme anomalies; parameter deduction is implemented for a low-flow road section, high-precision estimation is realized on the basis of ensuring physical interpretability, and the dynamic operation state of a road network is precisely restored.

The invention discloses a two-stage compression denoising imaging method for a large-area array Geiger mode APD (avalanche photo diode). The method comprises the following steps: intercepting photon events in a pre-sampling time window at the front part of a data block to carry out global histogram statistics, fitting and estimating time domain characteristics of background noise based on Poisson distribution, screening and compressing full-frame data, and eliminating most noise; carrying out local joint histogram statistics and peak value extraction on the compressed data based on a pixel space neighborhood, and reconstructing a preliminary distance image and a preliminary intensity image; if the effective data in the neighborhood of the data meets the flat area condition, standard median filtering is executed; otherwise, starting a robust filtering algorithm based on absolute median difference MAD for processing. According to the method, the cooperation of front-end data efficient compression and rear-end accurate denoising and edge preserving is realized, and the method has the advantages of high compression ratio, high adaptability and excellent imaging quality, and is particularly suitable for real-time three-dimensional imaging of a large-area-array GM-APD laser radar system.

The application discloses an interactive multi-model state estimation method based on intention estimation, which is used to improve the estimation accuracy of a state estimator, reduce estimation errors and over-forecasting quantities when a tracked target makes a maneuver, and further adapt to higher-precision target tracking requirements. Although various filter variants or robust filters appearing after Kalman filters can compensate for the problems of inaccurate and nonlinear state equations to a certain extent, they are also difficult to meet the state estimation requirements in the field of fast tracking of maneuvering targets. The application proposes an interactive multi-model state estimation method based on intention estimation, which breaks through the limitations of traditional filtering methods, can estimate the motion intention of a tracked target and key parameters of a maneuvering equation when the tracked target makes a fast maneuver, significantly improves the estimation accuracy and estimation curve smoothness of an estimator, and optimizes the estimation effect of the estimator.

The application discloses a kind of based on multiple adaptive factors interactive robust state estimator design method, for improving the estimation accuracy of estimator and the curve smoothness of filtering under the condition that observation noise occurs unpredictable surge, to meet the filtering estimation demand of higher accuracy.Standard robust state estimation method requires known observation noise covariance value, therefore cannot be normally used under the condition that observation noise surges;Robust filtering method combined with single adaptive factor can compensate the estimation error caused by noise surge to a certain extent, but its performance cannot achieve universal optimization under different noise surge degrees.The application can effectively improve the estimation effect of state estimator under complex observation noise surge condition, break through the limitation of traditional robust state estimation method, realize fast estimation of actual observation noise under complex observation noise surge condition, effectively improve the estimation accuracy and estimation curve smoothness of state estimator, optimize the estimation effect of estimator.

The invention relates to an evolutionary nanorobot design method based on biological information feedback, and belongs to the crossing field of soft robots, biomedical engineering and evolutionary computation. The objective of the invention is to solve the problem that an early-stage tiny tumor cannot be automatically recognized and targeted due to the fact that a nano robot is complex in structure / function coupling, depends on tumor prior information and is difficult to dynamically adapt to a biological environment. A multi-objective evolutionary algorithm AFPO is adopted to optimize the structure behavior of the nano-robot, and minimization of parameters such as motion performance is taken as a target; generating a network CPPN coding genotype by using a composite mode, mapping the three-dimensional coordinates to material distribution, and generating a regular structure; a robust filter is designed, and individuals with stable performance are screened through Gaussian noise disturbance; constructing a filter to screen the design conforming to the manufacturing constraint; establishing a reaction flow-dilute substance coupling model based on the tumor microenvironment to generate a three-dimensional time-varying biological gradient field BGF; in the VoxCAD environment, the BGF is used for dynamically guiding the movement adjustment of the nano-robot, so that the autonomous targeting of the early tumor is realized.

The invention particularly relates to an aviation fuel adjusting pump test equipment fault diagnosis method based on a parameter scheduling robust filter, and the method comprises the steps: building a linear variable parameter dynamic model, constructing the parameter scheduling robust filter, and achieving the dynamic balance of fault sensitivity and interference suppression while guaranteeing the stable distribution of system poles; and secondly, combining a multi-cell LPV system theory with a convex optimization technology, solving a global optimized filter gain parameter matrix through a finite-dimensional linear matrix inequality group, and finally forming a diagnosis architecture with high robustness, thereby improving the fault recognition rate and isolation response speed under complex working conditions. According to the method, the diagnosis robustness of a traditional method during parameter perturbation and system faults can be effectively solved, and reliable operation of fuel adjusting pump test equipment is ensured.

The application relates to a micro-scale robot depositing system control method based on AFD time-frequency iteration learning, which comprises the following steps: S1, an iteration learning dynamics model establishment step; S2, an initialization step; S3, a tracking error adaptive Fourier decomposition AFD time-frequency distribution calculation step; S4, a feedforward signal adaptive Fourier decomposition time-frequency distribution calculation step; S5, a critical frequency determination step; S6, a learning filter amplitude-frequency characteristic calculation step; S7, a robust filter amplitude-frequency characteristic calculation step; S8, an iteration learning system input calculation step; S9, an iteration learning number judgment step; and S10, a tracking trajectory output step. The application combines the AFD-based time-frequency analysis method, the ILC method with an advanced phase and the L-Q filter frequency band width adjustment method, and realizes high-precision control of the micro-scale robot depositing system.

The invention relates to the technical field of high-precision positioning, in particular to a high-precision positioning method, system and device and a medium, and the method comprises the steps: obtaining the original observation data of a Beidou receiver; performing multipath error suppression processing on the original observation data to obtain target observation data; based on the target observation data, searching and fixing the integer ambiguity by using an intelligent algorithm to obtain an integer ambiguity fixed solution; and taking the integer ambiguity fixed solution as a known quantity, resolving the dynamic positioning model by using an adaptive robust filtering algorithm, and outputting a positioning result. The method has the beneficial effects that the positioning precision, the refresh rate and the reliability are integrally improved in a complex mountain area environment, tiny deformation of a dam body and a side slope can be captured in time, and the actual requirements of small hydropower station safety monitoring for high-precision and high-timeliness early warning are met.

The present application relates to the field of satellite navigation and positioning technology, and particularly relates to a Beidou robust Kalman filtering positioning method based on a non-Gaussian noise model. The present application precisely characterizes non-Gaussian noise generated in Beidou positioning due to shielding and multipath effects by constructing a mixed Gaussian model and a Student's t-distribution model, and updates model parameters online using an expectation maximization algorithm to solve the problem that a traditional Gaussian assumption model cannot adapt to noise statistical characteristics. The non-Gaussian noise model is then fused with a robust filter to construct an adaptive robust weight matrix through a Huber cost function, an IGGIII scheme or a noise probability density, dynamically adjust an observation covariance matrix and suppress the influence of abnormal observation values to overcome the defects of a classical robust filter, such as lack of noise modeling and failure of weight adjustment under continuous strong interference, while avoiding the problem of complex calculation of a particle filter.

The invention belongs to the field of precision motion control and intelligent control algorithms, and relates to a time-varying iterative learning feedforward control system and method based on wavelet transform. The system comprises an addition and subtraction arithmetic unit, a first memory, a time-varying robust filter Q (f, t) based on wavelet transform, a learning filter L (z), a first addition arithmetic unit, a second memory, a second addition arithmetic unit, a feedback controller C (z) and a controlled object P (z). According to the method, trajectory tracking error signals are repeatedly collected, the coherent power spectrum and the energy contribution distribution of the trajectory tracking error signals are extracted through wavelet transform, then a time-varying robust filter Q (f, t) is constructed, and repetitive error components are accurately extracted. The feed-forward control quantity is iteratively updated based on the repetitive error component, non-repetitive error amplification can be effectively inhibited, and the trajectory tracking precision is remarkably improved. The method is suitable for servo systems needing high-precision repetitive trajectory tracking, such as a photoetching machine workpiece table, a precision motion platform and an ultra-precision machining system.