12 results about "Robust filter" patented technology

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.

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 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 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.