6 results about "Noise weighting" patented technology

The invention relates to the technical field of wearable device mutual-seeking positioning, in particular to a wearable device mutual-seeking positioning method and system based on an entry and exit principle, and the method comprises the steps: setting a base station, obtaining angle and distance information through a multi-antenna array, and carrying out the time slot positioning; constructing a shielding model by utilizing the equipment attitude information, and identifying a sight distance communication and signal shielding blind area; the Kalman filtering noise weight is adjusted by adopting a dynamic sector confidence gating strategy, a relative pose vector is solved in combination with geometric constraints, and a guide instruction is generated; in the near-field verification stage, a coding vibration sequence is transmitted, time domain wave packet cross correlation is calculated to confirm physical contact, and automatic search quitting is achieved. According to the invention, through a shielding model and a dynamic gating strategy, positioning drift caused by shielding is effectively inhibited, and the guiding robustness in a complex environment is improved; by means of coding vibration and a cross-correlation verification mechanism, misjudgment and real contact of the partition wall are accurately distinguished, and intelligent closed loop of guiding while entering and returning while contacting is achieved.

The invention discloses a sparse Bayesian out-of-grid orientation estimation method under non-Gaussian noise, which comprises the following steps of: constructing a hierarchical noise prior model based on Student-t distribution, a double-layer sparse signal prior model based on a binary indication variable and the like, and inferring approximate posteriori distribution of iteratively updating parameters by using Bayesian variation; the noise precision weight is adaptively adjusted according to the reconstructed residual energy so as to suppress impulse noise, and random pseudo peaks are effectively removed through a binary indication variable judgment mechanism. And finally, adaptively correcting the grid offset by using the residual gradient information subjected to anti-noise weighting so as to eliminate the off-grid effect. Compared with the traditional method, the method provided by the invention not only can carry out DOA estimation under non-Gaussian noise, but also has higher DOA estimation precision. The method disclosed by the invention still has relatively high performance under the conditions of few snapshots and low signal-to-noise ratio, so that the method has relatively high application value in actual engineering.

The invention discloses a Beidou-based high-precision differential positioning adjustment system and method, and relates to the technical field of satellite navigation and high-precision localization. Time-frequency analysis is carried out on a multi-frequency carrier phase observation sequence, signal-to-noise ratio monitoring is combined, and a flicker identifier and a noise weight are constructed; the system can identify frequency points and observation sections which are seriously polluted by flicker at an epoch level, and the weight of the system is actively reduced at a combination stage; secondly, under the constraint of maintaining ambiguity integer characteristics, the coefficient combination takes equivalent noise variance and residual ionosphere high-order term error sensitivity as optimization targets, and considers the observation noise level and the suppression capability for high-order ionosphere residual errors, so that on one hand, the reliability of carrier phase integer ambiguity calculation is maintained, and on the other hand, the reliability of carrier phase integer ambiguity calculation is improved; and on the other hand, the sensitivity degree of combined observation to high-order ionosphere residual errors is reduced, so that high-precision differential positioning of a low-latitude region has higher robustness.

This invention discloses an intelligent drill bit attitude segmented adaptive filtering method, system, and storage medium. The intelligent drill bit attitude segmented adaptive filtering method includes: acquiring multi-source measurement signals from the drill bit and performing preprocessing and dynamic-static separation; extracting features to construct discrimination indices, dividing the working conditions into drilling start-up impact, continuous cutting, and idling separation; calculating acceleration reliability coefficients; establishing a full-state filtering model including attitude and zero bias for attitude prediction and temperature drift compensation; matching measurement update strategies according to identified working conditions; adaptively adjusting the noise covariance matrix based on the reliability coefficients; performing smoothing processing on the preliminary results; and outputting the final attitude parameters. This invention can dynamically switch filtering strategies and adaptively adjust noise weights according to changing working conditions, isolate strong impact interference, overcome zero bias drift caused by high temperatures, and improve the accuracy and robustness of attitude calculation in complex drilling environments.

The present application relates to wearable device mutual search positioning technical field, specifically wearable device mutual search positioning method and system based on approach and retreat principle, including setting base station utilizes multi antenna array to obtain angle and distance information, time slot positioning is carried out simultaneously;Utilize device posture information to construct shielding model, identify line-of-sight communication and signal shielding blind area;Adopt dynamic sector confidence gating strategy to adjust Kalman filter noise weight, combine geometric constraint to solve relative pose vector and generate guide instruction;In near field verification stage, transmit coded vibration sequence, calculate time domain wave packet cross correlation to confirm physical contact, realize automatic exit search.The present application effectively suppresses the positioning drift caused by shielding through shielding model and dynamic gating strategy, improves the guidance robustness in complex environment;Utilize coded vibration and cross correlation verification mechanism, accurately distinguish partition wall misjudgment and real contact, realize intelligent closed loop of approach and guidance, contact and retreat.

The invention belongs to the technical field of power load prediction, and discloses a power load prediction model training method and a related device. The power load prediction model training method comprises the following steps: performing signal decomposition on a historical power load sequence by using ICEEMDAN to obtain a plurality of subsequences; during signal decomposition, the noise weight and the decomposition frequency of the ICEEMDAN adopt an optimal parameter combination (Nstd *, NE *) found by the IHHO; splicing the plurality of obtained subsequences and the plurality of selected exogenous variables in sequence in a time dimension to construct a training sample; and the iTransform model is trained, and a trained power load prediction model is obtained. According to the technical scheme, the technical problems that in the prior art, ICEEMDAN parameters are set depending on experience, the adaptive capacity is insufficient, the prediction model structure is complex, the training cost is high, the decomposition and modeling process lacks a collaborative optimization mechanism, and the external feature interference robustness is poor are solved.