41 results about "Kalman estimation" patented technology

The invention provides an indoor WLAN/MEMS fusion cross-stair three-dimensional positioning method. The indoor WLAN/MEMS fusion cross-stair three-dimensional positioning method is mainly characterized in that a WLAN/MEMS fusion positioning robust extended Kalman filter is designed, available information of two systems is fully utilized, and resolution of a high-precision two-dimensional position is realized; a height is calculated by using output of a barometer, a geographic position and stair height information, and a position fingerprint database is selected for positioning a wireless local area network (WLAN), and indoor space cross-stair three-dimensional positioning is realized. The system and method are high in positioning precision by introducing robust extended Kalman estimation, and can be used for effectively overcoming an accumulative error existing in positioning of a micro electro mechanical system (MEMS) sensor and received signal strength (position information and actual height information of the previous time) in WLAN positioning to realize indoor space cross-stair three-dimensional positioning.

The invention belongs to the field of image processing, and discloses a space-time correlated channel massive MIMO transmission method. According to the space-time correlated channel massive MIMO transmission method, a time-shifted pilot frequency system structure is adopted, a user selection scheme based on the position of a user is utilized, user sub-groups having large interference in surrounding cells are omitted, so that data interference of the adjacent cells is reduced in a channel estimation stage, meanwhile, interference among the cells is reduced in a downlink data transmission stage, and system capacity is promoted. On the basis of the user selection scheme, a Kalman estimation method is adopted, a space-time correlation between channels is used, residual interference among the cells is eliminated further, and channel estimation accuracy is promoted. By means of combination of a user selection process and Kalman channel estimation, more accurate channel estimation results are obtained under the space-time correlated channel of a multi-cell massive MIMO system, interference among the cells in a pilot frequency estimation stage is restrained, and meanwhile the throughput rate of downlink data of the system is increased.

The invention discloses a method for adjusting pressure of an anesthesia machine and a breathing machine through flow and pressure common control, which is characterized by comprising the following steps of: calculating a pressure value under the current flow by using a formula P=F*R in a pressure control mode, wherein P is pressure; F is the current flow; and R is air resistance; estimating the pressure at a next moment under the current pressure value by using a Kalman estimation algorithm into which sub-items with control parameters influencing an overall estimation result are added; substituting the estimated pressure value into an error system to calculate a control error quantity, performing proportion integration differentiation (PID) control on the control error quantity, and converting into control pressure required at the next moment; converting the control pressure into standard control flow by using the formula P=F*R; and inputting the converted control flow into a flow PID ratio controller for flow feedback control.

The invention relates to a self-localization method of an intelligent wheelchair in corner areas. The wheelchair comprises a camera, sonar sensors, a first Kalman filter, and a second Kalman filter. Environmental information is collected by the sensors. The information is processed through two times of Kalman estimation, such that corresponding weight values of the sensors are obtained. Then a corner position is monitored, and an accurate distance between the wheelchair and the corner is obtained through calculation, such that self-localization is achieved.

The invention relates to a self-adaption Kalman estimation and fusion method of the moisture content of two-phase flow. The method includes the steps of calculating measurement values of sensors capable of directly reflecting the moisture content to serve as the judgment standard of the sudden change of the moisture content; setting a threshold value to be compared with judgment parameters of the sudden change of the moisture content to judge whether the moisture content is suddenly changed or not so as to switch the working states of Kalman estimation, wherein the working states of the Kalman estimation include the stable state and the sudden change state; conducting Kalman estimation updating on each sensor; correcting the estimation value according to measurement information; conducting normalization calculation on self-adaption weights by means of different algorithms in the stable state and the sudden change state; calculating the fused moisture content. By means of the method, the estimation precision of the moisture content can be improved.

The invention relates to a centralized two-stage Kalman estimation method with related measurement noise. Aimied at the filtering problem of a multi-sensor measurement system with related measurementnoise influencing a measurement value, a measurement equation with measurement noise irrelevance is re-established by introducing a decorrelation technology, and an optimal estimation value of a system state is obtained through a two-stage Kalman filter. Compared with a two-stage Kalman method directly used, the method provided by the invention has the advantages that although the data fusion results are the same, the calculation complexity is greatly reduced.

The invention relates to a monitoring method of a value R and a value C of an anesthesia apparatus and a respirator. The method comprises the following steps of in the respiration stage of each respiration cycle of a patient, collecting the values of air supply volume V, pressure P and flow speed F of the time in a real-time way at certain time intervals, and transmitting the collected data to a CPU (central processing unit) of a controller; according to the collected values of volume V (t), pressure P (t) and flow speed F (t) at certain time t, and the collected values of volume V (t+1), pressure P (t+1) and flow speed F (t+1) at the next time (t+1), calculating approximate values of the value R and the value C through the CPU of the controller, transmitting the calculated approximate values of the value R and the value C into a Kalman estimation controller to estimate, and classifying and storing the estimating results into a storage unit of a controller according to the values; and after the respiration stage of the cycle is finished, solving the normal distribution of a plurality of stored value R and value C samples, and respectively calculating the average values of the value R and the value C with widest normal distribution as the results of the value R and the value C.

The invention belongs to the field of signal processing, and provides a self-adaptive clock synchronization method with high robustness for defects of the existing Kalman estimation clock synchronization method. The method provided by the invention comprises the steps that the topology model of a wireless sensor network is established; network nodes are divided into multiple levels; a subsequent clock synchronization method is carried out only between adjacent levels; progressive synchronization ultimately develops into global clock synchronization; a network node clock model and a transport model between nodes are established; an H infinity estimation filter is used to carry out relative clock parameter estimation on the nodes of adjacent levels; clock synchronization of adjacent levels is realized; and global clock synchronization is realized. According to the invention, H infinity estimation has the advantage of high robustness; and an H infinity clock synchronization method does not need the priori information of clock model noise, a transmission delay autocorrelation matrix and the like.

The invention discloses a blood oxygen content estimation method based on binary sensor Kalman fusion. The method comprises the following steps of: establishing a blood oxygen content dynamic physiological model and a binary measurement model, analyzing effective information in binary sensor measurement according to a blood oxygen content model, and obtaining a practical measurement model; designing a robust local Kalman estimator, giving a recursive computation process of an estimation error covariance upper bound, and through the minimization of the estimation error covariance upper bound, solving an optimal local estimation gain; and designing a distributed fusion Kalman filter used for blood oxygen content estimation, adopting a covariance insertion fusion criterion, and solving an optimal problem to obtain an optimal fusion weight matrix. The invention provides the blood oxygen content distributed fusion Kalman estimation method based on the binary sensor, and realizes the real-time non-invasive estimation of the blood oxygen content.

The invention discloses a vehicle state estimation method under the condition of abnormal measurement data of a vehicle-mounted sensor, which specifically comprises the following steps of: acquiring longitudinal acceleration, transverse acceleration, yaw velocity and front wheel rotation angle signals of a vehicle , combining with a nonlinear vehicle model, estimating axial force information of the vehicle by using strong tracking unscented Kalman filtering, estimating a tire road adhesion coefficient by utilizing interactive multi-model unscented Kalman based on the axial force information of the vehicle; wherein the vehicle axial force information includes longitudinal and lateral forces of a front axle of the vehicle and longitudinal and lateral forces of a rear axle of the vehicle. Through interaction, mixing, prediction and fusion, the invention provides an estimation method which can combine the advantages of a plurality of models to realize accurate estimation of the tire road adhesion coefficient under a complex driving condition, then updates a posteriori state and a covariance matrix P eta thereof, and adopts a prior and posteriori combined estimation method the technical blank that the axial force of the vehicle cannot be accurately estimated under the current mass mismatching condition can be filled.

The invention relates to the technical field of medical health, in particular to a heart rate calculation method based on heart sound. The method comprises the following steps of: acquiring real-timeheart sound signal and processing the waveform of the heart sound signal to form a calculable characteristic waveform; detecting the time interval T between adjacent two peaks and calculating an instantaneous heart rate value; calculating arithmetic mean values of the instantaneous heart rate values in turn according to each successive five beats; utilizing a system estimation method to process the arithmetic average values to obtain corresponding estimation values; carrying out anti-misjudgment processing on the calculated values and the estimated values. According to the calculation method,under the premise of accurate detecting the heart rates, the calculation amount is small, the complexity is low and the real-time property is good, and the calculation method is suitable for calculation on the basis of an embedded chip, so that the development cost of a enterprise product can be reduced; Kalman estimation equation is used in the data processing, so that the final result cannot beinfluenced by the signal-to-noise ratio and the human and environmental interference, and meanwhile, stable and reliable heart rate results can be obtained.

The invention provides an electronic throttle valve opening degree estimation method and an electronic throttle valve opening degree estimation system based on Kalman-like filtering. The electronic throttle valve opening degree estimation method comprises the steps of: acquiring electronic throttle valve opening degree data acquired by means of an electronic throttle valve position sensor; and processing the opening degree data, measured by means of the position sensor, of an electronic throttle valve by adopting a Kalman-like estimation algorithm to obtain a filtered opening degree of the electronic throttle valve, and feeding the filtered opening degree value of the electronic throttle valve back to a vehicle-mounted controller for further controlling the opening degree of the electronicthrottle valve according to the filtered opening degree value. By adopting the Kalman-like estimation algorithm for filtering an output signal of the electronic throttle valve position sensor, interference of non-Gaussian white noise on a measurement result is reduced, and an estimation strategy is high in estimation precision and high in robustness.

The invention discloses a self-adaptive fuzzy Kalman estimation SOC algorithm. The method comprises the following steps: S1, establishing an equivalent circuit model of a battery, establishing a state-space equation and an observation equation by applying an extended Kalman algorithm, and estimating a short-time polarization end voltage variable Vst, a medium-time polarization end voltage variableVmt, a long-time polarization end voltage variable Vlt and a battery state-of-charge SOC variable; S2, under the condition that different SOCs are matched with the temperature T, setting equivalent internal resistance, polarization capacitance and polarization resistance of an equivalent circuit model in the charging and discharging process of the battery through a battery characteristic experiment; S3, realizing Kalman prediction and updating, and estimating the SOC value in each sampling period in real time; S4, calculating a corrected ampere-hour integral factor of the platform period by applying the EKF and the ampere-hour integral in the OCV-SOC non-platform period; and verifying the corrected ampere-hour integral of the platform period by applying the EKF again when the platform period is ended, introducing fuzzy control to perform error correction on a platform period correction factor, and finally applying the correction factor to the ampere-hour integral of a new round of non-platform period correction algorithm. The method has the advantages that the estimation precision and the algorithm debugging time of the algorithm are improved, and the precision of the extended Kalman filter can meet corresponding requirements by defining parameters in the automatic adjustment method.

The present application discloses UWB (Ultra-Wide Band) fine positioning method, device and equipment and a computer-readable storage medium. The method comprises the steps of balancing an optimal observation solution and an estimated solution through Kalman filtering by fully utilizing motion information to obtain a Kalman estimated solution; and compensating error caused by modeling and uncertain noise, of the Kalman filtering, through a BP (Back Propagation) neural network at the same time to obtain an optimal final estimated solution. The method has the advantages that the positioning result is more accurate and the technical problem that large error exists in a conventional UWB precise positioning method is solved.

The invention discloses a four-wheel independent drive electric vehicle side slip angle and tire lateral force estimation method which comprises the following steps: calculating tire longitudinal force according to a wheel kinetic equation; according to a longitudinal dynamic balance equation of the vehicle, estimating the mass of the whole vehicle based on a least square method with a forgetting factor; the robust volume Kalman estimation module is used for establishing a four-wheel drive electric vehicle dynamic model comprising three degrees of freedom of vehicle longitudinal, lateral and yawing and a semi-experience magic tire model for reflecting instantaneous mechanical characteristics of tires; and estimating the side slip angle and the tire lateral force based on the established robust volume Kalman filtering module. The method effectively improves the perturbation of filtering to model parameters and the anti-interference capability of unmodeled noise under the complex working condition, improves the accuracy, robustness and anti-interference performance of a joint estimation algorithm under different working conditions, and solves the problem of joint estimation of the side slip angle and the tire lateral force of the four-wheel-drive electric vehicle under the composite working condition.

The invention discloses a measurement method for the affection of noise variances on SOC (State of Charge) filtering effect, which includes the following steps: under the condition that P<0> and SOC<0> are not changed, a noise variance is adjusted and measured in Matlab/Simulink; under different measured noise variance R values, the difference of UKF (Unscented Kalman Filter) filtering effects of a lithium battery pack is great, and as the R value is increased, the filtering error is remarkably reduced; according to the measurement, during the Kalman estimation of lithium battery SOC, appropriate SOC<0> and P<0> are chosen. The measurement method for the affection of noise variances on SOC filtering effect can overcome the defects in the prior art, such as short service life, poor safety and low reliability, thus achieving the advantages of long service life, high safety and high reliability.

The invention provides a ship shaft power measurement method based on Monte Carlo Kalman filtering. The method comprises the specific process: abstracting a physical model of a shaft system according to the working principle of the shaft system, building a state equation and a measurement equation, and employing Monte Carlo Kalman filtering. The method comprises the following steps: firstly, simulating a random point set of state torque by adopting Monte Carlo according to a state estimation value and covariance at a previous moment, carrying out nonlinear transformation on the random point set according to a state equation, and calculating a state prediction value at the moment; secondly, simulating a random point set for the measured value of the rotating speed by adopting Monte Carlo, and fusing and calculating a predicted value of the shaft power according to a measurement equation; and finally, performing Kalman estimation on a torque state estimation value at the moment, and calculating the ship shaft power. Aiming at the defects of contact type and non-contact ship shaft power measurement, the invention aims to adopt a Monte Carlo Kalman filtering algorithm to carry out fusion filtering and noise covariance estimation on acquired data on the premise of not increasing hardware, so that the measurement precision is improved, and the purpose of monitoring the shaft power on line is achieved.