1071 results about "Kalman filter algorithm" patented technology

The invention discloses an SOC (State of Charge) and SOH (State of Health) prediction method of an electric vehicle-mounted lithium iron phosphate battery, which comprises the following steps of: (a) improving a Thevenin cell equivalent model; (b) determining the state equation and the output equation of a system; (c) identifying battery model parameters; (d) using a Kalman filter algorithm to iterate the state variables of the system, so that the predictive value of SOC is closer to the actual value; and (e) using a dual-channel Kalman filter algorithm to carry out the online predication of an internal resistance and capacity of the lithium iron phosphate battery, and simultaneously predicating the SOH of the battery according to the changes in the internal resistance and the capacity value of the battery in the current state and the initial state. With the method, the predication precision of SOH of the battery is effectively improved, the decline in battery performance can be determined more accurately, and the internal resistance and capacity information of the battery is combined to provide a basis for making the battery management strategy and maintaining and replacing the battery.

A Micro Inertial Measurement Unit (IMU) which is based on MEMS accelerometers and gyro sensors is developed for real-time recognition of human hand motions, especially as used in the context of writing on a surface. Motion is recorded by a rate gyro and an accellerometer and communicated to a Bluetooth module, possibly to a computer which may be 20 to 30 feet or more from the sensor. The motion information generated and communicated is combined with appropriate filtering and transformation algorithms to facilitate a complete Digital Writing System that can be used to record handwriting on any surface, or on no surface at all. The overall size of an IMU can be less than 26 mm×20 mm×20 mm, and may include micro sensors, a processor, and wireless interface components. The Kalman filtering algorithm is preferably used to filter the noise of sensors to allow successful transformance of hand motions into recognizable and recordable English characters. The immediate advantage is the facilitation of a digital interface with both PC and mobile computing devices and perhaps to enable wireless sensing.

The invention discloses a high-precision vehicle positioning method for fusing multi-source information under a GPS (global positioning system) blind area and a device. By a strap-down matrix algorithm of the method, INS (inertial navigation system) vehicle position information and position information obtained by a pavement matching technology are calculated according to angular rate information output by a gyroscope and acceleration information output by an accelerometer and are fused by a kalman filtering algorithm to output final fused positioning information. As an INS positioning algorithm has an accumulated error, vehicle position information is recalibrated by arranging an anchor node beside a road. According to the method, stable and reliable high-precision vehicle position information can be obtained; the method is suitable for non-GPS-signal environments such as urban roads with dense buildings, mountain areas and tunnels.

The invention relates to an early warning method against vehicle collision based on an electronic map. The method comprises that 1) GPS data, which comprises movement station information and position information, of a vehicle is obtained, and the position information of the vehicle is converted by a coordinate system and mapped into a two-dimensional rectangular coordinate system; 2) according to historical GPS data of the vehicle, the present position information of the vehicle is optimized in a Kalman filtering algorithm to predict a first present position of the vehicle; 3) according to the historical GPS data of the vehicle, information of the electronic map is combined to predict a second present position of the vehicle; 4) the first present position and the second present position of the vehicle are combined to predict the optimal position of the vehicle; and 5) the predicted optimal position of the vehicle is combined with a TCC algorithm to make early warning against collision. Compared with the prior art, whether the vehicle possibly collides with vehicles in the surrounding is predicted in real time, early warning is given to a driver in real time, the driving safety and the vehicle positioning precision are effectively improved, and the method is practical and efficient.

The invention discloses an aero-engine remaining life prediction method based on multi-stage information fusion. The method comprises the steps that multi-source monitoring parameter denoising processing and feature extraction are conducted; stable analysis is conducted on multi-source monitoring time sequences, sudden change points of all parameter monitoring time sequences are calculated and parameter degeneration proportions at the positions of the sudden change points are calculated; multi-stage division is conducted on multi-source parameters, a regression fusion model is established, sample training is conducted by using historical monitoring data and parameters, in multiple stages, of the fusion model are obtained; according to monitoring data in a training set, the multi-source monitoring parameters are fused and a health indicator HI is obtained; by using the Kalman filtering algorithm, best fit is conducted on an engine in the whole process that the performance fails from being complete, and the error of a prediction model is minimized; according to real-time monitoring data in a test set, the multi-source monitoring parameters are fused and a health indicator HI is obtained; time-varying parameters of the prediction model are estimated in real time by using the Kalman filtering algorithm; the prediction model is determined, the time mechanism is introduced and the failure time of the engine is estimated in real time.

The invention provides an improved particle filter-based mobile robot positioning method. The improved particle filter-based mobile robot positioning method comprises the following steps: establishing a motion equation and a road sign calculation equation of a robot; optimizing a particle set by using a multi-agent particle swarm optimization algorithm, wherein the obtained optimal value is estimation of a pose; estimating an environmental road sign by using Kalman filtering algorithm; updating and normalizing the weight and resampling. The positioning method is accurate in positioning and easy to implement; the pose estimation and the environmental road sign estimation of the mobile robot are more accurate in a simulation process of the mobile robot.

The invention discloses a small satellite attitude determination system and a method thereof. The system comprises a plurality of attitude measuring units and a central processing unit; the central processing unit is used for collecting measurement data of the attitude measuring units, calculating environment model and selecting corresponding attitude determination algorithm according to the measurement data and the environment model, so as to determine the attitude. The central processing unit consists of a horizon sensor data sampling and processing unit, a solar sensor data sampling and processing unit, a gaussmeter data sampling and processing unit, an environment model calculating unit and an attitude determination selecting unit. The attitude determination selecting unit selects corresponding attitude determination algorithm according to data of the horizon sensor data sampling and processing unit, the solar sensor data sampling and processing unit, the gaussmeter data sampling and processing unit and the environment model calculating unit, so as to determine the attitude. The small satellite attitude determination system has low cost and simple structure; the small satellite attitude determination system also has various attitude determination algorithms, wherein, four fixed attitude determination algorithms and four Kalman filtering algorithms are designed; the algorithms can be effectively integrated and automatically alternated on the satellite to improve reliability of the system.

The invention discloses a location and environment modeling method of an intelligent movable robot, and the method comprises the steps of firstly forming correction iteration expanded Kalman filtering algorithm and determining a number of iterations, then establishing a movement model and an observation model of the movable robot, initializing the status of the movable robot, calculating a position jacobian matrix, controlling and inputting the jacobian matrix to calculate, observing the jacobian matrix and the like; and finally solving a Kalman gain matrix, updating a status estimation equation and a covariance matrix by resolving Kalman gain matrix, and repeating partial steps. The method is centralized on the expanded Kalman filter algorithm which is widely used in the simultaneous location and environment modeling field of the movable robot, and the algorithm is improved, so that the performance of the algorithm is greatly improved, and the algorithm can better meet the application in the SLAM (simultaneous location and mapping). The method also provides powerful technical support for the autonomous navigation and completion of complicated intelligent tasks of the movable robot in an unknown environment.

The invention discloses a lithium battery SOC online estimation method comprising the following steps that 1) the open-circuit voltage of a battery is measured, and the state-of-charge initial value of the battery is obtained according to an OCV-SOC curve; 2) the second-order RC equivalent model of the battery is established and the parameter initial value of the battery equivalent model is estimated; 3) the estimation program is started, and the matching coefficient initial value of a state equation is set according to the battery state-of-charge initial value and the parameter initial value of the battery equivalent model; 4) the current battery state-of-charge value is obtained by using an adaptive unscented Kalman filtering algorithm, and the current open-circuit voltage is obtained according to the OCV-SOC curve; 5) the least square method with the forgetting factor is started to identify the parameters of the current battery equivalent model, the matching coefficient of the state equation is updated by the identified parameters and the battery state-of-charge value of the next moment is solved; and 6) the steps 4) and 5) are repeated so that the battery state-of-charge value of each moment is obtained. Compared with the conventional unscented Kalman filtering algorithm, the method has higher accuracy and higher error convergence.

The invention relates to a method and system for estimating charge state of a power battery. The method includes the steps of selecting a Thevenin equivalent circuit model, exerting pulse current excitation to the power battery, collecting output voltage and current data of the power battery, obtaining a pulse current excitation response curve according to the relation of the output voltage and time, dividing the excitation response curve into an A section, a B section and a C section, obtaining a time constant through combination of a zero-input response expression of a resistance-capacitance return circuit and the least square method of the resistance-capacitance return circuit according to the A-section excitation response curve, according to the B-section excitation response curve, combining a zero-state response expression of the resistance-capacitance return circuit, substituting the time constant into the zero-state response expression, utilizing the least square method to obtain polarization resistance and polarization capacitance, obtaining ohm inner resistance by the utilization of the ohm law according to the C-section excitation response curve, and obtaining the estimated value of the charge state of the power battery by the utilization of the kalman filtering algorithm according to the polarization resistance, polarization capacitance and the ohm law. Therefore, the method can be used for recognizing parameters of the Thevenin equivalent circuit model accurately.

The invention relates to a mobile phone signaling and road network big data fusion method, application and a system thereof. The fusion method includes: extracting original signaling data from a signaling interface of an operator for screening, cleaning and denoising to select out signal data meeting road network operation monitoring needs; performing road network operation state analysis on the signaling data; performing data exchange with other areas of a road network, performing data exchange with other traffic data input systems and issuing systems, and fusing multidata through multisourcefusion, wherein a multidata fusion model is acquired through a Kalman filtering algorithm. The application and the system are put forward on the basis of data fusion. Providing of accurate real-timeroad network data for road network operation monitoring and predicting, providing of technical support for major emergency prewarning and handling, providing of authoritative information for travelingservice and providing of data sharing and intelligent analysis for traffic decision making are realized on the basis of fusing mobile phone signaling and traffic big data.

The invention discloses a device and a method for fusing sensor information. The sensor-information fusion method comprises the steps of adopting a self-adaptive weighted-data fusion algorithm to allocate corresponding weight numbers for induction information from a plurality of sensors, adopting a Kalman filtering algorithm to perform optimal estimation to the induction information of the corresponding weight numbers so as to acquire the locally fused induction information, adopting a D-S theory estimation algorithm to perform interval estimation to the uncertain induction information in the locally fused induction information, adopting a multi-Bayesian estimation algorithm to perform individual association probability distribution for the induction information after interval estimation so as to synthesize a joint posteriori probability distribution function, and utilizing the joint distribution function to output a final fusion value of the induction information. The invention has the advantages of improving the reliability, confidence and efficiency of sensor information fusion.

The invention discloses an AGV composite guiding system based on an image and inertia technology. The system comprises an inertia guiding module, which can intelligently sense the positions and moving information of AGV in a vehicle coordinate system in every moment through a plurality of inertia sensors; a visual guiding module, which can intelligently sense the position and environment information of AGV through a visual sensor, when AGV move to a preset position in a reference coordinate system; an information interaction module, which efficiently passing information among the inertia guiding module, the visual guiding module, and a movement control module, and the movement control module, wherein the movement control module obtains the data of the sensors of the inertia guiding module and the visual guiding module, then the data of each sensor are fused by a Sage-Husa self-adaption Kalman filtering algorithm, according to the obtained fused data, AGV is controlled, and the accumulated errors of the inertia guiding module are corrected. Based on visual guiding and inertia guiding technologies, multiple information sources supplement each other, and an AGV composite guiding system, which has redundancy and higher navigation accuracy, is constructed.

The invention relates to an intelligent linkage and tracking method based on a panorama camera and a high speed ball-head camera and relates to the machine vision field. A camera parameter model and a characteristic point matching method are utilized to acquire a mapping relationship between a panorama image coordinate and a rotation angle of the high speed ball-head camera, and intelligent linkage of the panorama camera and the high speed ball-head camera is accomplished through the mapping relationship; for tracking of a panorama end, motion target detection is carried out through utilizing a frame difference method and a Surendra adaptive background update method, multi-target tracking of detected motion targets is carried out through a Kalman filtering algorithm and establishing a target matching matrix, and motion locus of the targets is acquired; an interested target is selected through a mouse, a high-speed ball is made through an intelligent linkage method to rapidly turn to a target region, the target is locked, and motion target detection is carried out through Kalman filtering and a Mean Shift algorithm. The method is advantaged in that global and local integrated monitoring, multi-target tracking and high definition snapshot are realized.

The invention discloses a dead reckoning-based low-cost Big Dipper and MEMS tight-coupling positioning system and method. Based on the acceleration, the magnetometer and the angular velocity information of an MEMS, the PDR algorithm is implemented. The acquired PDR position and velocity information is combined with the Ephemeris information of a Big Dipper receiver, and then the pseudo range and the pseudo-range rate value of a PDR terminal can be estimated. After that, the above pseudo range and the above pseudo-range rate value are compared with the pseudo range and the pseudo-range rate value that are outputted by the Big Dipper receiver, and the difference values therebetween are adopted as the observed values of a navigation filter. The optimal estimation on the error amount of a combination system is conducted by the filter, and then error-corrected data are obtained. According to the technical scheme of the invention, in an environment wherein a target is moved in such a manner that the signal of the target is decreased significantly or the target has no satellite signal, the target can still be continuously positioned. The invention also provides a federal kalman filter algorithm. The time accumulative error of a traditional inertial navigation system is inhibited, and the stability of the overall system is improved. At the same time, based on the adaptive adjustment algorithm, the soft handoff of the system can be realized in different environments, so that the seamless transition of the positioning effect is realized.

The invention relates to a pilotless automobile combination navigation method based on vision screening. The method comprises the following steps: carrying out the conversion for a coordinate system; recognizing a building occlusion angle; judging availability of a satellite signal in a non-line-of-sight environment; implementing an improved self-adaptive square-root volume kalman filtering algorithm. A combination navigation algorithm adopting vision information as a screening condition is provided, a concept of removing the non-line-of-sight transmission of satellite data is introduced, a judgment method is also provided, so that the GPS satellite data information with lower precision caused by the blocking of a building can be eliminated; different from the traditional combination method adopting the vision navigation to process the data in parallel in real time, the vision information is used for screening the GPS data on purpose, so that the dimensional disaster caused by adopting the vision navigation in the traditional method is avoided; the improved self-adaptive square-root volume kalman filter algorithm is provided, and the strong nonlinear problem of the navigation data when a pilotless intelligent car runs on urban roads is considered.

The invention discloses a traffic flow three parameter real time prediction method taking regard of space-time correlation. According to the method, on the basis of acquiring traffic flow rate, speed and occupancy data of a target section and upstream and downstream sections of the target section, a state space model for multivariable short time prediction of traffic flow three parameters is established; according to spatial correlation of various traffic variables at different data acquisition sections, an observation equation of the state space model is established; according to time autocorrelation and cross correlation of the multiple traffic variables at one same data acquisition section, a state equation of the state space model is established; prediction and iteration update of the traffic flow three parameters are realized by employing the Kalman filtering algorithm. The method makes full use of the spatial correlation of the traffic flow three parameters at the different data acquisition sections, the time autocorrelation and the cross correlation of the different traffic variables at one same data acquisition section, the multivariable prediction algorithm is employed, and thereby traffic flow short time prediction accuracy is facilitated.

The invention discloses an SOC estimation method based on an adaptive double extended Kalman filtering method. The SOC estimation method comprises the following steps: firstly, establishing a second-order RC equivalent circuit model of a lithium battery; then, determining open-circuit voltage and battery equivalent model parameters at different SOC (state of charge) positions of the lithium battery through a pulse charging and discharging experiment; obtaining a function relationship between the open-circuit voltage and the SOC and relationships between other model parameters and different SOCs, the other model parameters including ohmic internal resistance, electrochemical polarization resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance values; establishing a state space equation taking the SOC and polarization voltage as state variables and a state space equation taking the ohmicinternal resistance as a state variable; and finally, performing iterative computation to obtain the SOC value of the lithium battery in real time. According to the method, the problem of unknown noise statistical characteristics in the prior art is solved, and meanwhile, the ohmic internal resistance of the battery is estimated by using the Kalman filtering algorithm, so that the model precisionis improved.

The invention discloses a high-altitude parabolic detection method and system based on computer vision. a ViBe algorithm is used as a detection means of a moving object; in the process of detecting the moving object by using the ViBe; A mechanism for detecting whether the camera shakes or not is added, the situation that the image position shifts due to shaking or moving of the camera is fully considered, a background model is updated by adopting a current frame of high-altitude parabolic monitoring image when the camera shakes, the accuracy of detecting a moving object through ViBe is improved, and therefore the false detection rate of high-altitude parabolic is reduced. The moving object is tracked by combining the Hungarian algorithm and the Kalman filtering algorithm, the running speedof the system is greatly increased through Hungarian algorithm tracking, the loss rate of parabolic tracking is greatly reduced through Kalman filtering algorithm tracking, and the parabolic trackingeffect is guaranteed. The method has a good detection effect on a moving object of a small target, and can adapt to the situations that the target is temporarily shielded or missed, the background environment slowly changes and the like.

The invention discloses a nonlinear system state estimation method based on Kalman filtering positioning, and proposes a strongly adaptive Kalman filtering mechanism which combines a nonlinear filtering algorithm and Kalman filtering. The method comprises the steps: carrying out the simultaneous estimation of node positions and channel parameters through employing an RSSI state estimation algorithm based on square root volume Kalman filtering, and obtaining the estimation value of a state vector; employing the Kalman filtering for further processing according to the linear change of a state equation, obtaining optimal estimation, and building a strongly adaptive square root volume Kalman filtering algorithm; giving the design steps of the strongly adaptive square root volume Kalman filtering algorithm; and calculating a theoretical square root error lower bound under a state space model based on the RSSI state estimation. The method enables an estimation result to be improved, and improves the precision. The method does not need to excessively depend on improper initial conditions, can be well adapted to a highly nonlinear system, and is not liable to enable the algorithm to be divergent and ineffective.

The invention discloses a method for estimating a lithium battery charge state. The method comprises steps of estimating the charge state in accordance with the system state initial value and the state equation; calculating the residual error of the measured value and the estimated value and then calculating the fading factor; calculating the time-varying fading factor; calculating and obtaining the adjusted value of the fading factor in accordance with the residual error change and the current of the actual station; obtaining the new value of the fading factor, and obtaining the gain matrix; updating the charge estimation state; and estimating and measuring the noise covariance matrix through the self-adapting filtering algorithm. Compared with the prior art, by the aid of the strong tracking filter algorithm and the modification of the strong tracking filter algorithm, the noise and the fading factor can be measured and updated in accordance with actual condition, the estimation accuracy of the lithium battery charge state can be improved effectively compared with the traditional Kalman filter algorithm, and the tracking and the self adaption of the algorithm can be improved.

The invention discloses a time division-synchronization code division multiple access (TD-SCDMA) system-based method for accurately positioning underground personnel. The method comprises the following steps of acquiring time of arrival (TOA) and an angle of arrival (AOA) of a signal at a base station; de-noising a TOA value by using an improved kalman filtering algorithm; calculating a time difference of arrival (TDOA) value according to the de-noised TOA value; estimating the position of a mobile station by using a TDOA/AOA mixed Chan algorithm and a TDOA/AOA mixed Taylor algorithm; performing first data fusion on a position estimated value by using a weighted residual method to obtain a new position estimated value; and performing second data fusion on the position estimated value by using Bayesian inference to obtain the final position estimated value. By the method for accurately positioning the underground personnel, the advantages of a TD-SCDMA system and the superiority of the data fusion are utilized, a TDOA/AOA mixed data fusion positioning algorithm is adopted, the positioning accuracy is high, and the problem that the personnel in an underground coal mine are hard to position is solved.

The invention relates to a panorama video adaptive transmission method based on DASH (Dynamic Adaptive Streaming over HTTP) and belongs to the technical field of panorama video transmission. The method comprises the steps that a mapping relationship model of a three-dimensional panorama video and a two-dimensional plane panorama video is established; region priority division is carried out on thepanorama video based on vision and motion characteristics of a human body; a server slices the panorama video; a client bandwidth estimation module carries out available bandwidth prediction through utilization of a Kalman filtering algorithm; a client video buffer module smoothens available bandwidths based on a buffer area state; a client user window sensing module predicts a user window based on motion inertia; and a client decision-making module adaptively transmits the panorama video by taking a network environment, a buffer area state and the user window into comprehensive consideration.Compared with a traditional video transmission method, the method has the advantages that the network environment, the buffer area state and the user window are taken into comprehensive consideration, and the user quality of experience (QoE) is improved.