240 results about "Extended kalman filter algorithm" patented technology

A vehicle operating state estimation method based on improved extended Kalman filter includes using the improved extended Kalman filter algorithm for properly modeling to acquire operating state information such as longitudinal forward speed, yaw velocity, lateral speed, side slip angle and the like of a vehicle in a higher maneuvering operating state, wherein the information can be used for relevant control of vehicle active safety. The vehicle operating state estimation method based on improved extended Kalman filter has the advantages of high precision, low cost, high instantaneity and the like.

The invention belongs to the crossing field of computer vision and intelligent robot, and discloses a new multi-feature multi-sensor method for a mobile robot to track a moving body. The method comprises the following steps: 1, coarsely positioning a body carrying a passive tag around a radio frequency identification (RFID) system by using the RFID system; 2, initially positioning the body in an image by using an adaptive template matching algorithm based on head and shoulder features; 3, accurately positioning the body in the image by using a multi-feature-based mean-shift algorithm; 4, predicting the moving state of the body by using a extended Kalman filter algorithm; 5, screening the acquired target position information by using a double-layer collaboration positioning mechanism; and 6, controlling the robot to move along with the body by using a robot following control algorithm. By the method, bodies with different poses can be tracked, the problem that the tracking is influenced when a target suddenly turns and is shielded is solved, and the robot can accurately, stably and continuously track the moving body.

The invention discloses a robot distributed type representation intelligent semantic map establishment method which comprises the steps of firstly, traversing an indoor environment by a robot, and respectively positioning the robot and an artificial landmark with a quick identification code by a visual positioning method based on an extended kalman filtering algorithm and a radio frequency identification system based on a boundary virtual label algorithm, and constructing a measuring layer; then optimizing coordinates of a sampling point by a least square method, classifying positioning results by an adaptive spectral clustering method, and constructing a topological layer; and finally, updating the semantic property of a map according to QR code semantic information quickly identified by a camera, and constructing a semantic layer. When a state of an object in the indoor environment is detected, due to the adoption of the artificial landmark with a QR code, the efficiency of semantic map establishing is greatly improved, and the establishing difficulty is reduced; meanwhile, with the adoption of a method combining the QR code and an RFID technology, the precision of robot positioning and the map establishing reliability are improved.

The invention discloses an attitude angle calculating and positioning method and a fusion sensor thereof. The fusion sensor comprises a plurality of IMU sensors, a magnetometer and a GPS. The method comprises the following steps that the magnetometer, an accelerometer and gyro are calibrated; measurement data of accelerometer and gyro are fused by a redundant information fusion algorithm correspondingly to obtain acceleration information and angular velocity information; an extended kalman filtering algorithm is used for fusing the acceleration information, angular velocity information and magnetic field information of the magnetometer to obtain a fusion attitude angle; and position information is obtained by fusion attitude angle auxiliary positioning information. Due to the fact that data measurement and data fusion calculation are conducted by using multiple redundant IMU sensors and the fusion attitude angle is obtain by using the extended kalman filtering algorithm to fuse the data, the high-accurate attitude angle can be obtained under both static and dynamic conditions. The fusion attitude angle is used for assisting the GPS to obtain corrected position information, and therefore position information is more accurate, and stability and reliability of attitude angle calculating and positioning are improved.

The invention discloses a battery charge state detecting method. The method comprises the steps that the initial SOC of a battery is determined; the self-discharge effect of the battery is considered; a table look-up method is used to accurately estimate the SOC of the battery; an ampere-hour integral method is used to acquire the SOC estimation value of the battery of a next moment; a battery Thevenin model is combined, and an extended Kalman filter algorithm is used to correct the SOC of the battery; and finally, low power, over-current, over-temperature and the like of the battery are judged to carry out early warning and protection. According to the method provided by the invention, the influence of self-discharge of the battery on the battery capacity and the influence of temperature, the number of times of cycle charge and discharge and charge and discharge current on the rated capacity of battery are comprehensively considered, wherein self-discharge of the battery is caused by first time of running and downtime of the battery; error correcting is carried out on the SOC of the battery; and the accuracy is improved.

A collision prevention warning method and device capable of tracking a moving object, the method comprises the following steps. Firstly, capturing a plurality of continuous images in a front region of 180 degrees, through identifying category of at least an obstacle in these continuous images, to find a moving obstacle. Next, detect continuous relative positions of the moving obstacle and vehicle, to estimate a first collision region of the vehicle. Then, based on the continuous relative positions and an Extended Kalman Filter Algorithm, to estimate a second collision region of the moving obstacle. Finally, based on the first collision region and the second collision region to calculate a collision point. When the first collision region and the second collision region at least partially overlap each other, estimate out a collision time, and then output an alarm signal to warn the driver and raise driving safety.

The invention relates to an SLAM (Simultaneous Localization and Mapping) method and device based on visual inertia, a storage medium and equipment, and relates to the technical field of wireless locating. The method comprises the steps of: fusing image information acquired by an image acquisition unit at the first time and motion information acquired by an inertial measurement unit IMU according to an extended Kalman filtering algorithm, so that the initial posture is obtained, determining an initial local map according to the image information and the initial posture, optimizing the initial posture and the initial local map according to a pre-set nonlinear optimization algorithm, so that the optimized posture and the optimized local map are obtained, and updating the target initial posture and the target initial local map according to the optimized posture and the optimized local map. The SLAM calculation efficiency and locating precision can be increased.

The invention discloses an inertial measurement unit based on a gyroscope and geomagnetic sensors. The unit is formed by a three-axis MEMS gyroscope and two film geomagnetic sensors. By using the characteristics that the film geomagnetic sensors do not accumulate errors with flight time, the film geomagnetic sensors and the inertial device, i.e. the three-axis MEMS gyroscope are designed in a combining way. The invention adopts a state estimation method, an attitude determination system takes the high-precision film geomagnetic sensors as attitude measurement references and corrects the drift of the three-axis MEMS gyroscope, and a comparatively accurate extended Kalman filter algorithm is adopted to improve the precision of attitude determination. The invention has the advantages of small volume, light weight and low cost, and can be used in the high-speed rotating missile attitude measurement field.

The invention discloses a pedestrian navigation device based on an inertial sensor. The pedestrian navigation device comprises a microprocessor, a three-axis acceleration sensor, a three-axis gyroscope, a three-axis magnetometer and a wireless communication module, wherein the three-axis acceleration sensor, the three-axis gyroscope, the three-axis magnetometer and the wireless communication module are respectively connected with the microprocessor; the microprocessor is used for receiving data collected by the three-axis acceleration sensor, the three-axis gyroscope and the three-axis magnetometer and uploading the data to a PC (Personal Computer) through the wireless communication module. The invention further discloses a pedestrian navigation method based on the pedestrian navigation device. The pedestrian navigation method comprises a step stage detection algorithm, a foot and body orientation estimation algorithm and an extended Kalman filter algorithm. The complexity of calculation is greatly reduced while the accuracy is guaranteed, and a condition that the requirement of timeliness can be met is guaranteed without consuming a large amount of hardware power consumption in a practical environment.

The invention relates to the aerospace field, and provides a rendezvous docking method and device of a space non-cooperative target. The method comprises the steps of guiding the space non-cooperative target to a system capture range through ground guideness; determining measurement angle of sight, relative distance and azimuthal angle respectively according to the simplest combination of measurement components capable of determining relative parameters of the space non-cooperative target; carrying out relative navigation by using an extended Kalman filter algorithm, according to the capture measurement results; tracking track the target by executing CW reference trajectory guidance and linear reference trajectory guidance respectively to according to navigation results and giving out a relative position and relative speed equation of an ideal trajectory; and carrying out thrust control by adopting a PID control law and a pseudo rate pulse modulator during an approaching process. Through capturing, tracking and approaching of the space non-cooperative target, autonomous capturing, continuous tracking and stable approaching of the space non-cooperative target can be finished after a spacecraft enters a work range of capturing the non-cooperative target, thereby realizing rendezvous docking of the space non-cooperative target.

The invention discloses an adaptive indoor dynamic target UWB positioning method. The method comprises the following steps: firstly measuring a TDOA value by using a UWB positioning system; then, processing the measured TDOA value by using a wavelet analysis adaptive denoising method, outputting a reconstructed TDOA value, converting the reconstructed TDOA value into a distance difference, establishing a nonlinear equation set, and obtaining an optimal solution of the equation set; then using the optimal solution as an initial value, performing tracking and positioning on a dynamic target by using an extended Kalman filtering algorithm, and figuring out a final estimated value; and finally outputting the final estimated value. By virtue of the adaptive indoor dynamic target UWB positioning method provided by the invention, a distance measuring error caused by the influence of multipath propagation (Multipath) and non-line-of-sight interference (NLOS) in a UWB signal propagation process can be weakened and even eliminated, the positioning precision is improved, and the accurate positioning of the dynamic target in a non-line-of-sight indoor environment can be realized. The ultra wide band positioning system verifies the validity of the positioning method and realizes the indoor positioning of the dynamic target.

The invention discloses a combined navigation method of joint entropy extended Kalman filter based on strong tracking. The combined navigation method comprises the following steps of: establishing a state equation of an aircraft navigation system; establishing a kinetic equation of an SINS / GNSS combined navigation system; establishing a measurement equation of the SINS / GNSS combined navigation system; dispersing the kinetic equation and measurement equation of the SINS / GNSS combined navigation system; linearizing a nonlinear dispersion kinetic equation; by adopting a joint entropy extended Kalman filter algorithm based on strong tracking, outputting information of SINS / GNSS combined navigation according to the linearized kinetic equation. As fading factors in the strong tracking theory are introduced into the joint entropy extended Kalman filter algorithm, model uncertainty of a non-linear SINS / GNSS combined navigation system and non-gaussian problem of noise statistical characteristics are effectively solved, the navigation precision can be improved, and the stability of the navigation process can be improved.

The invention provides an estimation method and device for the longitudinal vehicle speed of a vehicle. The estimation method for the longitudinal vehicle speed of the vehicle includes the steps that the wheel linear speed vwmi with measurement noise and the vehicle speed longitudinal acceleration axm with measurement noise of the vehicle are obtained; a first extended Kalman filter is used for conducting filtering and noise reduction processing on the wheel linear speed vwmi with measurement noise and the vehicle speed longitudinal acceleration axm with measurement noise, and the noise-reduced minimum or maximum wheel speed value vwe, the noise-reduced tire line acceleration value awe and the noise-reduced vehicle body acceleration value axe are generated; estimation parameters of a second extended Kalman filter are dynamically adjusted in real time through a fuzzy controller, and the second extended Kalman filter is used for outputting the vehicle longitudinal speed value ve at the current moment according to the noise-reduced minimum or maximum wheel speed value vwe and the noise-reduced vehicle body acceleration value axe. By the adoption of the method and device, the vehicle longitudinal speed can be estimated on the basis of an extended Kalman filtering algorithm of vehicle wheel speed signals and vehicle body acceleration signals.

The invention provides an online SOC estimation method for a lithium battery. The online SOC estimation method comprises the following steps: step (1) establishing an SOC model of a battery by using an improved ampere-hour integration method; step (2) establishing a second-order RC equivalent circuit model of the battery and establishing an equivalent discrete state space model of a battery system; step (3) obtaining a functional relation between an open-circuit voltage of the battery and the SOC of the battery through experiments to obtain an initial value of the SOC; step (4) finally, estimating the SOC of the battery by using an extended Kalman filter algorithm. The online SOC estimation method provided by the invention compensates the capacity of the battery by considering influences of temperature, discharge current and cycle number in the process of generating a SOC equation; the selected second-order RC equivalent circuit model has relatively high precision and is easier to implement in engineering; the initial value is periodically calibrated to reduce an error, thereby making the precision of the estimated value higher while increasing a convergence speed of the extended Kalman filter algorithm.

The present invention relates to a two-wheel self-balancing robot attitude calculation method based on the improved Extended Kalman Filter algorithm. In the prior art, the existing two-wheel self-balancing robot attitude calculation method can not well meet accuracy, real-time property, simplicity and other requirements. Baed on the problems of the existing two-wheel self-balancing robot attitude calculation method, the method of the present invention utilizes the improved Extended Kalman Filter algorithm so as to effectively combine the inertial sensor attitude measurement data, compensate the gyroscope random drift error and reduce the influence of the displacement acceleration of the two-wheel self-balancing robot on the attitude calculation during moving. The two-wheel self-balancing robot attitude calculation method of the present invention can further be simultaneously applied for the two-wheel self-balancing electric vehicle. The results of static experiments, simulated platform experiments and practical dynamic experiments of the two-wheel self-balancing robot verify that the attitude calculation accuracy of the two-wheel self-balancing robot can be increased with the method of the present invention.

A method for determining or compensating for the positional errors of a sensor tracking a target comprises the steps of operating the sensor to generate sensed information relating to the target and adding any sensor positional bias update information to produce updated sensed information. The target state is propagated to produce time updated state estimates. The Jacobian of the state dynamics and the state transition matrix for the extended Kalman filter algorithm are computed. The covariance of a state vector is time propagated using the state transition matrix.

The invention discloses a quaternion-based attitude resolving method for an extended Kalman filter algorithm. The method comprises the following steps: acquiring multisensor data of a vector in a fixed coordinate system; filtering data acquired by an accelerometer and a magnetometer, and subjecting the data acquired by the two sensors to normalization processing; constructing a state equation of avector system according to a quaternion differential equation and an attitude matrix, and determining a process noise variance matrix of the system; constructing a systematic observation model by using a rapid Gauss-Newton method, and determining a system measured noise variance matrix; constructing a Kalman filtering recurrence equation according to the constructed system state equation and theobservation model; resolving three attitude angles of the vector by using an optimal quaternion obtained through recurrence. According to the method, the computing capacity can be greatly simplified,and the existing problem that parameters are unclear in computing is solved.

The invention discloses a method for estimating the SOC (State of Charge) of a lithium ion battery based on a gray extended Kalman filtering algorithm. The method comprises the steps of firstly predicting prior estimated values of polarization voltage and an SOC state variable of a battery model at the present moment through a gray prediction model and replacing a Jacobian matrix in the extended Kalman filtering algorithm, and then updating and correcting the prior estimated values through observed values by using the extended Kalman filtering algorithm so as to acquire an SOC estimated value of the lithium ion battery at the present moment. The invention provides a lithium ion battery SOC estimation method for an electric automobile battery management system, and the SOC estimation precision of the lithium ion battery can be improved.

The invention discloses a high-capacity battery system charge state estimation method based on unscented Kalman filter. A high-capacity battery system is an M*N battery system, wherein M individual batteries are connected in series to form a battery string, and N battery strings are connected in parallel to form the high-capacity battery system. The method comprises the steps that a high-capacity battery system equivalent circuit model based on a battery charge state is established; the battery charge state meaning is combined, and a battery system space state equation is established; unscented Kalman filter is used to carry out charge state estimation on the battery system; the output voltage of the battery system and a voltage estimation value are detected online to update an unscented Kalman filter gain matrix; and recurrence is carried out to acquire a new battery charge state estimation value. According to the invention, a high-capacity battery system charge state estimation algorithm is more accurate and robust than an extended Kalman filter algorithm, and the method is applicable to the battery system and individual batteries.

The invention relates to a parameter estimation OCV (Open Circuit Voltage) based full temperature SOC (State of Charge) estimation method and belongs to the technical field of battery management. Themethod includes the following steps: S1, selecting a power battery to be tested and a required equivalent circuit model, and determining a system state and model parameters that need to be identifiedon-line; S2, constructing an experiment and carrying out a variable power cycle experiment to the power battery, and then recording experiment data; S3, identifying a battery state and the model parameters online on the basis of the recorded operating condition experiment data and an adaptive joint extended Kalman filter algorithm; and S4, establishing an OCV-SOC-T model based on parameter estimation, and realizing SOC accurate estimation of the power battery in the full temperature range on the basis of the model and experiment operating conditions. The model adopted by the invention is simple, the algorithm is not complicated, highly robust on-line estimation of the system state of the battery and the model parameters can be realized, and thus parameter estimation OCV based full temperature SOC accurate estimation is realized.

The invention provides a power battery health state estimation method. A lithium ion battery second-order Thevenin equivalent circuit model is adopted, and an adaptive unscented Kalman filter (AUKF) algorithm is applied to carry out real-time estimation on a state of a battery. The adaptive unscented Kalman filter algorithm is combined with an unscented Kalman filter algorithm and an extended Kalman filter algorithm, a loop iteration relation is established, the battery state is estimated according to known battery parameters, then the battery state serves as a known quantity to estimate modelparameters, recursive operation is performed in the same manner, and SOC and an ohmic internal resistance of the battery are estimated in real time. And battery SOH can be estimated in real time by using a function corresponding relationship between the ohmic internal resistance and the battery SOH.

The invention discloses a bearing fault diagnosis and prediction method based on an extended Kalman filtering algorithm, and the method comprises the following steps: 1) employing a full service life cycle vibration signal of a bearing; 2) constructing an AR model through the vibration signal, carrying out the filtering analysis of the vibration signal, and highlighting a signal correlated with a fault; 3) extracting energy information correlated with a wavelet packet coefficient through employing wavelet packet transformation, and constructing a feature character; 4) carrying out the calculation of a mahalanobis distance, constructing health indexes based on the mahalanobis distance, converting the non-negative and non-Gaussian distribution health indexes into Gaussian distribution data through Box-Cox transformation, and determining a related abnormal threshold range through the features of Gaussian distribution and the inverted Box-Cox transformation; 5) carrying out fitting analysis of health index data in a loss period, constructing a degeneration model and a status space model, updating model parameters through employing current data and the extended Kalman filtering algorithm, and predicting the remaining service life of the bearing. The method is higher in prediction precision, and is shorter in consumed time.

The invention provides a moving-object-traceable anti-collision warning method and a device thereof. The device is arranged on a vehicle, a plurality of continuous images in 180 degrees of a front area are captured, at least one kind of obstacles among the continuous images is recognized through the images, and dynamic obstacles are found out; continuous relative positions of the dynamic obstacles and the vehicle are detected, and a first collision area of the vehicle is estimated; a second collision area of the dynamic obstacles is estimated according to the continuous relative positions and an extended kalman filtering algorithm; finally, a first collision point is estimated according to the first collision area and the second collision area, when the first collision area and the second collision area are at least partially overlapped, collision time is estimated, and a warning signal is output to warn a driver so as to improve driving safety.

The invention relates to the field of underwater robotics. Based on distance collected every two moments between an underwater robot and an acoustic beacon and sailing distance of the underwater robot during the period, a computer in the underwater robot calculates a steering angle of the underwater robot and controls the underwater robot to sail toward the acoustic beacon. When the distance between the underwater robot and the acoustic beacon decreases to a set value, the underwater robot performs circular motion round the acoustic beacon. Based on a course and speed of the underwater robot and the distance collected between the underwater robot and the acoustic beacon, an improved extended Kalman filter algorithm is used for estimating position of the underwater robot online. According to the invention, the device requires only one acoustic beacon and a range finder, without the need of other auxiliary devices; can be easily transferred to various underwater robots with the range finder easy to install and the correction algorithm program of good transferability; and has the advantages of good stability and reliability, accurate correction, simple installation, long service life and wide application.

The invention discloses an adaptive optimization method for estimating SOC of a battery based on a Kalman filtering framework. The adaptive optimization method is characterized in that a second-orderRC equivalent circuit is used as a battery model; the second-order RC equivalent circuit parameters are identified by using the battery pulse experiment data and the MATLAB parameter identification tool box; and then a state equation and an observation equation of the battery are constructed according to the Kirchhoff voltage law, and an adaptive optimization strategy is added into an extended Kalman filtering algorithm on the basis of an estimated difference value of an observed quantity and the observation equation, and the optimized extended Kalman filtering algorithm is applied to SOC estimation of the battery. The result shows that compared with the traditional extended Kalman filter algorithm for estimating the SOC of the battery, the adaptive optimization method provided by the invention has the advantages that the precision is improved by 0.3%, and the fluctuation is smaller, and the accuracy and the practicability are very good.

The invention discloses a real-time estimation method for road slope under comprehensive driving conditions. The real-time estimation method for the road slope under the comprehensive driving conditions comprises the following steps that a data collection platform based on OpenXC is set up to obtain driving state data of vehicles at first; then a model of the relationship between the longitudinal power of the vehicles and the road slope is established; then an adaptive extended Kalman filter algorithm model is constructed; and finally, a short-range slope estimation method based on autoregressive prediction model is established according to the brake braking condition, and the slope estimation under the comprehensive driving conditions is calculated. The real-time estimation method for the road slope under the comprehensive driving conditions expands the scope of application while improving the slope estimation according to different driving conditions and uncertain noise interference in the actual driving environment; and the method further provides drivers with real-time road slop information, which is of great practical significance for improving vehicle auxiliary driving control, stability control, and realizing safe and energy-saving driving.

The invention relates to a fault diagnosis method of a lithium ion battery sensor of an electric vehicle based on an observer, and belongs to the technical field of battery management. The method comprises the following steps: determining parameters of a lithium ion battery, and establishing an electrothermal coupling dynamic model of the lithium ion battery of the electric vehicle; performing anopen circuit voltage test and an HPPC experiment on the measured battery to obtain characteristic parameters of the battery under different ambient temperatures; establishing a relationship between battery OCV and SoC, and identifying the parameters in the electrothermal coupling dynamic model by using a recursive least square method with a forgetting factor to obtain a quantitative relationship among the battery parameters, the ambient temperatures and the battery SoC; and importing current, a voltage and a temperature sensor measured value into a fault diagnosis and separation algorithm of the lithium ion battery based on the observer, estimating a quantity of state through an extended Kalman filter algorithm to generate a residual, and performing residual evaluation by using a CUSUM test method, and finally achieving fault diagnosis and separation of the lithium ion battery sensor according to combined response conditions of different residuals.

The embodiment of the invention discloses a control system used for an autonomous mobile robot platform. The control system comprises a robot platform and a server. The server comprises a human-computer interaction module, a data processing module and a route planning module. The embodiment of the invention also discloses a control method and device used for the autonomous mobile robot platform. With application of the control system used for the autonomous mobile robot platform and the control method and device thereof, omnidirectional movement of the robot platform can be realized by using a structure of multiple synchronous wheels so as to have flexible and high-precision plane movement capacity; and a speedometer is corrected by laser radar and an inertial navigation system by using an extended Kalman filtering algorithm, two sets of corrected positions are weighted and fused to act as spatial locating of the robot platform, and error caused by long time of operation of the robot platform is corrected by the contour characteristics of the environment so that the locating precision and the movement precision of the robot platform in long time of movement can be guaranteed, and multiple robot platforms can simultaneously operate according to the requirements to cooperatively complete the task.

The present invention discloses an estimation method of state of charge of a battery system on the basis of Unscented Kalman Filter. The battery system is a M*N type battery system, namely, M battery monomers are connected in series to be a battery string and then N battery strings are connected in parallel to be the battery system. The estimation method provided by the invention comprises the following steps: establishing a battery system equivalent circuit model based on the battery state of charge; building a battery system spatial state equation through combination of the battery state of charge implication; estimating the state of charge of the battery system through adoption of the Unscented Kalman Filter; and updating the gain matrix of the unscented kalman filter according to the output voltage of an on-line detection battery system and a estimated voltage value, and obtaining an new estimated value of battery state of charge through the cycling recursion like this. Compared with the Extended Kalman Filter algorithm, the estimation method of state of charge of a battery system on the basis of Unscented Kalman Filter is more accurate, has better robustness, is applicable to a battery system, and is also applicable to a battery monomer.

The invention discloses a method for estimating the state of charge (SOC) of a lithium battery by an extended Kalman filter algorithm. The method comprises the following steps that S1, an equivalent circuit model of the lithium battery is established; S2, a current source E is replaced with the voltage of circuit (VOC) of the lithium battery, and a first state equation and a first measurement equation of a system are established according to the equivalent circuit model of the lithium battery; S3, the first state equation and the first measurement equation are analogous to an EKF algorithm toobtain a second state equation and a second measurement equation correspondingly; S4, the SOC of the lithium battery is estimated by the EKF algorithm. The method has the advantages that the EKF algorithm does not depend on the setting of an SOC initial value, the filter process can be convergent in a short time even through the set initial value has large difference with a true value, and accurate SOC estimation is achieved.