239 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 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 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 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 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.

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 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 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 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.